arXiv subjects

Hui Wang

Publications and source records attributed to Hui Wang.

At least 253 records · Page 14Linked to original sources

arXiv2024

Addressing bias in Recommender Systems: A Case Study on Data Debiasing Techniques in Mobile Games

The mobile gaming industry, particularly the free-to-play sector, has been around for more than a decade, yet it still experiences rapid growth. The concept of games-as-service requires game developers to pay much more attention to recommendations of content in their games. With recommender systems (RS), the inevitable problem of bias in the data comes hand in hand. A lot of research has been done on the case of bias in RS for online retail or services, but much less is available for the specific case of the game industry. Also, in previous works, various debiasing techniques were tested on explicit feedback datasets, while it is much more common in mobile gaming data to only have implicit feedback. This case study aims to identify and categorize potential bias within datasets specific to model-based recommendations in mobile games, review debiasing techniques in the existing literature, and assess their effectiveness on real-world data gathered through implicit feedback. The effectiveness of these methods is then evaluated based on their debiasing quality, data requirements, and computational demands.

cs.LG↗

arXiv2024

Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation

Large Language Models (LLMs) have demonstrated remarkable success across a wide range of tasks and domains. However, their performance in low-resource language translation, particularly when translating into these languages, remains underexplored. This gap poses significant challenges, as linguistic barriers hinder the cultural preservation and development of minority communities. To address this issue, this paper introduces a novel retrieval-based method that enhances translation quality for low-resource languages by focusing on key terms, which involves translating keywords and retrieving corresponding examples from existing data. To evaluate the effectiveness of this method, we conducted experiments translating from English into three low-resource languages: Cherokee, a critically endangered indigenous language of North America; Tibetan, a historically and culturally significant language in Asia; and Manchu, a language with few remaining speakers. Our comparison with the zero-shot performance of GPT-4o and LLaMA 3.1 405B, highlights the significant challenges these models face when translating into low-resource languages. In contrast, our retrieval-based method shows promise in improving both word-level accuracy and overall semantic understanding by leveraging existing resources more effectively.

cs.CL↗

arXiv2024

Piezovalley effect and magnetovalley coupling in altermagnetic semiconductors

Clarifying the physical origin of valley polarization and exploring promising ferrovalley materials are conducive to the application of valley degrees of freedom in the field of information storage. Here, we explore two novel altermagnetic semiconductors (monolayers Nb2Se2O and Nb2SeTeO) with Néel temperature above room temperature based on first-principles calculations. It reveals that uniaxial strain induces valley polarization without spin-orbital coupling (SOC) in altermagnets owing to the piezovalley effect, while uniaxial compressive strain transforms the intrinsic ferrovalley semiconductor into a semimetal, half metal and metal. Moreover, moderate biaxial strain renders Janus monolayer Nb2SeTeO to robust Dirac-like band dispersion. The SOC and intrinsic in-plane magnetocrystalline anisotropy energy induce Dirac-like altermagnets to generate apparent valley polarization through magnetovalley coupling. In terms of SOC perturbation, we elucidate the physical mechanism behind in-plane-magnetization induced valley polarization and demonstrate the magnitude of valley polarization is positively correlated with the square of SOC strength and negatively correlated with the bandgap. The present work reveals the physical origin of valley polarization in altermagnets and expands the application of ferrovalley at room temperature in valleytronics.

cond-mat.mtrl-sci↗

arXiv2024

Medium recoil mode of $Δ$ production in single isobaric charge-exchange reactions

The dynamic mechanisms underlying single charge-exchange reactions have been investigated using a theoretical framework that combines the Isospin-dependent Quantum Molecular Dynamics (IQMD) model with the statistical decay model GEMINI++. Two distinct channels contribute to the single isobaric charge-exchange reaction: quasi-elastic channel, where neutron-proton scattering drives the charge-exchange, and inelastic channel, where the $Δ$ particle is produced during the process. In a referenced study [Phys.RevC 106.014618(2022)], experimental data have revealed that the inelastic channel accounts for approximately 50 percent of the single isobaric charge-exchange reaction. However, our current model fails in reproducing the significant contribution of inelastic channel unless the novel medium recoil mode associated with $Δ$ production is considered in the calculations. Notably, this in-medium effect arising from inelastic nucleon-nucleon collisions is not yet incorporated into mainstream microscopic transport models. The dynamical properties of protons and pions emitting in the single isobaric charge-exchange reactions are predicted. This exploration of in-medium effects adds a valuable dimension to our understanding of the intricate dynamics involved in single charge-exchange reactions.

nucl-th↗

arXiv2024

Efficient charging of multiple open quantum batteries through dissipation and pumping

We explore a protocol that efficiently charges multiple open quantum batteries in parallel using a single charger. This protocol shows super-extensive charging through collective coupling of the charger and the battery to the same thermal reservoir. When applied to multiple quantum batteries, each coupled to different thermal reservoirs, the energy cannot be efficiently transferred from the charger to the battery via collective dissipation alone. We show that the counter-intuitive act of incorporating both dissipation and incoherent collective pumping on the charger enables efficient parallel charging of many quantum batteries.

quant-ph↗

arXiv2024

Relay Decoding: Concatenating Large Language Models for Machine Translation

Leveraging large language models for machine translation has demonstrated promising results. However, it does require the large language models to possess the capability of handling both the source and target languages in machine translation. When it is challenging to find large models that support the desired languages, resorting to continuous learning methods becomes a costly endeavor. To mitigate these expenses, we propose an innovative approach called RD (Relay Decoding), which entails concatenating two distinct large models that individually support the source and target languages. By incorporating a simple mapping layer to facilitate the connection between these two models and utilizing a limited amount of parallel data for training, we successfully achieve superior results in the machine translation task. Experimental results conducted on the Multi30k and WikiMatrix datasets validate the effectiveness of our proposed method.

cs.CL↗

arXiv2024

MeloTrans: A Text to Symbolic Music Generation Model Following Human Composition Habit

At present, neural network models show powerful sequence prediction ability and are used in many automatic composition models. In comparison, the way humans compose music is very different from it. Composers usually start by creating musical motifs and then develop them into music through a series of rules. This process ensures that the music has a specific structure and changing pattern. However, it is difficult for neural network models to learn these composition rules from training data, which results in a lack of musicality and diversity in the generated music. This paper posits that integrating the learning capabilities of neural networks with human-derived knowledge may lead to better results. To archive this, we develop the POP909$\_$M dataset, the first to include labels for musical motifs and their variants, providing a basis for mimicking human compositional habits. Building on this, we propose MeloTrans, a text-to-music composition model that employs principles of motif development rules. Our experiments demonstrate that MeloTrans excels beyond existing music generation models and even surpasses Large Language Models (LLMs) like ChatGPT-4. This highlights the importance of merging human insights with neural network capabilities to achieve superior symbolic music generation.

cs.SD↗

arXiv2024

Chip-Tuning: Classify Before Language Models Say

The rapid development in the performance of large language models (LLMs) is accompanied by the escalation of model size, leading to the increasing cost of model training and inference. Previous research has discovered that certain layers in LLMs exhibit redundancy, and removing these layers brings only marginal loss in model performance. In this paper, we adopt the probing technique to explain the layer redundancy in LLMs and demonstrate that language models can be effectively pruned with probing classifiers. We propose chip-tuning, a simple and effective structured pruning framework specialized for classification problems. Chip-tuning attaches tiny probing classifiers named chips to different layers of LLMs, and trains chips with the backbone model frozen. After selecting a chip for classification, all layers subsequent to the attached layer could be removed with marginal performance loss. Experimental results on various LLMs and datasets demonstrate that chip-tuning significantly outperforms previous state-of-the-art baselines in both accuracy and pruning ratio, achieving a pruning ratio of up to 50%. We also find that chip-tuning could be applied on multimodal models, and could be combined with model finetuning, proving its excellent compatibility.

cs.CL↗

arXiv2024

A Systematic Assessment of OpenAI o1-Preview for Higher Order Thinking in Education

As artificial intelligence (AI) continues to advance, it demonstrates capabilities comparable to human intelligence, with significant potential to transform education and workforce development. This study evaluates OpenAI o1-preview's ability to perform higher-order cognitive tasks across 14 dimensions, including critical thinking, systems thinking, computational thinking, design thinking, metacognition, data literacy, creative thinking, abstract reasoning, quantitative reasoning, logical reasoning, analogical reasoning, and scientific reasoning. We used validated instruments like the Ennis-Weir Critical Thinking Essay Test and the Biological Systems Thinking Test to compare the o1-preview's performance with human performance systematically. Our findings reveal that o1-preview outperforms humans in most categories, achieving 150% better results in systems thinking, computational thinking, data literacy, creative thinking, scientific reasoning, and abstract reasoning. However, compared to humans, it underperforms by around 25% in logical reasoning, critical thinking, and quantitative reasoning. In analogical reasoning, both o1-preview and humans achieved perfect scores. Despite these strengths, the o1-preview shows limitations in abstract reasoning, where human psychology students outperform it, highlighting the continued importance of human oversight in tasks requiring high-level abstraction. These results have significant educational implications, suggesting a shift toward developing human skills that complement AI, such as creativity, abstract reasoning, and critical thinking. This study emphasizes the transformative potential of AI in education and calls for a recalibration of educational goals, teaching methods, and curricula to align with an AI-driven world.

cs.CY↗

arXiv2024

MetaTool: Facilitating Large Language Models to Master Tools with Meta-task Augmentation

Utilizing tools with Large Language Models (LLMs) is essential for grounding AI agents in real-world applications. The prevailing approach involves few-shot prompting with demonstrations or fine-tuning with expert annotations. However, mere in-context demonstrations may fail to cover sufficient knowledge for complex tools and tasks. Training on solution paths is also hindered by the high cost of expert annotations and generalizing to new tools. A core challenge of generalizable tool use lies in understanding the "meta", or fundamental natures of tools that are transferable across tasks, such as causality and constraints. In this paper, we present MetaTool, a novel tool learning methodology designed to generalize across any reusable toolset. Our approach incorporates a self-supervised augmentation technique derived from a series of meta-tasks. This involves predicting masked elements in the tool execution process. The self-supervised procedure enables scalable generation of high-quality QA data, which is handy for supervising tool understanding. By incorporating meta-task data into task-oriented training, our method significantly enhances the performance of open-source LLMs, achieving results comparable to ChatGPT in both tool-based planning and chatting scenarios. Through large-scale instruction tuning, the MetaTool model demonstrates impressive zero-shot generalizability on new tasks.

cs.CL↗

arXiv2024

On an unconditional spectral analog of Selberg's result on $S(t)$

Let $S_j(t)=\frac{1}π\arg L(1/2+it, u_j)$, where $u_j$ is an even Hecke--Maass cusp form for $\rm SL_2(\mathbb{Z})$ with Laplacian eigenvalue $λ_j=\frac{1}{4}+t_j^2$. Without assuming the GRH, we establish an asymptotic formula for the moments of $S_j(t)$.

math.NT↗

arXiv2024

Nonreciprocal Superradiant Phase Transitions and Multicriticality in a Cavity QED System

We demonstrate the emergence of nonreciprocal superradiant phase transitions and novel multicriticality in a cavity quantum electrodynamics (QED) system, where a two-level atom interacts with two counter-propagating modes of a whispering-gallery-mode (WGM) microcavity. The cavity rotates at a certain angular velocity, and is directionally squeezed by a unidirectional parametric pumping $χ^{(2)}$ nonlinearity. The combination of cavity rotation and directional squeezing leads to nonreciprocal first- and second-order superradiant phase transitions. These transitions do not require ultrastrong atom-field couplings and can be easily controlled by the external pump field. Through a full quantum description of the system Hamiltonian, we identify two types of multicritical points in the phase diagram, both of which exhibit controllable nonreciprocity. These results open a new door for all-optical manipulation of superradiant transitions and multicritical behaviors in light-matter systems, with potential applications in engineering various integrated nonreciprocal quantum devices

quant-ph↗

arXiv2024

IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation Scenarios

In Retrieval-Augmented Generation (RAG) tasks using Large Language Models (LLMs), the quality of retrieved information is critical to the final output. This paper introduces the IRSC benchmark for evaluating the performance of embedding models in multilingual RAG tasks. The benchmark encompasses five retrieval tasks: query retrieval, title retrieval, part-of-paragraph retrieval, keyword retrieval, and summary retrieval. Our research addresses the current lack of comprehensive testing and effective comparison methods for embedding models in RAG scenarios. We introduced new metrics: the Similarity of Semantic Comprehension Index (SSCI) and the Retrieval Capability Contest Index (RCCI), and evaluated models such as Snowflake-Arctic, BGE, GTE, and M3E. Our contributions include: 1) the IRSC benchmark, 2) the SSCI and RCCI metrics, and 3) insights into the cross-lingual limitations of embedding models. The IRSC benchmark aims to enhance the understanding and development of accurate retrieval systems in RAG tasks. All code and datasets are available at: https://github.com/Jasaxion/IRSC_Benchmark

cs.IR↗

arXiv2024

Quantum heat engine based on quantum interferometry: the SU(1,1) Otto cycle

We present a quantum heat engine based on a quantum Otto cycle, whose working substance reproduces the same outcomes of a SU(1,1) interference process at the end of each adiabatic transformation. This device takes advantage of the extraordinary quantum metrological features of the SU(1,1) interferometer to better discriminate the sources of uncertainty of relevant observables during each adiabatic stroke of the cycle. Applications to circuit QED platforms are also discussed.

quant-ph↗

arXiv2024

Rosen-Morse potential and gravitating kinks

We show that in a special type of two-dimensional dilaton-gravity-scalar model, where both the dilaton and the scalar matter fields have noncanonical kinetic terms, it is possible to construct kink solutions whose linear perturbation equation is a Schrödinger-like equation with Rosen-Morse potential. For this potential, eigenvalues and wave functions of the bound states, if had any, can be derived by using the standard shape invariance procedure. Depending on the values of the parameters, the stability potential can be reflective or reflectionless. There can be an arbitrary number of shape modes, but the zero mode is always absent.

hep-th↗

arXiv2024

MESC: Re-thinking Algorithmic Priority and/or Criticality Inversions for Heterogeneous MCSs

Modern Mixed-Criticality Systems (MCSs) rely on hardware heterogeneity to satisfy ever-increasing computational demands. However, most of the heterogeneous co-processors are designed to achieve high throughput, with their micro-architectures executing the workloads in a streaming manner. This streaming execution is often non-preemptive or limited-preemptive, preventing tasks' prioritisation based on their importance and resulting in frequent occurrences of algorithmic priority and/or criticality inversions. Such problems present a significant barrier to guaranteeing the systems' real-time predictability, especially when co-processors dominate the execution of the workloads (e.g., DNNs and transformers). In contrast to existing works that typically enable coarse-grained context switch by splitting the workloads/algorithms, we demonstrate a method that provides fine-grained context switch on a widely used open-source DNN accelerator by enabling instruction-level preemption without any workloads/algorithms modifications. As a systematic solution, we build a real system, i.e., Make Each Switch Count (MESC), from the SoC and ISA to the OS kernel. A theoretical model and analysis are also provided for timing guarantees. Experimental results reveal that, compared to conventional MCSs using non-preemptive DNN accelerators, MESC achieved a 250x and 300x speedup in resolving algorithmic priority and criticality inversions, with less than 5\% overhead. To our knowledge, this is the first work investigating algorithmic priority and criticality inversions for MCSs at the instruction level.

cs.AR↗

arXiv2024

Trustworthy Hate Speech Detection Through Visual Augmentation

The surge of hate speech on social media platforms poses a significant challenge, with hate speech detection~(HSD) becoming increasingly critical. Current HSD methods focus on enriching contextual information to enhance detection performance, but they overlook the inherent uncertainty of hate speech. We propose a novel HSD method, named trustworthy hate speech detection method through visual augmentation (TrusV-HSD), which enhances semantic information through integration with diffused visual images and mitigates uncertainty with trustworthy loss. TrusV-HSD learns semantic representations by effectively extracting trustworthy information through multi-modal connections without paired data. Our experiments on public HSD datasets demonstrate the effectiveness of TrusV-HSD, showing remarkable improvements over conventional methods.

cs.CV↗

arXiv2024

The THESAN project: galaxy sizes during the epoch of reionization

We investigate galaxy sizes at redshift $z\gtrsim 6$ with the cosmological radiation-magneto-hydrodynamic simulation suite THESAN(-HR). These simulations simultaneously capture the reionization of the large-scale intergalactic medium and resolved galaxy properties. The intrinsic size ($r^{\ast}_{1/2}$) of simulated galaxies increases moderately with stellar mass at $M_{\ast} \lesssim 10^{8}\,{\rm M}_{\odot}$ and decreases fast at larger masses, resulting in a hump feature at $M_{\ast}\sim 10^{8}\,{\rm M}_{\odot}$ that is insensitive to redshift. Low-mass galaxies are in the initial phase of size growth and are better described by a spherical shell model with feedback-driven gas outflows competing with the cold inflows. In contrast, massive galaxies fit better with the disk formation model. They generally experience a phase of rapid compaction and gas depletion, likely driven by internal disk instability rather than external processes. We identify four compact quenched galaxies in the $(95.5\,{\rm cMpc})^{3}$ volume of THESAN-1 at $z\simeq 6$, and their quenching follows reaching a characteristic stellar surface density akin to the massive compact galaxies at cosmic noon. Compared to observations, we find that the median UV effective radius ($R^{\rm UV}_{\rm eff}$) of simulated galaxies is at least three times larger than the observed ones at $M_{\ast}\lesssim 10^{9}\,{\rm M}_{\odot}$ or $M_{\rm UV}\gtrsim -20$ at $6 \lesssim z \lesssim 10$. This inconsistency, related to the hump feature of the intrinsic size--mass relation, persists across many other cosmological simulations with different galaxy formation models and demonstrates the potential of using galaxy morphology to constrain the physics of galaxy formation at high redshifts.

astro-ph.GA↗