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Jing Zhang

Publications and source records attributed to Jing Zhang.

At least 55 records · Page 3Linked to original sources

Certificate Complexity of Elementary Symmetric Boolean Functions of Arbitrary Degree

Let $σ_{n,d}$ denote the elementary symmetric Boolean function of $n$ variables and degree $d$. Previous work determined $C(σ_{n,d})$ when $d$ is odd or a power of two, but the general even non-power-of-two case remained open. We determine the certificate complexity for every degree $1\le d\le n$, thereby completing the classification for elementary symmetric Boolean functions. Writing $d=2^t m$ with $m$ odd, we obtain an explicit formula in which the possible deficit from the maximal value $n$ is controlled by $2^t$, while the exact value is determined by a binary containment condition involving $m$. In particular, \[ n-2^{ν_2(d)}+1\le C(σ_{n,d})\le n, \] and we characterize when the upper bound is attained. Moreover, we determine the least positive period of the certificate-complexity deficit $Δ_d(n)=n-C(σ_{n,d})$: it is $1$ for odd $d$, equals $d$ when $d$ is a power of two, and equals $2^{\lfloor\log_2 d\rfloor+1}$ for even non-power-of-two $d$. This least-period problem is distinct from the classical periodicity of the underlying value sequence $\binom{j}{d}\bmod 2$. The known odd-degree and power-of-two formulas are recovered as special cases.

math.CO↗

Enabling a Unified Cross-Domain Representation for Two-Finger Gripper Manipulation via Interaction-Centric Modeling

Achieving robust cross-embodiment generalization in imitation learning demands overcoming a critical representation flaw that inextricably entangles task semantics with hardware-specific visual geometry. We propose an interaction-centric framework that leverages the shared structure of two-finger grippers via a parameterized universal gripper abstraction, yielding a canonical gripper-frame representation. Given language and RGB-D observations, a VLM infers the subtask and grounds an interaction triplet (gripper, held, target), while SAM~2.1 tracks masks to reduce VLM queries. We design concise hybrid features that combine target/collision artificial potential fields for global guidance with segmented gripper-frame point clouds for local geometry, and use a Flow-Matching Transformer to predict smooth 7-DoF action chunks. Experiments in simulation and real-world tasks demonstrate that ours is the first imitation learning approach to simultaneously achieve competitive benchmark scores and extreme cross-embodiment/cross-viewpoint zero-shot sim-to-real transfer to completely distinct, heterogeneous robot platforms.

cs.RO↗

TokenScanner: Detecting Backdoors and Discovering Triggers in Text-to-Image LoRAs via Full Vocabulary Scanning

LoRAs are widely studied for adapting base text-to-image diffusion models. However, a backdoored LoRA can hide a backdoor: it behaves normally in most cases, but produces attacker-specified content (the backdoor target) when a hidden backdoor trigger appears in the input prompt. Detecting such backdoors before using an untrusted LoRA is important for the safety of LoRA adaptation. We present TokenScanner, a model-level vocabulary scanner for backdoor detection within a LoRA fine-tuned text-to-image diffusion model, aiming to discover the malicious trigger for trustworthy LoRA adaptation. The key observation is that backdoor trigger tokens that appear in a larger proportion of training prompts tend to induce more prominent token-specific LoRA responses than those induced by unrelated tokens. TokenScanner therefore scans the tokenizer vocabulary and measures token-wise LoRA responses in the U-Net and the text encoder. It uses these responses to detect backdoored LoRAs and rank candidate trigger tokens for subsequent testing of backdoor activation. Experiments on seven backdoor settings, comprising 840 backdoored LoRAs and 840 real-world benign test LoRAs, show that TokenScanner achieves 95.96% AUC and 96.55% TPR at an FPR of 10.95%. It also achieves 89.40% Hit@1 and 99.40% Hit@5 for trigger discovery across all seven settings.

cs.CR↗

Moiré droplet of ultracold Bose gases in a twisted-bilayer optical lattice

We report the emergence of Moiré droplet in two-dimensional ultracold bosons subjected to a spin-dependent optical lattice, effctively realizing a twisted-bilayer configuration. We show that the droplet formation dramatically enhances the visibility of Moiré pattern in the density profile, even for exceptionally weak lattice potentials. The Moiré pattern can be enhanced similarly by increasing the lattice depth, which, however, also induces droplet diffusion characterized by a spreading density profile. Furthermore, we demonstrate a dynamical generation of Moiré pattern by dragging a small droplet through a moving lattice. At appropriate velocities, the droplet undergoes bifurcation and exhibits pronounced Moiré pattern within periodic time intervals. Our results establish the ultracold droplet as a compelling platform for simulating interacting Moiré physics, particularly the interplay between Moiré lattice and bound-state formation.

cond-mat.quant-gas↗

RoboRecover: Benchmarking Robot Policy Recovery under Execution Deviations

Robot-policy benchmarks increasingly cover diverse tasks and preset out-of-distribution conditions, but typically evaluate complete trajectories from predefined initial states. These evaluations often focus on the initialized scene and the final outcome, while paying less attention to the dynamic interaction process. During closed-loop execution, actions and contacts can alter object relations and task progress, producing off-nominal intermediate states that need recovery. Recovery requires a policy to infer how task progress has changed, correct the relevant relations, and continue the original goal. We introduce RoboRecover, a benchmark for robot policy recovery under execution deviations. RoboRecover selects deviation states from trajectories, reconstructs them by replaying action prefixes, and evaluates policies on the original task. RoboRecover contains 2,000 scenarios across RoboTwin and LIBERO, with 1,000 scenarios and a fixed 800/200 train/test split on each platform. Results show that initial-state performance does not determine recovery performance and policies exhibit different recovery strengths across scenarios. Using its training split, RoboRecover further supports study on recovery interventions. RoboRecover establishes recovery from execution-induced intermediate states as a distinct dimension of robot policy evaluation.

cs.RO↗

Evaluation of OpenAI o1: Opportunities and Challenges of AGI

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: -83.3% success rate in solving complex competitive programming problems, surpassing many human experts. -Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. -100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. -Advanced natural language inference capabilities across general and specialized domains like medicine. -Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. -Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. -Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. -Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence.

cs.CL↗

Sensitivity and Block Sensitivity of Elementary Symmetric Boolean Functions of Arbitrary Degree

Let $σ_{n,d}$ denote the elementary symmetric Boolean function of $n$ variables and degree $d$. We completely determine the sensitivity, average sensitivity, and block sensitivity of $σ_{n,d}$ for every $1\le d\le n$. Using Lucas' theorem, we obtain a uniform binary description of the Hamming-weight value sequence, from which the sensitivity and average-sensitivity formulas follow and the computation of block sensitivity reduces to at most four explicit candidates. Combining these results with the arbitrary-degree formula for certificate complexity, we determine the exact relations among sensitivity, block sensitivity, and certificate complexity. We also prove a general result for symmetric Boolean functions: every nonconstant symmetric Boolean function $f$ satisfies \[ \bs(f)\le \max\{s(f),C(f)-1\}. \] Consequently, only the three patterns \[ s=\bs=C,\qquad s=\bs<C,\qquad s<\bs<C \] can occur for nonconstant symmetric Boolean functions. For elementary symmetric Boolean functions, we give necessary and sufficient conditions for each of these three patterns, thereby completely classifying the relations among $s(σ_{n,d})$, $\bs(σ_{n,d})$, and $C(σ_{n,d})$. In particular, we obtain a necessary and sufficient characterization of the full strict hierarchy \[ s(σ_{n,d})<\bs(σ_{n,d})<C(σ_{n,d}), \] and exhibit infinite families for which it holds.

math.CO↗

EgoPush: Egocentric Multi-Object Rearrangement for Mobile Robots via Constrained Teacher Observability

Humans rearrange objects in cluttered environments using egocentric perception, actively moving to keep task-relevant spatial cues in view. Mobile robots have not matched this: rearrangement is usually built on a global pose estimate or a map, which is exactly what a robot carrying one camera lacks, while pushing keeps changing the scene it would have to be built from. We present EgoPush, which pushes objects into anchor-relative formations from onboard RGB-D alone, with no global localization, external tracking, or map at deployment, and transfers zero-shot to a TurtleBot in controlled and visually cluttered scenes. What makes this learnable turns out to be a property of the teacher rather than of the student: three privileged teachers trained with identical rewards, architecture, and hyperparameters all exceed $98\%$ success, yet their distilled egocentric students reach $0\%$, $54.8\%$, and $87.3\%$, the only variable being the teacher's observation function. EgoPush therefore trains the teacher under egocentric observability constraints, restricting it to visibility-limited cues and revealing target references only when the anchor is centrally visible, so that its supervision is recoverable by a depth-based student distilled online. Making the teacher trainable in the first place needs two further pieces: a role-grouped object-centric interface shared by teacher and student, and stage-wise temporally decayed rewards for long-horizon credit assignment. Videos, the playable task, and code are available at https://ai4ce.github.io/EgoPush/.

cs.RO↗

Complex Problem Solving in Large Language Models: A Statistical Control Survey and Diagnostic Framework

Complex problem solving (CPS) with large language models (LLMs) is often framed as a matter of stronger reasoning or longer generation. Yet early-step error amplification, prompt brittleness, and failures to revise incorrect commitments are difficult to explain by missing knowledge or expressive capacity alone. This survey interprets CPS as a sequential estimation-and-decision problem over a latent solution state. A controller maintains a belief about an unobserved solution trajectory, updates it as noisy intermediate evidence arrives, and decides whether to commit, verify, branch, roll back, or abstain to minimize expected loss. Reasoning supplies candidate transitions and interpretations, whereas process control shapes and evaluates those proposals and regulates subsequent transitions and observations. Within this framework, we organize existing methods around five components: explicit state representation, transition structuring, validation and constraint enforcement, search and rollback, and uncertainty management. We also interpret evaluation metrics according to the statistical quantities they estimate. The framework further yields a diagnostic hypothesis: interventions should be most effective when they target the error or uncertainty component implicated by an observed failure. We distinguish systematic, stochastic, and irreducible error together with epistemic and aleatoric uncertainty, and call this alignment problem-control fit and its failure control mismatch. For example, additional sampling may reduce sampling variability while leaving a shared systematic error unchanged. This perspective clarifies what current methods estimate and control, what remains uncontrolled, and why reliable validation, targeted recovery, calibrated uncertainty, and matched-budget evaluation are central open problems.

stat.ML↗

Improving Offline Goal-Conditioned Reinforcement Learning via Selective Reward Stimulation

Goal-conditioned reinforcement learning aims to learn policies that reach specified goals, but remains challenging in offline settings with sparse rewards and long-horizon dependencies. In such settings, goal-completion information can be temporally distant from the early decisions that enable success, while offline value estimation introduces additional error. We study this issue from a reward-propagation perspective and show, in a stylized delayed-goal setting, how goal-directed value separation can become small relative to local estimation error. Motivated by this analysis, we propose Reward Stimulation Implicit Q-Learning (RSIQL), a simple non-hierarchical method that introduces additional reward signals at progress-making intermediate states in offline trajectories. RSIQL uses an auxiliary goal-conditioned value function to identify intermediate states estimated to make progress toward the goal and applies reward stimulation to provide less-delayed training supervision. Unlike hierarchical methods, RSIQL does not learn a separate high-level subgoal policy. Experiments on D4RL goal-reaching benchmarks and OGBench show that RSIQL improves over goal-conditioned IQL on average and achieves performance competitive with hierarchical offline goal-conditioned methods, while retaining a simple flat policy structure.

cs.LG↗

From Test Performance to Risk-Based Effect Sizes: A Unified Wald-Type Framework to Design Clinical Validation Studies for Binary and Survival Outcomes

Clinical validation studies of predictive tests are usually designed to focus on sensitivity ($Se$) and specificity ($Sp$), while statistical power is often calculated on regression-effect scales (e.g., risk ratio, hazard ratio). However, these quantities are statistically connected. Here, we provide closed-form links from sensitivity, specificity, and disease prevalence ($π$) to predictive risks, risk contrasts, and Wald-type variance, power, and sample-size formulas for binary and fixed-horizon survival outcomes. Analyses of statistical efficiency via C- and D-optimal principles demonstrate how prevalence and threshold choices affect study efficiency, supporting rapid decisions in preliminary studies and informing the design of subsequent, larger studies. Simulations show good calibration across most realistic scenarios; when events are rare and test effects are simultaneously very large, continuity and minimum-event corrections are needed to stabilize the approximation. We illustrate the framework with a case study describing use of the coronary artery calcium score for predicting incident cardiovascular disease in patients with type 2 diabetes mellitus. The formulas let investigators check power and required enrollment directly from $(Se,Sp,π)$, without running a separate simulation for each design candidate.

stat.ME↗

JewelTry: Mask-Free Scale Aware Jewelry Virtual Try-On

Virtual try-on (VTON) enables customers to visualize how fashion products appear when worn and has become an important technology for online shopping. While recent advances have substantially improved garment VTON, jewelry remains a challenging and underexplored category due to its small size, rigid structure, and sensitivity to fine-grained visual details. Realistic jewelry VTON requires not only faithful appearance transfer but also accurate scale and placement relative to the wearer. Existing jewelry VTON methods typically rely on mask guidance, whereas mask-free approaches lack explicit guidance for modeling the product scale. To bridge this gap, we introduce JVTO-Bench, a benchmark dataset for scale-faithful jewelry VTON, providing reference source target triplets with real-world product-scale annotations across four major jewelry categories. Building upon this benchmark, we propose JewelTry, a mask-free diffusion framework for scale-aware jewelry VTON. JewelTry incorporates a scale adapter that encodes product dimensions into a scale token, enabling the model to learn scale relationships between jewelry items and surrounding human anatomy in-context. To further improve jewelry consistency, we introduce a single-directional condition attention mechanism and an attention refinement loss that preserve both coarse geometry and fine-grained structural details of the reference jewelry. Extensive experiments show that JewelTry achieves a balance among visual fidelity, background preservation, object consistency and scale accuracy, establishing a strong baseline for mask-free, scale-aware jewelry virtual try-on.

cs.CV↗

Thermal Hall effect in elemental niobium, a two-gap superconductor

Niobium holds a pivotal place in superconductivity history: it is not only the elemental superconductor with the highest critical temperature, but also a long-standing candidate for multiband pairing whose evidence has remained controversial. A two-gap scenario was proposed as early as 1959, yet experimental proof stayed elusive. Here, through thermal Hall effect measurements, we unambiguously demonstrate a crossover in transverse thermal transport below $T_{\rm c}$, where the dominant carrier switches from hole-like to electron-like upon cooling. This crossover is a clear hallmark of two distinct superconducting condensates. Fitting our data to a two-gap Bardeen-Rickayzen-Tewordt (BRT) model yields a second energy gap of approximately 0.22 $k_{\rm B}T_{\rm c}$, only 11\% of the dominant gap. This small gap size accounts for the difficulty in resolving the two-gap structure in earlier experiments. Crucially, the temperature at which the electron-like contribution to longitudinal thermal conductivity begins to dominate coincides with the onset of the sign reversal in the thermal Hall coefficient, confirming consistency between the two methods, and providing crucial information on the assignment of the superconducting gaps to their respective hole- or electron-like Fermi surface sheets. These findings not only resolve a longstanding controversy, but also establish that multigap superconductivity is far more common than previously assumed, and demonstrate the thermal Hall effect as a powerful probe for resolving gap multiplicities in superconductors.

cond-mat.supr-con↗

Global boundedness of the chemotaxis system with weakly singular sensitivity and nonlocal term in any dimension

This paper is concerned with the parabolic-elliptic chemotaxis system involving weakly singular sensitivity and a nonlocal source: $u_t=Δu-χ\nabla\cdot\left(\frac{u}{v^k}\nabla v\right) +u^α\left(r-μ\int_Ωu^β\,dx\right)$ and $0=Δv-v+u^γ$ under homogeneous Neumann boundary conditions in a smooth bounded domain \(Ω\subset\mathbb{R}^N\) with \(N\ge1\), where \(χ,r,μ,γ>0\), \(k\in(0,1)\) and \(α,β\ge1\). In view of the enhanced aggregation induced by singular sensitivity and the suppression of excessive population growth by the nonlocal damping, we identify sufficient conditions under which solutions remain globally bounded. We show that classical solutions are globally bounded in the following cases: \par\smallskip \begin{center} \small \setlength{\tabcolsep}{5pt} \setlength{\arrayrulewidth}{0.3pt} \renewcommand{\arraystretch}{1.25} \begin{tabular}{c|c|c|c} \multirow{2}{*}{$β>1$} & \multirow{2}{*}{$γ=1$} & $N=1$ & $1\leqα<1+2β$ \\ \cline{3-4} & & $N\geq2$ & $\begin{gathered} 1\leqα<2,\quad α+β>2+\tfrac{N}{2},\\[-0.5mm] \text{or}\quad 2\leqα<1+\tfrac{2β}{N} \end{gathered}$ \\ \hline \multirow{2}{*}{$β=1$} & $γ=1$ & $N=1$ & \multirow{2}{*}{ $\begin{array}{l@{\quad}l@{\quad}l} \text{Case 1:} & 1\leqα<1+\tfrac{2}{N} & \text{if}\quad m_0<\tfrac{r}μ,\\[4pt] \text{Case 2:} & α\geq1 & \text{if}\quad m_0\geq\tfrac{r}μ \end{array}$ } \\ \cline{2-3} & $0<γ<\tfrac{2}{N}$ & $N\geq2$ & \\ \end{tabular} \end{center} \par \smallskip \noindent Here \(m_0:=\int_Ωu_0\) denotes the initial total mass. It is worth noting that both the uniform-in-time \(L^p\)-estimate and the \(L^\infty\)-bound obtained by Moser iteration are derived without first establishing a uniform positive lower bound for \(v\).

math.AP↗

Bi-Level Routing and Sparse Spatial Attention based Multi-View BEV 3D Object Detection for Autonomous Driving

Bird's Eye View (BEV)-based multi-view 3D object detection suffers from challenges of computational complexity, multi-scale feature extraction, and efficiency of dense 2D-to-BEV view transformation. To address these problems, this paper proposes an improved BEV 3D object detection algorithm Sparse-BEVNet. Firstly, a Bi-Level Routing Attention (BRA) mechanism is introduced into the image feature extraction network to reduce the computational burden of the backbone. Second, Cascaded Group Attention (CGA) is employed in the feature fusion module, which enhances deep interaction across features of different hierarchical levels without introducing additional computational overhead. Furthermore, a Sparse Spatial Cross-Attention mechanism is adopted to replace the conventional dense view projection pipeline. Experimental results on the public nuScenes dataset demonstrate that the proposed method achieves a mean Average Precision (mAP) of 45.2% and a nuScenes Detection Score (NDS) of 54.5%, corresponding to 3.6% and 2.8% improvements relative to the baseline model, respectively.

cs.CV↗

LLM-Enhanced Dual-Branch Learning for Large-Scale Multi-Label Text Classification

Large-scale multi-label text classification assigns a small subset of relevant labels to each document from a vocabulary containing thousands or tens of thousands of candidate labels. Although pretrained language models have improved semantic text representations, most representation-based approaches center their prediction pipelines on a primary encoder or combine auxiliary features within a single ranker. The complementarity between heterogeneous language models therefore remains insufficiently explored. We propose DualMLC, a dual-branch framework that processes the same document through an autoregressive decoder-only language model and a bidirectional encoder. Each branch maintains its own representation pathway and independently estimates relevance scores over the shared label space. DualMLC combines the two score vectors through late logit fusion, allowing shared evidence to reinforce relevant labels and branch-specific evidence to compensate for limitations in the other branch's representation. DualMLC achieves state-of-the-art results on three widely used large-scale multi-label text classification benchmarks. Ablation results further confirm that integrating the heterogeneous predictors produces stronger rankings than either branch alone. The source code is publicly available at https://github.com/huiyegit/DualMLC.

cs.CL↗

A non-trivially non-trivial automorphism at an inaccessible cardinal

We show that, relative to the existence of an inaccessible cardinal, it is consistent that for an inaccessible cardinal $κ$, there is a non-trivial automorphism of $\mathcal{P}(κ)/\mathrm{fin}$ whose restriction to $\mathcal{P}(β)/\mathrm{fin}$ is induced by a bijection on $β$ for every $β<κ$. This answers questions posed by Larson--McKenney, Shelah--Steprāns, and Veličković.

math.LO↗

A Graph Foundation Model for Large-Scale MIMO Detection

Large-scale multiple-input multiple-output (MIMO) detection is fundamental to modern wireless networks but constrained by performance-complexity trade-offs. Existing detectors, whether classical or learning-based, often fall short in either scalability or generalizability across heterogeneous scenarios. To overcome these limitations, we introduce a wireless-native graph foundation model (GFM) tailored for large-scale MIMO detection. The proposed GFM employs a physics-informed hybrid architecture, integrating the local correlation extraction of message passing neural networks with the global attention of graph Transformers, encoding the physical interference patterns from the expectation propagation algorithm. Via extensive pre-training, this synergy enables the learning of a general-purpose detection mapping scalable across antenna dimensions and channel conditions. For rapid downstream deployment, parameter-efficient fine-tuning is leveraged to adapt the GFM to specific non-ideal system regimes with minimal overhead. To enhance inference efficiency, a mixture-of-experts mechanism is embedded at downstream deployment to dynamically activate only the necessary sub-modules. Evaluations show that the proposed GFM consistently outperforms classical detectors and advanced data-driven baselines in accuracy, configuration generality, and cross-scenario transferability across various challenging zero-shot and few-shot conditions.

cs.IT↗