Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Integration Strategies of Retriever and Large Language Models

The integration of retrieved passages and large language models (LLMs), such as ChatGPTs, has significantly contributed to improving open-domain question answering. However, there is still a lack of exploration regarding the optimal approach for incorporating retrieved passages into the answer generation process. This paper aims to fill this gap by investigating different methods of combining retrieved passages with LLMs to enhance answer generation. We begin by examining the limitations of a commonly-used concatenation approach. Surprisingly, this approach often results in generating"unknown"outputs, even when the correct document is among the top-k retrieved passages. To address this issue, we explore four alternative strategies for integrating the retrieved passages with the LLMs. These strategies include two single-round methods that utilize chain-of-thought reasoning and two multi-round strategies that incorporate feedback loops. Through comprehensive analyses and experiments, we provide insightful observations on how to effectively leverage retrieved passages to enhance the answer generation capability of LLMs.
Prompt-Enhanced Software Vulnerability Detection Using ChatGPT

With the increase in software vulnerabilities that cause significant economic and social losses, automatic vulnerability detection has become essential in software development and maintenance. Recently, large language models (LLMs) like GPT have received considerable attention due to their stunning intelligence, and some studies consider using ChatGPT for vulnerability detection. However, they do not fully consider the characteristics of LLMs, since their designed questions to ChatGPT are simple without a specific prompt design tailored for vulnerability detection. This paper launches a study on the performance of software vulnerability detection using ChatGPT with different prompt designs. Firstly, we complement previous work by applying various improvements to the basic prompt. Moreover, we incorporate structural and sequential auxiliary information to improve the prompt design. Besides, we leverage ChatGPT's ability of memorizing multi-round dialogue to design suitable prompts for vulnerability detection. We conduct extensive experiments on two vulnerability datasets to demonstrate the effectiveness of prompt-enhanced vulnerability detection using ChatGPT. We also analyze the merit and demerit of using ChatGPT for vulnerability detection.
Spatial and Spatiotemporal Data Mining: Recent Advances

Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional data mining techniques for extracting spatial patterns. In this chapter we explore the emerging field of spatial data mining, focusing on four major topics: prediction and classification, outlier detection, co-location mining, and clustering. Spatiotemporal data mining is also briefly discussed.
Quantized distributed Nash equilibrium seeking under DoS attacks: A quantized consensus based approach

This paper studies distributed Nash equilibrium (NE) seeking under Denial-of-Service (DoS) attacks and quantization. The players can only exchange information with their own direct neighbors. The transmitted information is subject to quantization and packet losses induced by malicious DoS attacks. We propose a quantized distributed NE seeking strategy based on the approach of dynamic quantized consensus. To solve the quantizer saturation problem caused by DoS attacks, the quantization mechanism is equipped to have zooming-in and holding capabilities, in which the holding capability is consistent with the results in quantized consensus under DoS. A sufficient condition on the number of quantizer levels is provided, under which the quantizers are free from saturation under DoS attacks. The proposed distributed quantized NE seeking strategy is shown to have the so-called maximum resilience to DoS attacks. Namely, if the bound characterizing the maximum resilience is violated, an attacker can deny all the transmissions and hence distributed NE seeking is impossible.
A Distributed Linear Quadratic Discrete-Time Game Approach to Formation Control with Collision Avoidance

Formation control problems can be expressed as linear quadratic discrete-time games (LQDTG) for which Nash equilibrium solutions are sought. However, solving such problems requires solving coupled Riccati equations, which cannot be done in a distributed manner. A recent study showed that a distributed implementation is possible for a consensus problem when fictitious agents are associated with edges in the network graph rather than nodes. This paper proposes an extension of this approach to formation control with collision avoidance, where collision is precluded by including appropriate penalty terms on the edges. To address the problem, a state-dependent Riccati equation needs to be solved since the collision avoidance term in the cost function leads to a state-dependent weight matrix. This solution provides relative control inputs associated with the edges of the network graph. These relative inputs then need to be mapped to the physical control inputs applied at the nodes; this can be done in a distributed manner by iterating over a gradient descent search between neighbors in each sampling interval. Unlike inter-sample iteration frequently used in distributed MPC, only a matrix-vector multiplication is needed for each iteration step here, instead of an optimization problem to be solved. This approach can be implemented in a receding horizon manner, this is demonstrated through a numerical example.
Aperiodicity, Star-freeness, and First-order Definability of Structured Context-Free Languages

A classic result in formal language theory is the equivalence among noncounting, or aperiodic, regular languages, and languages defined through star-free regular expressions, or first-order logic. Together with first-order completeness of linear temporal logic these results constitute a theoretical foundation for model-checking algorithms. Extending these results to structured subclasses of context-free languages, such as tree-languages did not work as smoothly: for instance W. Thomas showed that there are star-free tree languages that are counting. We show, instead, that investigating the same properties within the family of operator precedence languages leads to equivalences that perfectly match those on regular languages. The study of this old family of context-free languages has been recently resumed to enhance not only parsing (the original motivation of its inventor R. Floyd) but also to exploit their algebraic and logic properties. We have been able to reproduce the classic results of regular languages for this much larger class by going back to string languages rather than tree languages. Since operator precedence languages strictly include other classes of structured languages such as visibly pushdown languages, the same results given in this paper hold as trivial corollary for that family too.
Leverage Classifier: Another Look at Support Vector Machine

Support vector machine (SVM) is a popular classifier known for accuracy, flexibility, and robustness. However, its intensive computation has hindered its application to large-scale datasets. In this paper, we propose a new optimal leverage classifier based on linear SVM under a nonseparable setting. Our classifier aims to select an informative subset of the training sample to reduce data size, enabling efficient computation while maintaining high accuracy. We take a novel view of SVM under the general subsampling framework and rigorously investigate the statistical properties. We propose a two-step subsampling procedure consisting of a pilot estimation of the optimal subsampling probabilities and a subsampling step to construct the classifier. We develop a new Bahadur representation of the SVM coefficients and derive unconditional asymptotic distribution and optimal subsampling probabilities without giving the full sample. Numerical results demonstrate that our classifiers outperform the existing methods in terms of estimation, computation, and prediction.
Nuclear physics constraints from binary neutron star mergers in the Einstein Telescope era

The next generation of ground-based gravitational-wave detectors, Einstein Telescope (ET) and Cosmic Explorer (CE), present a unique opportunity to put constraints on dense matter, among many other groundbreaking scientific goals. In a recent study the science case of ET was further strengthened, studying in particular the performances of different detector designs. In this paper we present a more detailed study of the nuclear physics section of that work. In particular, focusing on two different detector configurations (the single-site triangular-shaped design and a design consisting of two widely separated"L-shaped"interferometers), we study the detection prospects of binary neutron star (BNS) mergers, and how they can reshape our understanding of the underlying equation of state (EoS) of dense matter. We employ several state-of-the-art EoS models and state-of-the-art synthetic BNS merger catalogs, and we make use of the Fisher information formalism (FIM) to quantify statistical errors on the astrophysical parameters describing individual BNS events. To check the reliability of the FIM method, we further perform a full parameter estimation for a few simulated events. Based on the uncertainties on the tidal deformabilities associated to these events, we outline a mechanism to extract the underlying injected EoS using a recently developed meta-modelling approach within a Bayesian framework. Our results suggest that with $\gtrsim 500$ events with signal-to-noise ratio greater than $12$, we will be able to pin down very precisely the underlying EoS governing the neutron star matter.
Towards Realistic Zero-Shot Classification via Self Structural Semantic Alignment

Large-scale pre-trained Vision Language Models (VLMs) have proven effective for zero-shot classification. Despite the success, most traditional VLMs-based methods are restricted by the assumption of partial source supervision or ideal target vocabularies, which rarely satisfy the open-world scenario. In this paper, we aim at a more challenging setting, Realistic Zero-Shot Classification, which assumes no annotation but instead a broad vocabulary. To address the new problem, we propose the Self Structural Semantic Alignment (S3A) framework, which extracts the structural semantic information from unlabeled data while simultaneously self-learning. Our S3A framework adopts a unique Cluster-Vote-Prompt-Realign (CVPR) algorithm, which iteratively groups unlabeled data to derive structural semantics for pseudo-supervision. Our CVPR algorithm includes iterative clustering on images, voting within each cluster to identify initial class candidates from the vocabulary, generating discriminative prompts with large language models to discern confusing candidates, and realigning images and the vocabulary as structural semantic alignment. Finally, we propose to self-train the CLIP image encoder with both individual and structural semantic alignment through a teacher-student learning strategy. Our comprehensive experiments across various generic and fine-grained benchmarks demonstrate that the S3A method substantially improves over existing VLMs-based approaches, achieving a more than 15% accuracy improvement over CLIP on average. Our codes, models, and prompts are publicly released at https://github.com/sheng-eatamath/S3A.
Emotion-Aligned Contrastive Learning Between Images and Music

Traditional music search engines rely on retrieval methods that match natural language queries with music metadata. There have been increasing efforts to expand retrieval methods to consider the audio characteristics of music itself, using queries of various modalities including text, video, and speech. While most approaches aim to match general music semantics to the input queries, only a few focus on affective qualities. In this work, we address the task of retrieving emotionally-relevant music from image queries by learning an affective alignment between images and music audio. Our approach focuses on learning an emotion-aligned joint embedding space between images and music. This embedding space is learned via emotion-supervised contrastive learning, using an adapted cross-modal version of the SupCon loss. We evaluate the joint embeddings through cross-modal retrieval tasks (image-to-music and music-to-image) based on emotion labels. Furthermore, we investigate the generalizability of the learned music embeddings via automatic music tagging. Our experiments show that the proposed approach successfully aligns images and music, and that the learned embedding space is effective for cross-modal retrieval applications.
Continuous Reinforcement Learning-based Dynamic Difficulty Adjustment in a Visual Working Memory Game

Dynamic Difficulty Adjustment (DDA) is a viable approach to enhance a player's experience in video games. Recently, Reinforcement Learning (RL) methods have been employed for DDA in non-competitive games; nevertheless, they rely solely on discrete state-action space with a small search space. In this paper, we propose a continuous RL-based DDA methodology for a visual working memory (VWM) game to handle the complex search space for the difficulty of memorization. The proposed RL-based DDA tailors game difficulty based on the player's score and game difficulty in the last trial. We defined a continuous metric for the difficulty of memorization. Then, we consider the task difficulty and the vector of difficulty-score as the RL's action and state, respectively. We evaluated the proposed method through a within-subject experiment involving 52 subjects. The proposed approach was compared with two rule-based difficulty adjustment methods in terms of player's score and game experience measured by a questionnaire. The proposed RL-based approach resulted in a significantly better game experience in terms of competence, tension, and negative and positive affect. Players also achieved higher scores and win rates. Furthermore, the proposed RL-based DDA led to a significantly less decline in the score in a 20-trial session.
Understanding Solidity Event Logging Practices in the Wild

Writing logging messages is a well-established conventional programming practice, and it is of vital importance for a wide variety of software development activities. The logging mechanism in Solidity programming is enabled by the high-level event feature, but up to now there lacks study for understanding Solidity event logging practices in the wild. To fill this gap, we in this paper provide the first quantitative characteristic study of the current Solidity event logging practices using 2,915 popular Solidity projects hosted on GitHub. The study methodically explores the pervasiveness of event logging, the goodness of current event logging practices, and in particular the reasons for event logging code evolution, and delivers 8 original and important findings. The findings notably include the existence of a large percentage of independent event logging code modifications, and the underlying reasons for different categories of independent event logging code modifications are diverse (for instance, bug fixing and gas saving). We additionally give the implications of our findings, and these implications can enlighten developers, researchers, tool builders, and language designers to improve the event logging practices. To illustrate the potential benefits of our study, we develop a proof-of-concept checker on top of one of our findings and the checker effectively detects problematic event logging code that consumes extra gas in 35 popular GitHub projects and 9 project owners have already confirmed the detected issues.
On the origin of quasi-periodicity in the atmospheres of Earth, Jupiter, Saturn and the Sun

This paper explores a possible linkage between solar motion about the solar system center of mass and the quasi-periodicity evident in the pressure and temperature of planet atmospheres. We establish that dominant mid frequency range periodicity in planet atmospheres corresponds closely to the harmonic series 39.5/n = TA/n years where n = 2, 3, 4, etc. We establish that the period TA = 39.5 years is the interval between acceleration impulses experienced by the Sun as it passes close to the solar system center of mass and that the time sequence of impulses generates the spectral harmonic series TA/n that is observed in the periodicity of climate indices like the North Atlantic Oscillation and the Quasi Biennial Oscillation. We develop a model of a simple harmonic oscillator responding to periodic acceleration impulses and show that the response duplicates several features of the Quasi Biennial Oscillation. We conclude that oscillatory phenomena observed in solar activity and in planet atmosphere variability could be due to the response of the various natural oscillatory modes to impulsive Sun acceleration associated with planetary motion.
Towards Realistic Unsupervised Fine-tuning with CLIP

The emergence of vision-language models (VLMs), such as CLIP, has spurred a significant research effort towards their application for downstream supervised learning tasks. Although some previous studies have explored the unsupervised fine-tuning of CLIP, they often rely on prior knowledge in the form of class names associated with ground truth labels. In this paper, we delve into a realistic unsupervised fine-tuning scenario by assuming that the unlabeled data might contain out-of-distribution samples from unknown classes. Furthermore, we emphasize the importance of simultaneously enhancing out-of-distribution detection capabilities alongside the recognition of instances associated with predefined class labels. To tackle this problem, we present a simple, efficient, and effective fine-tuning approach called Universal Entropy Optimization (UEO). UEO leverages sample-level confidence to approximately minimize the conditional entropy of confident instances and maximize the marginal entropy of less confident instances. Apart from optimizing the textual prompts, UEO also incorporates optimization of channel-wise affine transformations within the visual branch of CLIP. Through extensive experiments conducted across 15 domains and 4 different types of prior knowledge, we demonstrate that UEO surpasses baseline methods in terms of both generalization and out-of-distribution detection.
NeuralClothSim: Neural Deformation Fields Meet the Kirchhoff-Love Thin Shell Theory

Cloth simulation is an extensively studied problem, with a plethora of solutions available in computer graphics literature. Existing cloth simulators produce realistic cloth deformations that obey different types of boundary conditions. Nevertheless, their operational principle remains limited in several ways: They operate on explicit surface representations with a fixed spatial resolution, perform a series of discretised updates (which bounds their temporal resolution), and require comparably large amounts of storage. Moreover, back-propagating gradients through the existing solvers is often not straightforward, which poses additional challenges when integrating them into modern neural architectures. In response to the limitations mentioned above, this paper takes a fundamentally different perspective on physically-plausible cloth simulation and re-thinks this long-standing problem: We propose NeuralClothSim, i.e., a new cloth simulation approach using thin shells, in which surface evolution is encoded in neural network weights. Our memory-efficient and differentiable solver operates on a new continuous coordinate-based representation of dynamic surfaces, i.e., neural deformation fields (NDFs); it supervises NDF evolution with the rules of the non-linear Kirchhoff-Love shell theory. NDFs are adaptive in the sense that they 1) allocate their capacity to the deformation details as the latter arise during the cloth evolution and 2) allow surface state queries at arbitrary spatial and temporal resolutions without retraining. We show how to train our NeuralClothSim solver while imposing hard boundary conditions and demonstrate multiple applications, such as material interpolation and simulation editing. The experimental results highlight the effectiveness of our formulation and its potential impact.
Computational Dating for the Nuzi Cuneiform Archive: The Least Squares Constrained by Family Trees and Synchronisms

We introduce a computational method of dating for an archive in ancient Mesopotamia. We use the name index Nuzi Personal Names (NPN) published in 1943. We made an electronic version of NPN and added the kinships of the two powerful families to NPN to reflect the Nuzi studies after 1943. Nuzi is a town from the 15th - 14th century B.C.E.for a period of some five generations in Arrapha. The cuneiform tablets listed in NPN are for contracts on land transactions, marriage, loans, slavery, etc. In NPN, the kinships and cuneiform tablets (contracts, documents, texts) involved are listed for each person. We reconstruct family trees from the added NPN to formulate the least squares problem with the constraints: a person's father is at least 22.5 years older than the person, contractors were living at the time of the contract, etc. Our results agree with the Assyriological results of M. P. Maidman on the seniority among siblings of a powerful family. Our method could be applied to the other clay tablet archives once we have the name index in the format of NPN.
Enhancing the Capture of Magnetic Nanoparticles Inside of Ferromagnetic Nanostructures Using External Magnetic Fields

The influence of an external magnetic field upon the capture of 130 nm magnetic nanoparticles (MNPs) by ferromagnetic nanostructures was investigated. The magnetophoretic forces acting upon a nanoparticle were simulated in external magnetic fields parallel and perpendicular to ferromagnetic nanostructures consisting of arrays of antidots and dots. Changing the direction of the external field was found to dramatically alter the magnetophoretic forces acting on the particle and the trajectories of the MNPs. A field parallel to the nanostructures' surfaces generated magnetophoretic forces that directed the nanoparticle into the nanostructures. A perpendicular field produced forces directing particles onto the structures' surfaces. Nanostructures were etched into the surfaces of Permalloy films using ion beam lithography. MNPs were then deposited onto the films' surfaces under a parallel or perpendicular magnetic field. The distributions of particles in the nanostructures were analysed to obtain the capture efficiencies of each structure which indicate the proportion of particles trapped inside. Without an external field, the highest efficiency was displayed by arrays of circular antidots with circular dot arrays displaying the lowest. Antidot arrays displayed higher capture efficiencies than dot arrays. Addition of a field parallel to the surface significantly increased the capture efficiencies and addition of a field perpendicular to the surface decreased the efficiencies. Under the perpendicular field, the particles were instead caught on the outer edges of the nanostructures. These results suggest that application of a parallel external magnetic field promotes the capture of MNPs within ferromagnetic nanostructures and a perpendicular field increases the capture of MNPs onto the outer surface and edges of nanostructures.