arXiv subjects

Ang Zhang

Publications and source records attributed to Ang Zhang.

8 recordsLinked to original sources

arXiv2026

Disclosed Human-Capital Disruption and Firm-Specific Risk

Human capital is a central organizational input, but standard financial data reveal little about firm-specific disruptions to workforce availability, cost, skills, and continuity. I construct a measure of disclosed human-capital disruption from earnings calls using author-defined coding criteria and a contextual language model. Within firms, a one-standard-deviation increase in the annual measure is associated with 0.55 percentage points higher idiosyncratic volatility, 0.58 percentage points higher downside deviation, and a 0.46 percentage point lower worst monthly return, with no corresponding relation to market beta. The results are stable across seven broader and narrower classification rules and remain after removing explicit labor-shortage passages and controlling for a recently published labor-shortage measure and transcript-wide negative and uncertain language. At the call level, human-capital disruption predicts approximately 0.50% higher idiosyncratic volatility over the following 42 trading days after conditioning on pre-call risk. Risk is elevated before the call as well, and the score predicts the continuation of that firm-specific risk state over the following 42 trading days. After telegraphing and succession passages are removed, the measure also predicts subsequent named-executive roster exits and the incumbent CEO's effective departure from office. Earnings calls therefore reveal disturbances to a key organizational input that are broader than labor shortages and informative about the distribution of firm outcomes.

q-fin.ST↗

arXiv2026

GRACE: Generative Recommender Acceleration Engine for Real-Time Ads Retrieval

Productionizing generative recommenders for high-volume, real-time ads retrieval creates two serving challenges: eligibility, ensuring that each generated ad is eligible for the request under the advertiser's audience targeting rules, and compute, which requires meeting strict latency and GPU cost requirements while remaining capable of generating thousands of ads per request with wide-beam decoding. This paper presents GRACE, a serving system for ads generative retrieval that addresses both challenges. For eligibility, GRACE introduces Generative Target Matching (GTM), which extends catalog-valid constrained decoding with personalized filtering over Semantic ID (SID) prefixes using bitmask and Bloom filter matchers derived from targeting rules. SID-level GTM improves final ad-level target matching pass rate from 23.55% to 40.42% over constrained decoding alone. For compute-cost and latency, GRACE targets encoder-decoder Transformers, which are more lightweight than LLMs. It redesigns the decoder around the wide-beam, short-sequence regime, covering attention kernels, KV cache, and beam search optimizations. On NVIDIA GH200, compared with the faster of FlashAttention-2 and FlashAttention-3 baselines, GRACE improves cross-attention latency by 68.0 times and self-attention latency by 23.4-25.8 times across decode steps. Together, these changes reduce decoder latency by 11.1 times, keeping ads generative retrieval within latency and compute requirements.

cs.IR↗

arXiv2025

UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound

Echocardiography is routine for cardiac examination. However, 2D ultrasound (US) struggles with accurate metric calculation and direct observation of 3D cardiac structures. Moreover, 3D US is limited by low resolution, small field of view and scarce availability in practice. Constructing the cardiac anatomical twin from 2D images is promising to provide precise treatment planning and clinical quantification. However, it remains challenging due to the rare paired data, complex structures, and US noises. In this study, we introduce a novel generative framework UltraTwin, to obtain cardiac anatomical twin from sparse multi-view 2D US. Our contribution is three-fold. First, pioneered the construction of a real-world and high-quality dataset containing strictly paired multi-view 2D US and CT, and pseudo-paired data. Second, we propose a coarse-to-fine scheme to achieve hierarchical reconstruction optimization. Last, we introduce an implicit autoencoder for topology-aware constraints. Extensive experiments show that UltraTwin reconstructs high-quality anatomical twins versus strong competitors. We believe it advances anatomical twin modeling for potential applications in personalized cardiac care.

eess.IV↗

arXiv2025

Hierarchical Corpus-View-Category Refinement for Carotid Plaque Risk Grading in Ultrasound

Accurate carotid plaque grading (CPG) is vital to assess the risk of cardiovascular and cerebrovascular diseases. Due to the small size and high intra-class variability of plaque, CPG is commonly evaluated using a combination of transverse and longitudinal ultrasound views in clinical practice. However, most existing deep learning-based multi-view classification methods focus on feature fusion across different views, neglecting the importance of representation learning and the difference in class features. To address these issues, we propose a novel Corpus-View-Category Refinement Framework (CVC-RF) that processes information from Corpus-, View-, and Category-levels, enhancing model performance. Our contribution is four-fold. First, to the best of our knowledge, we are the foremost deep learning-based method for CPG according to the latest Carotid Plaque-RADS guidelines. Second, we propose a novel center-memory contrastive loss, which enhances the network's global modeling capability by comparing with representative cluster centers and diverse negative samples at the Corpus level. Third, we design a cascaded down-sampling attention module to fuse multi-scale information and achieve implicit feature interaction at the View level. Finally, a parameter-free mixture-of-experts weighting strategy is introduced to leverage class clustering knowledge to weight different experts, enabling feature decoupling at the Category level. Experimental results indicate that CVC-RF effectively models global features via multi-level refinement, achieving state-of-the-art performance in the challenging CPG task.

cs.CV↗

arXiv2022

CHES: a space-borne astrometric mission for the detection of habitable planets of the nearby solar-type stars

The Closeby Habitable Exoplanet Survey (CHES) mission is proposed to discover habitable-zone Earth-like planets of the nearby solar-type stars ($\sim 10~\mathrm{pc}$ away from our solar system) via micro-arcsecond relative astrometry. The major scientific objectives of CHES are: to search for Earth Twins or terrestrial planets in habitable zones orbiting 100 FGK nearby stars; further to conduct a comprehensive survey and extensively characterize the nearby planetary systems. The primary payload is a high-quality, low-distortion, high-stability telescope. The optical subsystem is a coaxial three-mirror anastigmat (TMA) with a $1.2 \mathrm{~m}$-aperture, $0.44^{\circ} \times 0.44^{\circ}$ field of view and $500 \mathrm{~nm}-900 \mathrm{~nm}$ working waveband. The camera focal plane is composed of 81 MOSAIC scientific CMOS detectors each with $4 \mathrm{~K} \times 4 \mathrm{~K}$ pixels. The heterodyne laser interferometric calibration technology is employed to ensure micro-arcsecond level (1 $\mu$as) relative astrometry precision to meet the requirements for detection of Earth-like planets. CHES satellite operates at the Sun-Earth L2 point and observes the entire target stars for 5 years. CHES will offer the first direct measurements of true masses and inclinations of Earth Twins and super-Earths orbiting our neighbor stars based on micro-arcsecond astrometry from space. This will definitely enhance our understanding of the formation of diverse nearby planetary systems and the emergence of other worlds for solar-type stars, and finally to reflect the evolution of our own solar system.

astro-ph.EP↗

arXiv2021

Controlling Pivoting Gait using Graph Model Predictive Control

Pivoting gait is efficient for manipulating a big and heavy object with relatively small manipulating force, in which a robot iteratively tilts the object, rotates it around the vertex, and then puts it down to the floor. However, pivoting gait can easily fail even with a small external disturbance due to its instability in nature. To cope with this problem, we propose a controller to robustly control the object motion during the pivoting gait by introducing two gait modes, i.e., one is the double-support mode, which can manipulate a relatively light object with faster speed, and the other is the quadruple-support mode, which can manipulate a relatively heavy object with lower speed. To control the pivoting gait, a graph model predictive control is applied taking into account of these two gait modes. By adaptively switching the gait mode according to the applied external disturbance, a robot can stably perform the pivoting gait even if the external disturbance is applied to the object.

cs.RO↗

arXiv2018

Translational and rotational dynamical heterogeneities in granular systems

We use X-ray tomography to investigate the translational and rotational dynamical heterogeneities of a three dimensional hard ellipsoids granular packing driven by oscillatory shear. We find that particles which translate quickly form clusters with a size distribution given by a power-law with an exponent that is independent of the strain amplitude. Identical behavior is found for particles that are translating slowly, rotating quickly, or rotating slowly. The geometrical properties of these four different types of clusters are the same as those of random clusters. Different cluster types are considerably correlated/anticorrelated, indicating a significant coupling between translational and rotational degrees of freedom. Surprisingly these clusters are formed already at time scales that are much shorter than the $\alpha-$relaxation time, in stark contrast to the behavior found in glass-forming systems.

cond-mat.dis-nn↗

arXiv2017

Granular materials flow like complex fluids

Granular materials such as sand, powders, foams etc. are ubiquitous in our daily life, as well as in industrial and geotechnical applications. Although these disordered systems form stable structures if unperturbed, in practice they do relax because of the presence of unavoidable external influences such as tapping or shear. Often it is tacitly assumed that for granular systems this relaxation dynamics is similar to the one of thermal glass-formers, but in fact experimental difficulties have so far prevented to determine the dynamic properties of three dimensional granular systems on the particle level. This lack of experimental data, combined with the fact that in these systems the motion of the particles involves friction, makes it very challenging to come up with an accurate description of their relaxation dynamics. Here we use X-ray tomography to determine the microscopic relaxation dynamics of hard granular ellipsoids that are subject to an oscillatory shear. We find that the distribution function of the particle displacement can be described by a Gumbel law with a shape parameter that is independent of time and the strain amplitude $\gamma$. Despite this universality, the mean squared displacement of a tagged particle shows power-laws as a function of time with an exponent that depends on $\gamma$ and the time interval considered. We argue that these results are directly related to the existence of the microscopic relaxation mechanisms that involve friction and memory effects. These observations demonstrate that on the particle level the dynamical behavior of granular systems is qualitatively different from the one of thermal glass-formers and instead more similar to the one of complex fluids. Thus we conclude that granular materials can relax even when the driving is weak, an insight which impacts our understanding of the nature of granular solids.

cond-mat.soft↗