Search

Solving algebra problems (APs) continues to attract significant research interest as evidenced by the large number of algorithms and theories proposed over the past decade. Despite these important research contributions, however, the body of work remains incomplete in terms of theoretical justification and scope. The current contribution intends to fill the gap by developing a review framework that aims to lay a theoretical base, create an evaluation scheme, and extend the scope of the investigation. This paper first develops the State Transform Theory (STT), which emphasizes that the problem-solving algorithms are structured according to states and transforms unlike the understanding that underlies traditional surveys which merely emphasize the progress of transforms. The STT, thus, lays the theoretical basis for a new framework for reviewing algorithms. This new construct accommodates the relation-centric algorithms for solving both word and diagrammatic algebra problems. The latter not only highlights the necessity of introducing new states but also allows revelation of contributions of individual algorithms obscured in prior reviews without this approach., Comment: 22pages,5figures

Current techniques for detecting AI-generated text are largely confined to manual feature crafting and supervised binary classification paradigms. These methodologies typically lead to performance bottlenecks and unsatisfactory generalizability. Consequently, these methods are often inapplicable for out-of-distribution (OOD) data and newly emerged large language models (LLMs). In this paper, we revisit the task of AI-generated text detection. We argue that the key to accomplishing this task lies in distinguishing writing styles of different authors, rather than simply classifying the text into human-written or AI-generated text. To this end, we propose DeTeCtive, a multi-task auxiliary, multi-level contrastive learning framework. DeTeCtive is designed to facilitate the learning of distinct writing styles, combined with a dense information retrieval pipeline for AI-generated text detection. Our method is compatible with a range of text encoders. Extensive experiments demonstrate that our method enhances the ability of various text encoders in detecting AI-generated text across multiple benchmarks and achieves state-of-the-art results. Notably, in OOD zero-shot evaluation, our method outperforms existing approaches by a large margin. Moreover, we find our method boasts a Training-Free Incremental Adaptation (TFIA) capability towards OOD data, further enhancing its efficacy in OOD detection scenarios. We will open-source our code and models in hopes that our work will spark new thoughts in the field of AI-generated text detection, ensuring safe application of LLMs and enhancing compliance. Our code is available at https://github.com/heyongxin233/DeTeCtive., Comment: To appear in NeurIPS 2024. Code is available at https://github.com/heyongxin233/DeTeCtive

Significant efforts have been committed to understanding the fundamental combustion chemistry of ammonia at high-pressure and low-temperature conditions with or without blending with other fuels, as these are promising to improve ammonia combustion performance and reduce NOx emission. A commonly used fundamental reactor is the jet-stirred reactor (JSR). However, modeling of high-pressure JSR experiments have been conducted assuming complete ideal gas behaviors, which might lead to misinterpreted or completely wrong results. Therefore, this study proposes, for the first time, a novel framework coupling high-order Virial equation of state, ab initio multi-body intermolecular potential, and real-fluid governing equations. The framework is further applied to investigate NH$_3$ oxidation under supercritical conditions in jet-stirred reactors, where the real-fluid effects on NH$_3$ oxidation characteristics are quantified and compared, via simulated species profiles and relative changes in simulated mole fractions, at various temperatures, pressures, dilution ratios, equivalence ratios, and with or without blending with H$_2$ and CH$_4$. Strong promoting effects on NH$_3$ oxidation from real-fluid effects are revealed, with significant shifts in simulated species profiles observed for both fuel, intermediates and product species. Sensitivity analyses are also conducted based on the new framework, with diverse influences of real-fluid effects on the contributions of the most sensitive pathways highlighted. It is found that, without considering real-fluid behaviors, the error introduced in simulated species mole fractions can reach 85% at the conditions investigated in this study. Propagation of such levels of error to chemical kinetic mechanisms can disqualify them for any meaningful modeling work. These errors can now be excluded using the framework developed in this study.

In this letter, we demonstrate for the first time the creation of Josephson-like superconducting nanojunctions using a thermal scanning probe to directly inscribe weak links into microstrips of YBa2Cu3O7-x (YBCO). Our method effectively reduces the critical current (Ic) over an order of magnitude. The resulting nanobridges exhibit clear evidence of Josephson effects, of SNS-type junctions, as shown by both the DC and AC Josephson effects. This approach provides a novel and flexible method for scaling up quantum mechanical circuits that operate at liquid nitrogen temperatures. Additionally, it offers a promising pathway for modifying properties of the junctions in-situ and post fabrication., Comment: 14 pages, 4 figures

Task offloading is of paramount importance to efficiently orchestrate vehicular wireless networks, necessitating the availability of information regarding the current network status and computational resources. However, due to the mobility of the vehicles and the limited computational resources for performing task offloading in near-real-time, such schemes may require high latency, thus, become even infeasible. To address this issue, in this paper, we present a Trajectory Prediction-based Pre-offloading Decision (TPPD) algorithm for analyzing the historical trajectories of vehicles to predict their future coordinates, thereby allowing for computational resource allocation in advance. We first utilize the Long Short-Term Memory (LSTM) network model to predict each vehicle's movement trajectory. Then, based on the task requirements and the predicted trajectories, we devise a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) that enables the edge server to minimize task processing delay, while ensuring effective utilization of the available computational resources. Our simulation results verify the effectiveness of the proposed approach, showcasing that, as compared with traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization.

We present a comprehensive dataset of Java vulnerability-fixing commits (VFCs) to advance research in Java vulnerability analysis. Our dataset, derived from thousands of open-source Java projects on GitHub, comprises two variants: JavaVFC and JavaVFC-extended. The dataset was constructed through a rigorous process involving heuristic rules and multiple rounds of manual labeling. We initially used keywords to filter candidate VFCs based on commit messages, then refined this keyword set through iterative manual labeling. The final labeling round achieved a precision score of 0.7 among three annotators. We applied the refined keyword set to 34,321 open-source Java repositories with over 50 GitHub stars, resulting in JavaVFC with 784 manually verified VFCs and JavaVFC-extended with 16,837 automatically identified VFCs. Both variants are presented in a standardized JSONL format for easy access and analysis. This dataset supports various research endeavors, including VFC identification, fine-grained vulnerability detection, and automated vulnerability repair. The JavaVFC and JavaVFC-extended are publicly available at https://zenodo.org/records/13731781.

Age-related macular degeneration (AMD) is a major cause of blindness in older adults, severely affecting vision and quality of life. Despite advances in understanding AMD, the molecular factors driving the severity of subretinal scarring (fibrosis) remain elusive, hampering the development of effective therapies. This study introduces a machine learning-based framework to predict key genes that are strongly correlated with lesion severity and to identify potential therapeutic targets to prevent subretinal fibrosis in AMD. Using an original RNA sequencing (RNA-seq) dataset from the diseased retinas of JR5558 mice, we developed a novel and specific feature engineering technique, including pathway-based dimensionality reduction and gene-based feature expansion, to enhance prediction accuracy. Two iterative experiments were conducted by leveraging Ridge and ElasticNet regression models to assess biological relevance and gene impact. The results highlight the biological significance of several key genes and demonstrate the framework's effectiveness in identifying novel therapeutic targets. The key findings provide valuable insights for advancing drug discovery efforts and improving treatment strategies for AMD, with the potential to enhance patient outcomes by targeting the underlying genetic mechanisms of subretinal lesion development.

Supercritical oxidation processes in jet-stirred reactors (JSR) have been modeled based on ideal gas assumption. This can lead to significant errors in or complete misinterpretation of modeling results. Therefore, this study newly developed a framework to model supercritical oxidation in JSRs by incorporating ab initio multi-body molecular potentials and high-order mixture Virial equation of state (EoS) into real-fluid conservation laws, with the related numerical strategies highlighted. With comparisons with the simulation results based on ideal EoS and the experimental data from high-pressure JSR experiments, the framework is proved to be a step forward compared to the existing JSR modeling frameworks. To reveal the real-fluid effects on the oxidation characteristics in jet-stirred reactors, simulations are further conducted at a wide range of conditions (i.e., temperatures from 500 to 1100 K and pressures from 100 to 1000 bar), the real-fluid effect is found to significantly promote fuel oxidation reactivity, especially at low temperatures, high pressures, and for mixtures with heavy fuels. The significant influences of real-fluid behaviors on JSR oxidation characteristics emphasize the need to adequately incorporate these effects for future modeling studies in JSR at high pressures, which has now been enabled through the framework proposed in this study.

An adequate understanding of NOx interacting chemistry is a prerequisite for a smoother transition to carbon lean and carbon free fuels such as ammonia and hydrogen. In this regard, this study presents a comprehensive study on the H atom abstraction by NO2 from C3 to C7 alkynes and dienes forming 3 HNO2 isomers (i.e., TRANS HONO, HNO2, and CIS HONO), encompassing 8 hydrocarbons and 24 reactions. Through a combination of high level quantum chemistry computation, the rate coefficients for all studied reactions, over a temperature range from 298 to 2000 K, are computed based on Transition State Theory using the Master Equation System Solver program with considering unsymmetric tunneling corrections. Comprehensive analysis of branching ratios elucidates the diversity and similarities between different species, different HNO2 isomers, and different abstraction sites. Incorporating the calculated rate parameters into a recent chemistry model reveals the significant influences of this type of reaction on model performance, where the updated model is consistently more reactive for all the alkynes and dienes studied in predicting autoignition characteristics. Sensitivity and flux analyses are further conducted, through which the importance of H atom abstractions by NO2 is highlighted. With the updated rate parameters, the branching ratios in fuel consumption clearly shifts towards H atom abstractions by NO2 while away from H atom abstractions by OH. The obtained results emphasize the need for adequately representing these kinetics in new alkyne and diene chemistry models to be developed by using the rate parameters determined in this study, and call for future efforts to experimentally investigate NO2 blending effects on alkynes and dienes., Comment: 33 pages. arXiv admin note: text overlap with arXiv:2408.15023

In this article, we study the long-time dynamics of threshold solutions for the focusing energy-critical inhomogeneous Schr\"odinger equation and classify the corresponding threshold solutions in dimensions $d=3,4,5$. We first show the existence of special threshold solutions $W^\pm$ by constructing a sequence of approximate solutions in suitable Lorentz space, which exponentially approach the ground state $W$ in one of the time directions. We then prove that solutions with threshold energy either behave as in the subthreshold case or agree with $W, W^+$ or $W^-$ up to the symmetries of the equation. The proof relies on detailed spectral analysis of the linearized Schr\"odinger operator, the relevant modulation analysis, the global Virial analysis, and the concentration compactness argument in the Lorentz space.

Open-source software (OSS) has experienced a surge in popularity, attributed to its collaborative development model and cost-effective nature. However, the adoption of specific software versions in development projects may introduce security risks when these versions bring along vulnerabilities. Current methods of identifying vulnerable versions typically analyze and trace the code involved in vulnerability patches using static analysis with pre-defined rules. They then use syntactic-level code clone detection to identify the vulnerable versions. These methods are hindered by imprecisions due to (1) the inclusion of vulnerability-irrelevant code in the analysis and (2) the inadequacy of syntactic-level code clone detection. This paper presents Vercation, an approach designed to identify vulnerable versions of OSS written in C/C++. Vercation combines program slicing with a Large Language Model (LLM) to identify vulnerability-relevant code from vulnerability patches. It then backtraces historical commits to gather previous modifications of identified vulnerability-relevant code. We propose semantic-level code clone detection to compare the differences between pre-modification and post-modification code, thereby locating the vulnerability-introducing commit (vic) and enabling to identify the vulnerable versions between the patch commit and the vic. We curate a dataset linking 74 OSS vulnerabilities and 1013 versions to evaluate Vercation. On this dataset, our approach achieves the F1 score of 92.4%, outperforming current state-of-the-art methods. More importantly, Vercation detected 134 incorrect vulnerable OSS versions in NVD reports.

Software vulnerabilities pose significant security challenges and potential risks to society, necessitating extensive efforts in automated vulnerability detection. There are two popular lines of work to address automated vulnerability detection. On one hand, Static Application Security Testing (SAST) is usually utilized to scan source code for security vulnerabilities, especially in industries. On the other hand, deep learning (DL)-based methods, especially since the introduction of large language models (LLMs), have demonstrated their potential in software vulnerability detection. However, there is no comparative study between SAST tools and LLMs, aiming to determine their effectiveness in vulnerability detection, understand the pros and cons of both SAST and LLMs, and explore the potential combination of these two families of approaches. In this paper, we compared 15 diverse SAST tools with 12 popular or state-of-the-art open-source LLMs in detecting software vulnerabilities from repositories of three popular programming languages: Java, C, and Python. The experimental results showed that SAST tools obtain low vulnerability detection rates with relatively low false positives, while LLMs can detect up 90\% to 100\% of vulnerabilities but suffer from high false positives. By further ensembling the SAST tools and LLMs, the drawbacks of both SAST tools and LLMs can be mitigated to some extent. Our analysis sheds light on both the current progress and future directions for software vulnerability detection.

We present RodinHD, which can generate high-fidelity 3D avatars from a portrait image. Existing methods fail to capture intricate details such as hairstyles which we tackle in this paper. We first identify an overlooked problem of catastrophic forgetting that arises when fitting triplanes sequentially on many avatars, caused by the MLP decoder sharing scheme. To overcome this issue, we raise a novel data scheduling strategy and a weight consolidation regularization term, which improves the decoder's capability of rendering sharper details. Additionally, we optimize the guiding effect of the portrait image by computing a finer-grained hierarchical representation that captures rich 2D texture cues, and injecting them to the 3D diffusion model at multiple layers via cross-attention. When trained on 46K avatars with a noise schedule optimized for triplanes, the resulting model can generate 3D avatars with notably better details than previous methods and can generalize to in-the-wild portrait input., Comment: ECCV 2024; project page: https://rodinhd.github.io/

Gaze-based interactions offer a potential way for users to naturally engage with mixed reality (XR) interfaces. Black-box machine learning models enabled higher accuracy for gaze-based interactions. However, due to the black-box nature of the model, users might not be able to understand and effectively adapt their gaze behaviour to achieve high quality interaction. We posit that explainable AI (XAI) techniques can facilitate understanding of and interaction with gaze-based model-driven system in XR. To study this, we built a real-time, multi-level XAI interface for gaze-based interaction using a deep learning model, and evaluated it during a visual search task in XR. A between-subjects study revealed that participants who interacted with XAI made more accurate selections compared to those who did not use the XAI system (i.e., F1 score increase of 10.8%). Additionally, participants who used the XAI system adapted their gaze behavior over time to make more effective selections. These findings suggest that XAI can potentially be used to assist users in more effective collaboration with model-driven interactions in XR.

Characterization of quantum states is a fundamental requirement in quantum science and technology. As a promising framework, shadow tomography shows significant efficiency in estimating linear functions, however, for the challenging nonlinear ones, it requires measurements at an exponential cost. Here, we implement an advanced shadow protocol, so-called hybrid shadow~(HS) tomography, to reduce the measurement cost in the estimation of nonlinear functions in an optical system. We design and realize a deterministic quantum Fredkin gate with single photon, achieving high process fidelity of $0.935\pm0.001$. Utilizing this novel Fredkin gate, we demonstrate HS in the estimations, like the higher-order moments up to 4, and reveal that the sample complexity of HS is significantly reduced compared with the original shadow protocol. Furthermore, we utilize these higher-degree functions to implement virtual distillation, which effectively extracts a high-purity quantum state from two noisy copies. The virtual distillation is also verified in a proof-of-principle demonstration of quantum metrology, further enhancing the accuracy of parameter estimation. Our results suggest that HS is efficient in state characterization and promising for quantum technologies., Comment: 14 pages, 10 figures

Recent years have seen the remarkable capabilities of large language models (LLMs) for code generation. Different from existing work that evaluate the correctness of the code generated by LLMs, we propose to further evaluate its efficiency. More efficient code can lead to higher performance and execution efficiency of programs and software completed by LLM-assisted programming. First, we evaluate the efficiency of the code generated by LLMs on two benchmarks, HumanEval and MBPP. Then, we choose a set of programming problems from the online judge platform LeetCode to conduct a more difficult evaluation. Finally, we explore several prompts that would enable LLMs to generate more efficient code., Comment: 1st special event of AI Foundation Models and Software Engineering (FORGE 2024)

The combination of metallicity and ferroelectricity breaks traditional boundaries, paving new avenues for innovative electronic materials and devices. This breakthrough is particularly notable, as metallicity and ferroelectricity have traditionally been considered mutually exclusive physical properties. In this work, starting with non-polar metallic single layers, we propose a general scheme for designing 2D ferroelectric metals (FEMs) based on van der Waals interaction. By first-principles calculations, we further substantiate the feasibility of the design scheme in real materials such as FeSe and H-MnTe2. Notably, this scheme unveils unique metallic ferroelectricity, characterized by reversing polarization through interlayer sliding. Furthermore, the combination of inherent magnetism with sliding ferroelectricity leads to multiferroicity. The investigated design scheme and observed phenomena have broad applicability across 2D materials. Our results not only pave the way for research in 2D FEMs but also offer promising prospects for foundational studies of coupled physical phenomena in 2D lattices.

The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i.e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials. One of the reasons is the inadequacy of Cross-drug Response Evaluation (CRE) during the late stages of drug R&D. Although in-silico CRE models bring a promising solution, existing methodologies are restricted to early stages of drug R&D, such as target and cell-line levels, offering limited improvement to clinical success rates. Herein, we introduce DeepCRE, a pioneering AI model designed to predict CRE effectively in the late stages of drug R&D. DeepCRE outperforms the existing best models by achieving an average performance improvement of 17.7% in patient-level CRE, and a 5-fold increase in indication-level CRE, facilitating more accurate personalized treatment predictions and better pharmaceutical value assessment for indications, respectively. Furthermore, DeepCRE has identified a set of six drug candidates that show significantly greater effectiveness than a comparator set of two approved drugs in 5/8 colorectal cancer organoids. This demonstrates the capability of DeepCRE to systematically uncover a spectrum of drug candidates with enhanced therapeutic effects, highlighting its potential to transform drug R&D.

In trapped ion system, accurate thermometry of ion is crucial for evaluating the system state and precisely performing quantum operations. However, when the motional state of a single ion is far away from the ground state, the spatial dimension of the phonon state sharply increases, making it difficult to realize accurate and mode-resolved thermometry with existing methods. In this work, we apply deep learning for the first time to the thermometry of trapped ion, providing an efficient and mode-resolved method for accurately estimating large mean phonon numbers. Our trained neural network model can be directly applied to other experimental setups without retraining or post-processing, as long as the related parameters are covered by the model's effective range, and it can also be conveniently extended to other parameter ranges. We have conducted experimental verification based on our surface trap, of which the result has shown the accuracy and efficiency of the method for thermometry of single ion under large mean phonon number, and its mode resolution characteristic can make it better applied to the characterization of system parameters, such as evaluating cooling effectiveness, analyzing surface trap noise.

Previous learning-based vulnerability detection methods relied on either medium-sized pre-trained models or smaller neural networks from scratch. Recent advancements in Large Pre-Trained Language Models (LLMs) have showcased remarkable few-shot learning capabilities in various tasks. However, the effectiveness of LLMs in detecting software vulnerabilities is largely unexplored. This paper aims to bridge this gap by exploring how LLMs perform with various prompts, particularly focusing on two state-of-the-art LLMs: GPT-3.5 and GPT-4. Our experimental results showed that GPT-3.5 achieves competitive performance with the prior state-of-the-art vulnerability detection approach and GPT-4 consistently outperformed the state-of-the-art., Comment: Accepted in the New Ideas and Emerging Results Track at ICSE 2024

For non-simple laced Lie algebras, the $\text{B}_{N}$ and $\text{C}_{N}$ are Langlands dual to each other in mathematical. In this article, we give another Bethe/Gauge correspondence between 3d (or 2d) classical Lie group supersymmetry gauge theory with closed and open $\text{XXZ}$ (or $\text{XXX}$) spin chain. Here, the representations of the $\text{ADE}$ Lie algebras are self-dual, and while for the non-simple laced Lie algebras $\text{B}_{N}$ and $\text{C}_{N}$, their roles are exchanged in contrast with the results in \cite{DZ23a}. From Bethe/Gauge correspondence point of view, the two types of the effective superpotentials are Langlands duality to each other. For the $\text{B}_{N}$-type Lie algebra, a remarkable feature is that, to fix the spin sites by boundaries through Bethe/Gauge, the spins of the sites will be reversed. This is similarly to the so called electron-hole effect, we call this as a boundary-spin effect, a new kind of duality., Comment: 25 pages, one figure

This paper introduces a pioneering 3D volumetric encoder designed for text-to-3D generation. To scale up the training data for the diffusion model, a lightweight network is developed to efficiently acquire feature volumes from multi-view images. The 3D volumes are then trained on a diffusion model for text-to-3D generation using a 3D U-Net. This research further addresses the challenges of inaccurate object captions and high-dimensional feature volumes. The proposed model, trained on the public Objaverse dataset, demonstrates promising outcomes in producing diverse and recognizable samples from text prompts. Notably, it empowers finer control over object part characteristics through textual cues, fostering model creativity by seamlessly combining multiple concepts within a single object. This research significantly contributes to the progress of 3D generation by introducing an efficient, flexible, and scalable representation methodology.

In this paper, we present a novel two-stage approach that fully utilizes the information provided by the reference image to establish a customized knowledge prior for image-to-3D generation. While previous approaches primarily rely on a general diffusion prior, which struggles to yield consistent results with the reference image, we propose a subject-specific and multi-modal diffusion model. This model not only aids NeRF optimization by considering the shading mode for improved geometry but also enhances texture from the coarse results to achieve superior refinement. Both aspects contribute to faithfully aligning the 3D content with the subject. Extensive experiments showcase the superiority of our method, Customize-It-3D, outperforming previous works by a substantial margin. It produces faithful 360-degree reconstructions with impressive visual quality, making it well-suited for various applications, including text-to-3D creation., Comment: Project Page: https://nnanhuang.github.io/projects/customize-it-3d/