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Chao Zhang

Publications and source records attributed to Chao Zhang.

At least 577 records · Page 32Linked to original sources

Investigating $D^0$ meson production in $p-$Pb collisions at 5.02 TeV with a multi-phase transport model

We study the production of $D^0$ meson in $p$+$p$ and $p-$Pb collisions using the improved AMPT model considering both coalescence and independent fragmentation of charm quarks after the Cronin broadening are included. After a detailed discussion of the improvements implemented in the AMPT model for heavy quark production, we show that the modified AMPT model can provide good description of $D^0$ meson spectra in $p-$Pb collisions, the $Q_{\rm pPb}$ data at different centrality and $R_{\rm pPb}$ data in both mid- and forward (backward) rapidities. We also studied the effects of nuclear shadowing and parton cascade on the rapidity dependence of $D^{0}$ meson production and $R_{\rm pPb}$. Our results indicate that having the same strength of the Cronin (i.e $δ$ value) obtained from the mid-rapidity data leads to a considerable overestimation of the $D^0$ meson spectra and $R_{\rm pPb}$ data at high $p_{T}$ in the backward rapidity. As a result, the $δ$ is determined via a $χ^2$ fitting of the $R_{\rm pPb}$ data across various rapidities. This work lays the foundation for a better understanding of cold-nuclear-matter (CNM) effects in relativistic heavy-ion collisions.

nucl-th↗

Superconductivity of the New Medium-Entropy Alloy V4Ti2W with a Body-Centered Cubic Structure

Medium- and high-entropy alloy (MEA and HEA) superconductors have attracted considerable interest since their discovery. This paper reports the superconducting properties of ternary tungsten-containing MEA V4Ti2W for the first time. V4Ti2W is a type II superconductor with a body-centered cubic (BCC) structure. Experimental results of resistivity, magnetization, and heat capacity indicate that the superconducting transition temperature of the MEA V4Ti2W is roughly 5.0 K. The critical magnetic fields at the upper and lower ends are 9.93(2) T and 40.7(3) mT, respectively. Interestingly, few BCC MEA superconductors with VEC greater than 4.8 have been found. The addition of tungsten leads to a VEC of 4.83 e/a for V4Ti2W, which is rarely higher than the 4.8 value. Adding tungsten element expands the variety of MEA alloys, which may improve the microstructure and mechanical properties of materials and even superconducting properties. This material could potentially offer a new platform for the investigation of innovative MEA and HEA superconductors.

cond-mat.supr-con↗

Wrist-bound Guanxi, Jiazu, and Kuolie: Unpacking Chinese Adolescent Smartwatch-Mediated Socialization

Adolescent peer relationships, essential for their development, are increasingly mediated by digital technologies. As this trend continues, wearable devices, especially smartwatches tailored for adolescents, are reshaping their socialization. In China, smartwatches like XTC have gained wide popularity, introducing unique features such as "Bump-to-Connect" and exclusive social platforms. Nonetheless, how these devices influence adolescents' peer experience remains unknown. Addressing this, we interviewed 18 Chinese adolescents (age: 11 -- 16), discovering a smartwatch-mediated social ecosystem. Our findings highlight the ice-breaking role of smartwatches in friendship initiation and their use for secret messaging with local peers. Within the online smartwatch community, peer status is determined by likes and visibility, leading to diverse pursuit activities (i.e., chu guanxi, jiazu, kuolie) and negative social dynamics. We discuss the core affordances of smartwatches and Chinese cultural factors that influence adolescent social behavior and offer implications for designing future wearables that responsibly and safely support adolescent socialization.

cs.HC↗

CEBin: A Cost-Effective Framework for Large-Scale Binary Code Similarity Detection

Binary code similarity detection (BCSD) is a fundamental technique for various application. Many BCSD solutions have been proposed recently, which mostly are embedding-based, but have shown limited accuracy and efficiency especially when the volume of target binaries to search is large. To address this issue, we propose a cost-effective BCSD framework, CEBin, which fuses embedding-based and comparison-based approaches to significantly improve accuracy while minimizing overheads. Specifically, CEBin utilizes a refined embedding-based approach to extract features of target code, which efficiently narrows down the scope of candidate similar code and boosts performance. Then, it utilizes a comparison-based approach that performs a pairwise comparison on the candidates to capture more nuanced and complex relationships, which greatly improves the accuracy of similarity detection. By bridging the gap between embedding-based and comparison-based approaches, CEBin is able to provide an effective and efficient solution for detecting similar code (including vulnerable ones) in large-scale software ecosystems. Experimental results on three well-known datasets demonstrate the superiority of CEBin over existing state-of-the-art (SOTA) baselines. To further evaluate the usefulness of BCSD in real world, we construct a large-scale benchmark of vulnerability, offering the first precise evaluation scheme to assess BCSD methods for the 1-day vulnerability detection task. CEBin could identify the similar function from millions of candidate functions in just a few seconds and achieves an impressive recall rate of $85.46\%$ on this more practical but challenging task, which are several order of magnitudes faster and $4.07\times$ better than the best SOTA baseline. Our code is available at https://github.com/Hustcw/CEBin.

cs.SE↗

CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision

Binary code representation learning has shown significant performance in binary analysis tasks. But existing solutions often have poor transferability, particularly in few-shot and zero-shot scenarios where few or no training samples are available for the tasks. To address this problem, we present CLAP (Contrastive Language-Assembly Pre-training), which employs natural language supervision to learn better representations of binary code (i.e., assembly code) and get better transferability. At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations (in natural language), resulting a model able to generate better embeddings for binary code. To enable this alignment training, we then propose an efficient dataset engine that could automatically generate a large and diverse dataset comprising of binary code and corresponding natural language explanations. We have generated 195 million pairs of binary code and explanations and trained a prototype of CLAP. The evaluations of CLAP across various downstream tasks in binary analysis all demonstrate exceptional performance. Notably, without any task-specific training, CLAP is often competitive with a fully supervised baseline, showing excellent transferability. We release our pre-trained model and code at https://github.com/Hustcw/CLAP.

cs.SE↗

Mathemyths: Leveraging Large Language Models to Teach Mathematical Language through Child-AI Co-Creative Storytelling

Mathematical language is a cornerstone of a child's mathematical development, and children can effectively acquire this language through storytelling with a knowledgeable and engaging partner. In this study, we leverage the recent advances in large language models to conduct free-form, creative conversations with children. Consequently, we developed Mathemyths, a joint storytelling agent that takes turns co-creating stories with children while integrating mathematical terms into the evolving narrative. This paper details our development process, illustrating how prompt-engineering can optimize LLMs for educational contexts. Through a user study involving 35 children aged 4-8 years, our results suggest that when children interacted with Mathemyths, their learning of mathematical language was comparable to those who co-created stories with a human partner. However, we observed differences in how children engaged with co-creation partners of different natures. Overall, we believe that LLM applications, like Mathemyths, offer children a unique conversational experience pertaining to focused learning objectives.

cs.HC↗

Variational learning algorithms for quantum query complexity

Quantum query complexity plays an important role in studying quantum algorithms, which captures the most known quantum algorithms, such as search and period finding. A query algorithm applies $U_tO_x\cdots U_1O_xU_0$ to some input state, where $O_x$ is the oracle dependent on some input variable $x$, and $U_i$s are unitary operations that are independent of $x$, followed by some measurements for readout. In this work, we develop variational learning algorithms to study quantum query complexity, by formulating $U_i$s as parameterized quantum circuits and introducing a loss function that is directly given by the error probability of the query algorithm. We apply our method to analyze various cases of quantum query complexity, including a new algorithm solving the Hamming modulo problem with $4$ queries for the case of $5$-bit modulo $5$, answering an open question raised in arXiv:2112.14682, and the result is further confirmed by a Semidefinite Programming (SDP) algorithm. Compared with the SDP algorithm, our method can be readily implemented on the near-term Noisy Intermediate-Scale Quantum (NISQ) devices and is more flexible to be adapted to other cases such as the fractional query models.

quant-ph↗

HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification

Hierarchical text classification (HTC) is a complex subtask under multi-label text classification, characterized by a hierarchical label taxonomy and data imbalance. The best-performing models aim to learn a static representation by combining document and hierarchical label information. However, the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation. To address this, we propose HiGen, a text-generation-based framework utilizing language models to encode dynamic text representations. We introduce a level-guided loss function to capture the relationship between text and label name semantics. Our approach incorporates a task-specific pretraining strategy, adapting the language model to in-domain knowledge and significantly enhancing performance for classes with limited examples. Furthermore, we present a new and valuable dataset called ENZYME, designed for HTC, which comprises articles from PubMed with the goal of predicting Enzyme Commission (EC) numbers. Through extensive experiments on the ENZYME dataset and the widely recognized WOS and NYT datasets, our methodology demonstrates superior performance, surpassing existing approaches while efficiently handling data and mitigating class imbalance. The data and code will be released publicly.

cs.CL↗

Reactor antineutrino flux and anomaly

Reactor antineutrinos have played a significant role in establishing the standard model of particle physics and the theory of neutrino oscillations. In this article, we review the reactor antineutrino flux and in particular the reactor antineutrino anomaly (RAA) coined over a decade ago. RAA refers to a deficit of the measured antineutrino inverse beta decay rates at very short-baseline reactor experiments compared to the theoretically improved predictions (i.e. the Huber-Mueller model). Since the resolution of several previous experimental anomalies have led to the discovery of non-zero neutrino mass and mixing, many efforts have been invested to study the origin of RAA both experimentally and theoretically. The progress includes the observation of discrepancies in antineutrino energy spectrum between data and the Huber-Mueller model, the re-evaluation of the Huber-Mueller model uncertainties, the potential isotope-dependent rate deficits, and the better agreement between data and new model predictions using the improved summation method. These developments disfavor the hypothesis of a light sterile neutrino as the explanation of RAA and supports the deficiencies of Huber-Mueller model as the origin. Looking forward, more effort from both the theoretical and experimental sides is needed to fully understand the root of RAA and to make accurate predictions of reactor antineutrino flux and energy spectrum for future discoveries.

hep-ph↗

New low-order mixed finite element methods for linear elasticity

New low-order $H(\textrm{div})$-conforming finite elements for symmetric tensors are constructed in arbitrary dimension. The space of shape functions is defined by enriching the symmetric quadratic polynomial space with the $(d+1)$-order normal-normal face bubble space. The reduced counterpart has only $d(d+1)^2$ degrees of freedom. Basis functions are explicitly given in terms of barycentric coordinates. Low-order conforming finite element elasticity complexes starting from the Bell element, are developed in two dimensions. These finite elements for symmetric tensors are applied to devise robust mixed finite element methods for the linear elasticity problem, which possess the uniform error estimates with respect to the Lamé coefficient $λ$, and superconvergence for the displacement. Numerical results are provided to verify the theoretical convergence rates.

math.NA↗

The Quasinormal Modes and Isospectrality of Bardeen (Anti-) de Sitter Black Holes

Black holes (BHs) exhibiting coordinate singularities but lacking essential singularities throughout the entire spacetime are referred to as regular black holes (RBHs). The initial formulation of RBHs was presented by Bardeen, who considered the Einstein equation coupled with a nonlinear electromagnetic field. In this study, we investigate the gravitational perturbations, including the axial and polar sectors, of the Bardeen (Anti-) de Sitter black holes. We derive the master equations with source terms for both axial and polar perturbations, and subsequently compute the quasinormal modes (QNMs) through numerical methods. For the Bardeen de Sitter black hole, we employ the 6th-order WKB approach. The numerical results reveal that the isospectrality is broken in this case. Conversely, for Bardeen Anti-de Sitter black holes, the QNM frequencies are calculated by using the HH method.

gr-qc↗

A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction

Large language models (LLMs) have demonstrated impressive abilities in generating unstructured natural language according to instructions. However, their performance can be inconsistent when tasked with producing text that adheres to specific structured formats, which is crucial in applications like named entity recognition (NER) or relation extraction (RE). To address this issue, this paper introduces an efficient method, G&O, to enhance their structured text generation capabilities. It breaks the generation into a two-step pipeline: initially, LLMs generate answers in natural language as intermediate responses. Subsequently, LLMs are asked to organize the output into the desired structure, using the intermediate responses as context. G&O effectively separates the generation of content from the structuring process, reducing the pressure of completing two orthogonal tasks simultaneously. Tested on zero-shot NER and RE, the results indicate a significant improvement in LLM performance with minimal additional efforts. This straightforward and adaptable prompting technique can also be combined with other strategies, like self-consistency, to further elevate LLM capabilities in various structured text generation tasks.

cs.CL↗

Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation

The subjective perception of emotion leads to inconsistent labels from human annotators. Typically, utterances lacking majority-agreed labels are excluded when training an emotion classifier, which cause problems when encountering ambiguous emotional expressions during testing. This paper investigates three methods to handle ambiguous emotion. First, we show that incorporating utterances without majority-agreed labels as an additional class in the classifier reduces the classification performance of the other emotion classes. Then, we propose detecting utterances with ambiguous emotions as out-of-domain samples by quantifying the uncertainty in emotion classification using evidential deep learning. This approach retains the classification accuracy while effectively detects ambiguous emotion expressions. Furthermore, to obtain fine-grained distinctions among ambiguous emotions, we propose representing emotion as a distribution instead of a single class label. The task is thus re-framed from classification to distribution estimation where every individual annotation is taken into account, not just the majority opinion. The evidential uncertainty measure is extended to quantify the uncertainty in emotion distribution estimation. Experimental results on the IEMOCAP and CREMA-D datasets demonstrate the superior capability of the proposed method in terms of majority class prediction, emotion distribution estimation, and uncertainty estimation.

cs.CL↗

A modified J model for efficiently calculating the electromagnetic fields of ReBCO no-insulation pancake coils using an explicit-implicit hybrid algorithm

Rare-earth (Re)Ba2Cu3O7-x (ReBCO) no-insulation (NI) coil is widely concerned due to its excellent electromagnetic and thermal properties. However, the presence of the turn-to-turn shunts in NI coils leads to that complexity of numerical simulation is increased. In this paper, a modified J model is proposed and the corresponding explicit-implicit hybrid algorithm is designed to calculate NI coils. The numerical results are in good agreement with the experimental data and the circuit model. The homogenization model is also proposed to simulate the large-scale NI coils in the background magnets. The modified J model has good accuracy and fast calculation speed, which can also be used to solve electromagnetic fields of insulation coils efficiently.

physics.app-ph↗

Orientation-Aware Leg Movement Learning for Action-Driven Human Motion Prediction

The task of action-driven human motion prediction aims to forecast future human motion based on the observed sequence while respecting the given action label. It requires modeling not only the stochasticity within human motion but the smooth yet realistic transition between multiple action labels. However, the fact that most datasets do not contain such transition data complicates this task. Existing work tackles this issue by learning a smoothness prior to simply promote smooth transitions, yet doing so can result in unnatural transitions especially when the history and predicted motions differ significantly in orientations. In this paper, we argue that valid human motion transitions should incorporate realistic leg movements to handle orientation changes, and cast it as an action-conditioned in-betweening (ACB) learning task to encourage transition naturalness. Because modeling all possible transitions is virtually unreasonable, our ACB is only performed on very few selected action classes with active gait motions, such as Walk or Run. Specifically, we follow a two-stage forecasting strategy by first employing the motion diffusion model to generate the target motion with a specified future action, and then producing the in-betweening to smoothly connect the observation and prediction to eventually address motion prediction. Our method is completely free from the labeled motion transition data during training. To show the robustness of our approach, we generalize our trained in-betweening learning model on one dataset to two unseen large-scale motion datasets to produce natural transitions. Extensive experimental evaluations on three benchmark datasets demonstrate that our method yields the state-of-the-art performance in terms of visual quality, prediction accuracy, and action faithfulness.

cs.CV↗

Formation of a streamer blob via the merger of multiple plasma clumps below 2Rs

Context. Propagating streamer blobs could be an important source of disturbances in the solar wind. Direct observations on formation of streamer blobs could be a proxy for understanding the formation of small-scale structures and disturbances in the solar wind. Aims. We aim to investigate how a streamer blob is formed before it is observed in the outer corona. Methods. Usingspecialcoordinated-observationsfromSOHO/LASCO,GOES/SUVIandSDO/AIA, we study the precursors of a streamer blob as seen in the corona below 2.0 solar radii (Rs). Results. We found that the streamer blob was formed due to the gradual merging of three clumps of brightenings initiated from the lower corona at about 1.8Rs, which is likely driven by expansion of the loop system at the base of the streamer. The acceleration of the blob starts from 1.9Rs or lower. It propagates along the south flank of the streamer where an expanding elongated brightening occurs coincidently. Conclusions. Our observations demonstrate that formation of a streamer blob is a complex process. We suggest that the expansion of the loop results in a pinching-off flux-rope-like blob at the loop apex below 2Rs. When the blob moves outward, it can be transferred across the overlying loops through interchange/component magnetic reconnection and then is released into the open field system. When the blob moves toward open field lines, interchange magnetic reconnections might also occur, and that can accelerate the plasma blob intermittently whilst allow it to transfer across the open field lines. Such dynamics in a streamer blob might further trigger small-scale disturbances in the solar wind such as switchbacks in the inner heliosphere.

astro-ph.SR↗

From Awareness to Action: Exploring End-User Empowerment Interventions for Dark Patterns in UX

The study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer's perspective on education and advocacy for ethical designs; and (2) the policymaker's perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users' needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users' awareness of and action on dark patterns being empowered in a realistic in-situ setting.

cs.HC↗

Thermocrystallization of lattice dipolar bosons coupled to a high-finesse cavity

Investigating finite temperature effects on quantum phases is key to their experimental realization. Finite temperature, and the interplay between quantum and thermal fluctuations can undermine properties and/or key features of quantum systems but they can also bring upon interesting phenomena. In this paper, we present a comprehensive investigation of the finite temperature phase diagram of two-dimensional lattice dipolar bosons coupled to a high-finesse optical cavity. Interestingly, we observe that checkerboard density-density correlations are enhanced at finite temperature. Indeed, we found that finite temperature drives a superfluid ground state into a normal state which will then develop checkerboard order at higher temperatures. We show that this effect is solely due to the cavity-mediated interactions. We also confirm that the supersolid checkerboard phase survives for a wide range of filling factors up to temperature scale of the order of half hopping amplitude, while the checkerboard diagonal order can survive up to temperatures of a few hopping amplitudes.

cond-mat.quant-gas↗