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All of arXiv

Wednesday 7 October

Audio and Speech Processing

A Lightweight Hybrid Framework for Acoustic Echo Cancellation and Suppression via Constrained Recursive Training

Huawei Zhang, Rilin Chen, Hao Zhang et al. (+2 more)

We propose a hybrid framework that combines deep learning with adaptive filtering for acoustic echo cancellation (AEC) and suppression. It integrates a multi-output neural network to jointly update filter parameters and perform post-processing. A learnable variant of the normalized least mean squares (NLMS) filter is introduced, incorporating a variable step size and a variable transition factor that are recursively updated for dynamic adaptation. In addition, an enhanced near-end speech estimate is generated from the filter output to suppress the residual echoes. To ensure training stability, we propose a constrained recursive training strategy that aligns with the adaptive nature of the filter, where an upper-bound constraint is introduced on the filter output. For efficiency, the framework adopts a lightweight implementation with only 253 k parameters. Experimental results show that the framework outperforms the baselines in terms of both signal quality and speech intelligibility.
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All authors Huawei Zhang, Rilin Chen, Hao Zhang, Meng Yu, Dong Yu

Machine Learning

DSTNet: Dynamic Spectral Trajectory Network for Causal Multi-Horizon Financial Forecasting

Aashish Bohra and Lokendra Vishwakarm

Wavelet-based financial forecasters typically use the transform only to denoise, or reduce it to a single spectral snapshot at the forecast origin, and the convolution that produces the coefficients is usually bilateral, so it can read past the forecast origin. DSTNet instead retains the recent evolution of filter-bank magnitudes as a causal Dynamic Spectral Trajectory, built from seven trailing technical indicators over a twenty-day lookback with a one-sided Morlet-derived filter bank and an explicit burn-in for the left-boundary transient. A factorized Scale-Temporal Spectral Transformer attends along the time and filter-bank axes separately, a learned gate fuses the spectral branch with a CNN-BiLSTM, and horizon-specific gates emit one, three, five, and ten day forecasts in a single pass. We evaluate seven equity indices and gold under a common expanding-window protocol and an untouched one-year hold-out, against nine learned baselines and a random-walk persistence benchmark. Under MAE and MAPE, persistence is the strongest of the ten fixed competitors in 29 of the 32 series-horizon cells and DSTNet is the only model below it in every cell, by 0.7 to 0.9 percent at one day and 3.4 to 4.5 percent at ten days. At one day, paired testing favours DSTNet against the weaker learned baselines but is inconclusive against persistence and the strongest learned forecasters. A downstream allocation diagnostic does not support an equity-timing advantage on any of the seven indices.
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Soft Condensed Matter

Resolving the Effects of the Mixing Process on Hierarchical Structures in Polymer Nanocomposites by Bayesian Ultra-Small-Angle X-ray Scattering Analysis

Yui Hayashi, Kazuki Mita, Shigeo Kuwamoto et al. (+1 more)

Bayesian inference was combined with Unified Fit to resolve the effects of the mixing history on polymer nanocomposites whose ultra-small-angle X-ray scattering profiles are nearly indistinguishable by conventional analysis. Bayesian Unified Fit was applied to comparing the aggregate size, internal mass-fractal structure, and surface roughness for three carbon black–filled ethylene propylene diene copolymer rubber samples prepared by different mixing processes. Whereas conventional Unified Fit reported essentially identical carbon-black dispersions, the radius of gyration, mass fractal dimension, and surface fractal dimension for the three samples all followed the same ordering, which coincides with the reported ordering of the tensile strength and elongation at break and suggests that the inferred aggregate hierarchy is physically meaningful for the macroscopic mechanical response. These results identify the aggregate size as a candidate structural descriptor of rubber reinforcement and provide an uncertainty-aware basis for examining processing–structure–property relationships in polymer nanocomposites.
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All authors Yui Hayashi, Kazuki Mita, Shigeo Kuwamoto, Masato Okada

Artificial Intelligence

MeshSIPP: Efficient Lattice Planning in Dynamic Environment

Marat Agranovskiy and Konstantin Yakovlev

Autonomous navigation in dynamic environments requires computing spatiotemporal trajectories that satisfy non-holonomic motion constraints. When the trajectories of the moving obstacles are predictable or known, a promising approach is to rely on the combination of state lattices constructed from precomputed feasible motion primitives and Safe Interval Path Planning – a search-based algorithm with strong theoretical guarantees. While this approach yields feasible paths, the rich primitive sets needed for smooth navigation induce a large branching factor, which becomes costly when coupled with time-dependent obstacle intervals. To this end, we present MeshSIPP, an efficient planner that removes the computational bottleneck by exploiting the fact that many primitives sweep the same regions and can therefore be validated together. MeshSIPP propagates primitives as spatial bundles, screens them with lightweight bounding-interval checks, and defers the expensive exact departure-time search until a primitive reaches its terminal state. A time-aware pruning rule additionally discards redundant space-time branches early in the search. We prove that the resulting search is complete and optimal. Extensive experiments over more than 6,000 benchmark instances and real-time ROS~2 simulations show that MeshSIPP achieves up to a 3$\times$ speedup over state-of-the-art spatiotemporal planners.
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Machine Learning

Closed-Form Noise Calibration Against Membership Inference for Random-Allocation DP-SGD

Murat Bilgehan Ertan and Marten van Dijk

DP-SGD protects training data by adding Gaussian noise to clipped gradients. The amount of noise is usually chosen by running a numerical privacy accountant inside a search. We study DP-SGD with random allocation, where each epoch uses every record once, at a randomly chosen step. For this setting we give a one-line formula that bounds the accuracy of every membership inference attack (MIA) on the trained model. With $M$ steps per epoch, $E$ epochs and noise multiplier $\sigma$, and with membership and non-membership equally likely a priori, the attack accuracy is at most $\frac12+\frac14\sqrt{(1+(e^{1/\sigma^2}-1)/M)^E-1}$. The formula comes from the chi-square divergence between a Gaussian distribution and a Gaussian mixture that dominates random allocation. It is interpretable and gives $\sigma$ in about a microsecond. Where applicable, our formula needs at most about half the noise of the state-of-the-art closed-form bound. To measure how close the bound is, we also derive an exact expression for the attack accuracy of these two distributions and evaluate it numerically. Calibrating to this exact expression requires $13.0\%$ to $20.2\%$ less noise than the formula in our main experiments, and since it is exact, no accountant that knows only $M$, $E$ and $\sigma$ can certify a smaller $\sigma$. In training, the resulting $\sigma$ outperforms the formula and matches a published accountant in test accuracy. It is found in seconds and certified in minutes, whereas every search we ran with that accountant took longer or returned at least $0.62\%$ more noise. We show that MIAs on the trained models stay below the bound.
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Combinatorics

The Almost Stacked Hypothesis: A Conjectural Analogue of Sjöstrand's Cover Pebbling Theorem

Tamás Csernák and Lajos Soukup

We study two graph pebbling parameters, the stacking number and the clearing number, through the Almost Stacked Hypothesis (ASH). This hypothesis asserts that these thresholds can be determined by testing only configurations in which at most one vertex carries more than one pebble. We prove that every almost stacked configuration of size $2^{n+1}-1$ on $C_{2n}$ is stackable and that every almost stacked configuration of size $3\cdot 2^n-2$ on $C_{2n+1}$ is clearable. Together with the known lower bounds, these results show that ASH implies $\operatorname{stack}(C_{2n})=2^{n+1}-1$ and $\operatorname{clear}(C_{2n+1})=3\cdot 2^n-2$. For a finite tree $T$, we introduce an explicit invariant $\operatorname{estim}(T)$. We prove unconditionally that $\operatorname{stack}(T)\geq\operatorname{estim}(T)$ and prove the reverse inequality under ASH. Consequently, ASH yields $\operatorname{stack}(T)=\operatorname{estim}(T)$, and we conjecture that this equality holds unconditionally. Finally, we study perfectly pebblable graphs: finite connected non-bipartite graphs whose clearing number has the minimum possible value $\operatorname{clear}(G)=|V(G)|+1$. Every complete graph with at least three vertices is perfectly pebblable, which might suggest that perfect pebblability requires high edge density. Assuming ASH, however, we show that this is not the case. We give a sufficient criterion involving strong edge-triangulation and Hamiltonian-path and path-cover conditions in vertex-deleted subgraphs and use it to construct two explicit infinite families of perfectly pebblable graphs with edge density tending to zero, one of which has only a linear number of edges.
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Combinatorics

Melnikov's Valency Variety Problem

Zhanping Yang and Qinghou Zeng

Let $w(G)$ denote the number of distinct vertex degrees of a finite simple graph $G$. Melnikov asked for a lower bound on the chromatic number $\chi(G)$ in terms of $|V(G)|$ and $w(G)$, and conjectured a strict bound of this type. We resolve Melnikov's valency-variety problem by proving that every graph $G$ with at least two vertices satisfies \begin{equation*} \chi(G)\ge\left\lceil1+\frac{2w(G)(w(G)-1)}{4w(G)(|V(G)|-w(G))+(|V(G)|-w(G)-1)^2}\right\rceil, \end{equation*} and consequently \begin{equation*} \chi(G)\ge\left\lceil\frac{\lfloor w(G)/2\rfloor}{|V(G)|-w(G)}\right\rceil. \end{equation*} Finally, we also construct an explicit infinite family of graphs attaining equality in both bounds. In particular, these examples show that Melnikov's proposed strict inequality is false and that the bounds above are best possible.
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Combinatorics

An asymptotically tight bound on oriented diameter

Jiangdong Ai, Yaojun Chen, Yaokun Feng et al. (+5 more)

Let $f(d)$ be the smallest integer such that every finite connected bridgeless simple graph of diameter $d$ admits a strong orientation of diameter at most $f(d)$. Chvátal and Thomassen (1978) proved $\lceil d^2/2\rceil+d\le f(d)\le2d^2+2d$. In this paper, we prove that $\lceil d^2/2\rceil+d\le f(d)\le\lceil d^2/2\rceil+d+18$ for every $d\ge2$, which shows that $f(d)=\lceil d^2/2\rceil+d+O(1)$ and determines both the quadratic and linear terms up to a bounded additive error. The key method of our proof is to construct a central subgraph $H$ that admits a strong orientation of diameter $O(d)$ and is within distance $\lfloor d/2\rfloor$ of every vertex outside it. We obtain the sharp linear coefficient by jointly estimating outside paths and their connecting paths in $H$ at the actual attachment vertices, rebuilding $H$ when necessary.
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All authors Jiangdong Ai, Yaojun Chen, Yaokun Feng, Hui Lei, Jifu Lin, Zijian Ren, Xiaolin Wang, Ruilin Zheng

Machine Learning

When Rank Rises as LLMs Degrade

Zhaohui Geoffrey Wang

Post-training adapts language models in non-stationary environments. Practitioners monitor representation health with RankMe and related spectral statistics, often assuming that rank falls when representations degrade. We show that this assumption is unsafe for LLM post-training. In a controlled study of Qwen3-0.6B with four degradation modes and three seeds, data duplication worsens held-out loss by 75% relative to healthy while increasing both original and centred RankMe; the latter changes by 13.5 pooled standard deviations. Covariance effective rank rises to nearly twice its healthy value. This failure is spectral dispersion rather than collapse, so a one-sided monitor rates the worst checkpoint as the healthiest. By contrast, a learning-rate misconfiguration lowers centred RankMe and k95, while uncentred RankMe is inconsistent across seeds. Direction is therefore a property of the regime-statistic pair and cannot be fixed by recalibration alone. We also distinguish two often-conflated statistics: RankMe normalises singular values, whereas covariance effective rank normalises eigenvalues. On raw intermediate-layer states in the pretrained model, massive activations pin the latter near 1 out of dimension d while RankMe retains usable range. We then test a two-sided, multichannel sequential monitor with separate calibration and test data. In a pre-registered shared-prefix, leave-one-seed-out evaluation, it detects all three damage regimes in every fold 10 to 60 steps after the fork and separates dispersion from downward-rank damage by firing direction. However, it never precedes held-out probe loss, and calibration with two seeds produces false alarms on the held-out healthy seed. Spectral monitoring can diagnose failure regimes, but it does not warn earlier than held-out loss, and validity claims require held-out healthy data.
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Numerical Analysis

A locking-free virtual element method for the three-dimensional quasi-static electroporoelasticity equations

Jianguo Huang and Wenxuan Wang

We develop and analyze a locking-free virtual element method for the three-dimensional quasi-static electroporoelasticity equations. The pressure, electric, magnetic, and displacement fields are discretized by the lowest-order nodal, edge, face, and Stokes-like virtual element spaces, respectively, so that the scheme works on general polyhedral meshes, while the temporal discretization is carried out by a backward Euler scheme. The locking phenomenon is overcome by defining the Stokes-like space on a refined mesh $\widetilde{\mathcal{T}}_h$, on which we can construct an interpolation operator $\mathrm{I}_s$ satisfying $\div(\mathrm{I}_s\bm v)|_K=\Pi^K_0\div\bm v$, which renders the error estimates independent of the Lamé constant $\lambda$. Moreover, since the exact sequence $\curl\bm V^e_h=\bm V^f_{h,0}$ holds, the magnetic field is updated explicitly, reducing the four-field system to a three-field one and lowering the computational cost. Numerical experiments are provided to demonstrate the performance of the proposed method.
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Robotics

Design optimization of tendon-driven robots considering tendon wrapping and shortcut

Yuta Sahara, Kento Kawaharazuka and Kei Okada

This study proposes a multi-objective optimization method for tendon-driven arm design, addressing the inherent trade-off between joint torque and arm thickness through tendon wrapping and shortcut effects. We formulate the problem to simultaneously maximize torque and minimize thickness, solved using NSGA-II. The algorithm optimizes tendon routing, pulley configurations, and attachment points. Results reveal Pareto-optimal designs with effective moment arm expansion via strategic shortcuts, providing practical guidelines for compact high-torque arms. The optimized configurations demonstrate non-trivial patterns beyond conventional design intuition, offering new insights for engineering efficient robotic systems.
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Artificial Intelligence

Automatically Building and Updating a Knowledge Graph of MLIP Models

Alexis Beer, Liudmyla Klochko and Mathieu d'Aquin

Complementing the many efforts in providing semantic representations of concepts, notions, and entities in materials science, we report and illustrate a process by which we can automatically build a knowledge graph of the fast evolving field of machine learning applied to the prediction of material properties, focusing on MLIP (Machine Learning Interatomic Potential). This LLM-based process relies on multiple steps, from information extraction in documents and articles to a validation loop using SHACL constraints to detect and correct errors. It is carried out on a model-by-model basis, focusing on the consistency of representation, therefore enabling an iterative construction where the addition of new models is facilitated. We illustrate the process by showing a few interesting aspects that can be queried from a knowledge graph built from the models listed in the Matbench Discovery leaderboard.
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Quantitative Methods

CircuitATLAS: Agentic reasoning over a systems neuroscience knowledge graph for target discovery in circuitopathies

Gabriel Ocana-Santero and Marko Tvrdic

Drug discovery for neurological disease has traditionally centered on the molecules altered by disease. But the molecules that cause pathology are not necessarily the best points from which to reverse it. Here, we ask which otherwise unaltered molecular control points can be engaged to restore pathological neural circuits toward functional states. We present CircuitATLAS, a provenance-grounded systems-neuroscience knowledge graph and agentic framework for target discovery in circuitopathies. It structures literature-derived relationships across diseases, phenotypes, electrophysiology, circuits, brain regions, cell types and molecular effectors, while deliberately excluding direct disease-gene and disease-protein edges to reduce shortcut reasoning. The graph contains 3.83 million nodes and 7.66 million edges, including 5.31 million LLM-extracted relations, and incorporates structured datasets such as the Human Cell Atlas and new multimodal in vivo measurements. We then introduce an agentic workflow that reasons from measurable disease phenotypes through their circuit and cellular substrates to molecular interventions, therapeutic feasibility and clinical constraints. Finally, we introduce a human-governed in vivo lab-in-the-loop linking hypothesis generation to experimental iteration. Within this framework an agent nominated ATP1A3, the neuronal alpha3 Na+/K+-ATPase, as a control point on cortical excitability; interneuron-restricted expression of ATP1A3 abolished the beta- and gamma-band response to a focal 4-aminopyridine challenge in vivo, and the validated target was then carried into a structure-guided small-molecule campaign terminating in a defined assay to resolve the direction of modulation. CircuitATLAS thus provides a framework for discovering therapeutics based not only on what is molecularly disrupted in disease, but on what can be controlled to restore circuit function.
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Databases

LDPGraph: Locally Differentially Private Graph Synthesis by Exploiting Neighborhood Structure

Jiawei Dong, Zhikun Zhang, Quan Yuan et al. (+2 more)

The widespread application of graph data inevitably brings significant privacy risks, as its unprotected use can lead to the leakage of sensitive information. These risks are particularly acute in the setting with an untrusted curator, where the data remains decentralized and each user only holds the connections to their neighbors. To mitigate such privacy risks, we adopt local differential privacy (LDP) to collect users' private information and generate a synthetic graph. However, existing methods suffer from either excessive noise injection by perturbing the local adjacency lists or significant structural information loss due to the simplistic graph encoding process. To address these issues, we propose LDPGraph, an effective graph synthesis algorithm that takes one step further by exploiting neighborhood structures under LDP. To obtain neighborhood statistics beyond degrees, LDPGraph aggregates a noisy global view from perturbed adjacency lists and combines it with projected local connections to estimate node-level triangle counts. To correct the structural inconsistency caused by separately perturbing degree and triangle count, LDPGraph jointly refines them into feasible degree-triangle targets. To reconstruct a global graph from these estimated targets, LDPGraph adopts a triangle-first strategy that first preserves local clustering structures and then fulfills remaining degree requirements. Extensive experiments on four real-world datasets and multiple commonly used graph metrics validate the superiority of LDPGraph. Source code is available at https://github.com/ZJU-TrustAID/LDPGraph.
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All authors Jiawei Dong, Zhikun Zhang, Quan Yuan, Zhe Liu, Yunjun Gao

Quantum Physics

Q-PhotoMarket: A Design Space Exploration Framework for Photonic Hybrid Quantum Neural Networks in Financial Market Prediction

Alberto Marchisio, Hanzalah Mohamed Siraj, Muhammad Kashif et al. (+2 more)

Photonic quantum computing has recently emerged as a promising platform for hybrid quantum machine learning due to its native realization of linear-optical circuits and the computational complexity of boson sampling. However, despite growing interest in quantum methods for finance, the influence of photonic circuit design choices on predictive performance remains largely unexplored. Existing studies typically evaluate a single architecture, leaving the broader photonic design space unexamined. In this work, we present Q-PhotoMarket, a systematic design space exploration (DSE) framework for photonic hybrid quantum neural networks (HQNNs) applied to financial market prediction. We explore over 5,000 valid photonic configurations spanning input photon states, circuit architectures, entangling models, and measurement strategies across their compatible computation spaces, for U.S., Indian, and cryptocurrency markets. To improve search efficiency, the exhaustive exploration is complemented with Bayesian optimization. We further incorporate threshold calibration and prediction-collapse diagnostics to enable reliable evaluation under increasingly imbalanced return thresholds. Experimental results show that systematic exploration of more than 5,000 photonic HQNN configurations reveals consistent architectural patterns across financial markets, identifies robust high-performing designs, and demonstrates competitive performance relative to classical machine learning baselines.
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All authors Alberto Marchisio, Hanzalah Mohamed Siraj, Muhammad Kashif, Nouhaila Innan, Muhammad Shafique

Probability

Quantitative stability of optimal transport via regularization

Songbo Wang and Xiaozhen Wang

We prove quantitative stability estimates for quadratic optimal transport maps by regularizing a Brenier potential and estimating the error in the duality gap. The source has an $L^p$ density, $p>1$, relative to a probability measure with a BV density. When one target lies in a fixed ball, we obtain the $W_2$ Hölder exponent $(p-1)/(4p-2)$. The constant depends on the dimension through three explicit source quantities: the variance, the total variation of the reference density and the $L^p$ norm of the weight. The proof uses a second-order bound on the averaged regularized Fenchel gap. By restricting the slopes of the potential, we also treat targets with a bounded moment of any order greater than two. In both estimates, the condition is imposed on one target, while the other can be any probability measure with finite second moment. For Gaussian and isotropic log-concave sources, the constants grow polynomially with the dimension. For $L^p$ changes of Gaussian measure, the factor in the bounded-target estimate is $d^{(p-1)/(4p-2)}$. For each $p$, a fixed source in two dimensions proves optimality of both stability exponents in this source class. A Gaussian construction provides polynomial lower bounds on the dimension dependence.
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Computation and Language

On-Policy Distillation Teaches New Skills but Not New Knowledge

Yixuan Tang and Yi Yang

On-policy distillation (OPD) strengthens language-model reasoning, yet whether students acquire new factual knowledge or compositional skill for multi-step reasoning remains unknown. We separate these capabilities using a controlled synthetic framework that measures the student's initial capabilities and independently controls the teacher's additional facts, compositional skill, or both. Across four models from three families, reverse-KL OPD reliably transfers compositional skill across unseen reasoning structures, but transfers minimal factual knowledge. Decoupling the distillation recipe reveals the source of this asymmetry: replacing reverse KL with forward KL restores factual transfer, whereas student rollouts specifically improve the execution of multi-step reasoning. Experiments on recent factual QA and competition mathematics show a similar asymmetry under reverse-KL OPD, yielding notable reasoning gains without factual memory expansion. Together, these results demonstrate that on-policy distillation does not expand a model's parametric knowledge, but instead teaches it to organize and compose the knowledge it already possesses.
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Functional Analysis

The spectral structure of Toeplitz operators with polynomial symbols on the harmonic Bergman space

Mingmei Huang, Jiawei Wang and Xianfeng Zhao

Using the theory of symmetric polynomials, we prove that the spectrum of every Toeplitz operator with an analytic polynomial symbol on the harmonic Bergman space can be represented as the union of a closed curve and a finite set of at most $(n-1)2^{n-1}$ isolated eigenvalues, where $n$ is the degree of the symbol. This implies that Weyl's theorem holds for such operators.
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Sound

Tracing Inputs, Verifying Outputs: Validating Attribution in Music Generation

Taejun Kim, Wonil Kim, Jongmin Jung et al. (+10 more)

How can we verify whose music contributed to an AI-generated output? This paper demonstrates how input-based attribution can provide verifiable evidence of which audio sources were used in a generation and whether they shaped the output. To do so, we condition the generation solely on audio without any text input, then trace the inputs behind each output, and establish their musical effect. In prompt adherence tests and controlled input swaps, the stems generated by our generator, MixAudio, follow the prompt audio in timbre and the context audio in harmony. Yet these outputs may still reproduce training data not supplied as inputs. We therefore audit memorization with our musical version identification model, musicDNA, and find few reproductions outside the input records. On human-judged cases within the flagged pool, it achieves higher precision and recall than the other tested memorization detectors. The two evaluations suggest that input records and output analysis provide complementary evidence for attribution, on which rights-holder reporting and compensation can draw as the AI music economy takes shape. Audio examples are available at https://neutune.github.io/attr2027demo/
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All authors Taejun Kim, Wonil Kim, Jongmin Jung, Hyeongseok Wi, Sangeun Kum, Keunhyoung Luke Kim, Taehyoung Kim, Dongjoo Moon, Seungsoon Park, Taewan Kim, Virginie Berger, Juhan Nam, Jongpil Lee

Functional Analysis

On the $L_p$ Brunn–Minkowski inequality for planar dual quermassintegrals

Jiangyan Tao

We determine the sharp $L_p$ Brunn–Minkowski range for planar dual quermassintegrals on origin-symmetric convex bodies: $0<q\leq 2$ for $0\leq p<1$, and $0<q\leq4$ for $p=1$. The main new results are the Minkowski inequality for $2<q<4$, proved using a weighted spectral inequality on the projective circle, and parallelogram counterexamples for $0<p<1$ and $q>2$. Together with the known logarithmic and endpoint results, we obtain the complete equality classification: only positive dilates occur, except at $(p,q)=(0,2)$, where parallelograms with corresponding sides parallel also give equality.
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Quantum Physics

Quantum anomaly detection in real scarce data

Emanuele Casciaro, Fabio Mascherpa, Alfonso Amendola et al. (+1 more)

Anomaly detection on small and unbalanced datasets remains very challenging in machine learning, although this scenario is common in several domains, including healthcare, cybersecurity, finance, and energy. Data augmentation and generative AI may mitigate training-data scarcity, but they often fall short because anomalies are, by definition, unpredictable, rare, and highly diverse events compared to high-probability normal data. Overfitting to pseudo-anomalies, model collapse, high-dimensional data, uninterpretable black-box models, and validation challenges are typical issues limiting their practical applicability. In this context, quantum machine learning may provide a promising and more sustainable avenue because it can enable more interpretable models with far fewer trainable parameters and smaller datasets, implementable on energy-efficient quantum hardware. Here, we propose a novel two-step hybrid classical–quantum architecture for sequential data and test it on a realistic scenario in the global energy-transition domain, i.e., automated anomaly detection in large-scale photovoltaic plants. The achieved generalization capability and competitive prediction accuracy may pave the way for new hybrid learning models able to exploit the continuously increasing power of cloud-available and more sustainable quantum accelerators integrated with more traditional energy-hungry High Performance Computing resources.
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All authors Emanuele Casciaro, Fabio Mascherpa, Alfonso Amendola, Filippo Caruso

Systems and Control

Safe Control of Semi-Explicit Differential-Algebraic Systems: Control Barrier Functions with SOS Verification

Mohamad H. Kazma, Hongchao Zhang, Abdallah A. Albustami et al. (+4 more)

Differential-algebraic equation (DAE) systems arise in power networks, multibody mechanics, and chemical processes, where algebraic constraints couple dynamic and algebraic states. Standard control barrier function (CBF) methods, designed for ordinary differential equations, fail for DAE systems because the algebraic constraints impose compatibility requirements that may render safety filters infeasible. This paper develops a safety-critical control framework for semi-explicit DAE systems using CBFs that consider compatibility with the algebraic constraints, termed DAE-aware CBFs. We construct projected vector fields on the constraint manifold to account for the hidden dynamics, while ensuring compatibility conditions that keep the algebraic constraints satisfied. We then present sum of squares (SOS) verification conditions that certify both correctness and feasibility of the resulting DAE-aware CBF. The framework is validated on power system case studies.
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All authors Mohamad H. Kazma, Hongchao Zhang, Abdallah A. Albustami, Ben Wooding, Meiyi Ma, Taylor T. Johnson, Ahmad F. Taha

Machine Learning

Coding-Agent Benchmarks Should Match Their Users' Task Flows

Igor Slinko, Yaroslav Golubev and Sergey Titov

The evaluation of coding agents generally strives to be as realistic as possible. In our study, we collect 4,782 agent sessions of real software engineers in JetBrains IDEs, which we call Production Sessions. Since our subject is interactive agents, we study the sessions with at least three user messages (33% of the sample). These long sessions differ from issue-derived benchmark tasks in two ways: (i) user requests span a far wider mix of task types - questions about the project's code, planning, review, refactoring, execution - and (ii) users switch between types throughout a session. Long-session samples from three public interaction corpora exhibit markedly different Task Flows (the distributions of session lengths, task types, and type-to-type transitions), so no single interaction distribution is universally realistic: benchmarks should name a target use case and calibrate to measurements from it. We present SWE-TaskFlow, an approach for transforming any issue-derived benchmark: it preserves the verified tasks and tests while steering the interaction toward a target Task Flow through prompt splitting and verifiable repository QA, with a TaskFlow Alignment Score (TFAS) for selecting among generated trajectories. In a pilot on 700 SWE-Bench Pro tasks, solving the task sequentially in several steps approximately doubles agent cost without a stable change in resolve rate: the interaction protocol itself is an important dimension of evaluation.
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Networking

Age of Information in Queueing Systems with Merging Server Streams

Lucrezia Rossi, Leonardo Badia and Andrea Munari

We present a novel analytical framework for evaluating the age of information (AoI) in multi-path queueing topologies where output streams from multiple servers converge into a single pipeline. This theoretical scenario finds immediate application in ultra-reliable low-latency networks and edge computing architectures, where multi-path routing and stream merging can be vital strategies for maintaining fresh status updates. Specifically, we investigate two setups: a split-merge network and a duplicate-merge network. The split-merge case has been addressed in prior work, but we show that existing studies are incorrect, and our approach provides instead an exact analytical formulation. Moreover, building on similar reasoning, we provide the first formal analysis of the previously unexplored duplicate-merge scenario. Finally, we show how to leverage our theoretical results to solve key system-level optimization problems, deriving the optimal randomized routing probabilities and traffic injection rates that minimize average AoI.
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Robotics

Fast and Robust Teach-and-Repeat Navigation Using MixVPR Visual Place Recognition*

Václav Truhlařík, Tomáš Pivoňka and Libor Přeučil

Teach-and-repeat navigation systems employing advanced visual place recognition techniques for localization exhibit key attributes for long-term mobile robot navigation, such as the ability to operate in unstructured and dynamic environments. However, existing solutions based on deep-learning techniques are computationally demanding, limiting their applicability. This work introduces a novel and efficient teach-and-repeat system built on the modern visual place recognition method MixVPR. Real-world testing demonstrated its ability to operate both indoors and outdoors, achieving robustness and navigation precision comparable to other state-of-the-art systems. In addition, its lower hardware requirements make it suitable for a wide range of robotic platforms and practical applications.
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Solar and Stellar Astrophysics

A Novel Approach to Identify Unseen Companions: An In-Depth study of GDR3 220_28

T. B. Cysil, Blesson Mathew, B. Shridharan et al. (+6 more)

Emission lines in the spectrum and the excess seen in the spectral energy distribution (SED) at infrared (IR) wavelengths serve as crucial indicators of accretion activity and the presence of gas and dust in the circumstellar environment of a young star. As accretion ceases over several million years, the H$\alpha$ emission diminishes. However, IR excess combined with negligible H$\alpha$ emission could also extend to indicate certain close binary systems, where a circumbinary disk contributes to the observed excess. Additionally, in unresolved binaries, companion stars can contribute to the photometry, leading to inaccuracies in parameter measurements and skewed positions in the color-magnitude diagram (CMD). We identify a photometrically peculiar object from a survey of the Cepheus star-forming regions (SFRs), Gaia DR3 2205212960562531328, exhibiting a shift from its expected position in the CMD, as indicated by the Gaia optical photometry. This peculiarity drives our research, prompting an investigation through an approach that combines astrometric accuracy, photometric modeling and spectroscopic studies. The Gaia XP spectrum of the source shows a systematic blue excess, indicating an additional hotter unresolved component. Two-component SED modelling yields a companion with $T_{\rm eff}\sim27000$ K and $\log g\ge4.5$, consistent with sdB stars. Further CMD and spectroscopic analyses support a binary system consisting of an A0-type PMS star with an sdB companion. In addition, ZTF photometry shows tentative evidence for a periodicity of 0.498 days. This study paves the way for a systematic search of similar binary systems that would otherwise remain unidentified.
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All authors T. B. Cysil, Blesson Mathew, B. Shridharan, S. Muneer, Pramod Kumar S, S. Nidhi, Sreeja S Kartha, Suman Bhattacharyya, T. Robin

Statistics Applications

Predicting Long-Term ADAS-Cog Change From Early Response After Repetitive Transcranial Magnetic Stimulation in Alzheimer Disease

Mohammad Alamgir Chowdhury and Hina Shaheen

Early cognitive response after repetitive transcranial magnetic stimulation (rTMS) may predict later cognitive change in Alzheimer disease, but the incremental value of domain-specific response is unclear. We analyzed data from 112 randomized participants; 101 entered a Week-5 landmark cohort and 91 had complete predictors and outcomes at 8, 16, and 24 weeks post-treatment. Nested predictor-set comparisons used ridge regression to evaluate baseline information, trial variables, early total-score response, and domain-specific response. Model-class comparisons included ridge, multi-task elastic net, restricted random forest, and longitudinal mixed effects. Validation used repeated treatment-site-stratified 5-fold cross-validation with five repetitions, nested hyperparameter tuning, and paired participant-level bootstrap comparisons with 10,000 resamples. Adding Week-3 and Week-5 ADAS-Cog total-score response improved prediction. The early total-response ridge achieved overall MAE 3.067, RMSE 3.885, and $R^2=0.384$. Replacing two total-response variables with 22 component-level changes worsened performance (MAE 3.363, RMSE 4.269, $R^2=0.257$). The paired MAE difference was -0.297 points (95% CI -0.485 to -0.115). Mixed effects was competitive (MAE 3.162), with an uncertain overall difference from total-response ridge. Sensitivity analyses using absolute future scores and excluding Week-3 response supported the main findings. In this sample, early aggregate ADAS-Cog response was more informative than a higher-dimensional domain-specific representation. The model is an internally validated early-response updating tool, not a pretreatment selection model or evidence of causal rTMS efficacy.
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Astrophysics of Galaxies

Registering Gaia positions on VLBI images: constraining opacity-driven core shift in B 1038+528 / B 1038+529

M. H. Xu and Y. Y. Kovalev

Because of the complex and time-variable radio structure of active galactic nuclei (AGNs), absolute astrometric positions cannot generally be registered unambiguously with specific features in VLBI images. We aim to register Gaia optical centroids on VLBI images with about 0.1 mas accuracy and to locate the optical emission relative to the radio core in the quasar pair B 1038+528 and B 1038+529, in which the opacity-driven core shift was first discovered. We combine the relative position of the pair from phase-referenced VLBI at four epochs between 1981 and 1995, their Gaia optical positions, and the nearly orthogonal radio-jet directions. Assuming that each Gaia–core displacement lies along the local jet axis, we register the Gaia centroids relative to the radio cores with about 0.1 mas accuracy without using absolute VLBI positions. The Gaia centroids lie upstream of the 8.4-GHz radio cores, by 293 {\pm} 93 {\mu}as in B 1038+529 and 169 {\pm} 96 {\mu}as in B 1038+528. The latter agrees with the 260 {\mu}as 8.4-GHz core shift implied by the 2.3–8.4 GHz shift measured for this quasar four decades ago. The absolute astrometric position of B 1038+528 with VLBI lies about 0.6 mas downstream of the core, and its geodetic position time series drifts along the jet by more than 1 mas, showing that the 0.8–1.6 mas Gaia–VLBI offsets of the pair are dominated by source structure rather than by core shift. Gaia positions can be registered on VLBI images with an accuracy set by the astrometry uncertainties. If the Gaia centroids mark the jet base, the measured separations are absolute 8.4-GHz core shifts, corresponding to projected distances of about 1.2 pc and about 2.5 pc from the jet base; a contribution from an optical jet emission would make them lower limits. The method is applicable to AGNs with Gaia detection and phase-referenced VLBI astrometry.
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Statistical Methodology

The Lambert Penalty: Logarithmic Shrinkage for Sparse Regression

Bahad{\i}r Yüzbaş{\i}

Sparse regression must balance prediction accuracy with reproducible variable selection. We introduce the Lambert penalty by limiting how quickly the retained fraction of a scalar score increases after variable entry. Maximizing retention under this scale-equivariant constraint yields a logarithmic transition from exact zero to an identity tail. The bounded penalty has a fixed shape and a single penalty parameter selected by training-only five-fold cross-validation. We derive an explicit scalar update, a sharp weak-convexity bound, a sufficient condition for multivariate uniqueness, and conditional convergence of exact cyclic coordinate descent. For fixed dimension, global minimizers are uniformly equivalent to the oracle estimator over an asymptotic tuning band. Gaussian simulations compare Lambert with LASSO, ridge, elastic net, MCP, and SCAD. Lambert favors prediction for strong equal signals and usually improves support recovery over SCAD; MCP often gives better support recovery, and convex methods remain competitive with weak signals or strong correlation. In a QSAR toxicity application, repeated nested cross-validation gives competitive prediction with compact, stable models, although error differences from MCP and SCAD are small. The empirical design followed exploratory development; independent confirmation remains necessary.
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High Energy Physics (Experiment)

Observation of the electromagnetic Dalitz transition $J/\psi\to e^+ e^- \eta_c$

BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson et al. (+766 more)

Using $(10.087\pm0.044)\times10^9$ $J/\psi$ events collected with the BESIII detector at the $e^+e^-$ BEPCII collider, we present the first observation of the electromagnetic Dalitz decay $J/\psi\to e^+ e^- \eta_c$. The relative branching fraction $R \equiv \frac{\Gamma(J/\psi\to e^+e^-\eta_c)}{\Gamma(J/\psi\to \gamma\eta_c)}$ is determined to be $(0.65\pm0.02_{\rm stat.}\pm0.07_{\rm sys.})\%$, where the first uncertainty is statistical and the second systematic. The $q^2$-dependent form factors are also extracted, and no significant deviation from the theoretical prediction is seen.
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All authors BESIII Collaboration: M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Aliberti, A. Amoroso, Q. An, Y. H. An, M. S. Anderson, Y. Bai, O. Bakina, H. R. Bao, X. L. Bao, M. Barbagiovanni, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. B. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, D. Cabiati, H. Cai, M. H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, T. T. Chang, G. R. Che, Y. Z. Che, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, W. Chen, X. R. Chen, X. T. Chen, X. Y. Chen, Y. B. Chen, Y. Q. Chen, Z. K. Chen, J. Cheng, L. N. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. Cottee-Meldrum, H. L. Dai, J. P. Dai, X. C. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denisenko, M. Destefanis, F. De Mori, E. Di Fiore, X. X. Ding, Y. Ding, Y. X. Ding, J. Dong, L. Y. 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