Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffusion Facial Forgery Detection

Detecting diffusion-generated images has recently grown into an emerging research area. Existing diffusion-based datasets predominantly focus on general image generation. However, facial forgeries, which pose a more severe social risk, have remained less explored thus far. To address this gap, this paper introduces DiFF, a comprehensive dataset dedicated to face-focused diffusion-generated images. DiFF comprises over 500,000 images that are synthesized using thirteen distinct generation methods under four conditions. In particular, this dataset leverages 30,000 carefully collected textual and visual prompts, ensuring the synthesis of images with both high fidelity and semantic consistency. We conduct extensive experiments on the DiFF dataset via a human test and several representative forgery detection methods. The results demonstrate that the binary detection accuracy of both human observers and automated detectors often falls below 30%, shedding light on the challenges in detecting diffusion-generated facial forgeries. Furthermore, we propose an edge graph regularization approach to effectively enhance the generalization capability of existing detectors.
AUTEKOLOGI BEGONIA DI SEBAGIAN KAWASAN TAMAN NASIONAL MANUSELA, MALUKU

Penelitian dilakukan pada bulan September 2010 di Taman Nasional Manusela, Maluku dengan tujuan untuk mengetahui data ekologi Begonia di habitat alaminya serta tumbuhan bawahnya yang ada di sekitarnya. Pengumpulan data dilakukan dengan menggunakan teknik purposive sampling , dan pengukuran dilakukan pada tempat-tempat ditemukan Begonia . Pengambilan sampel dilakukan dengan membuat plot ukuran 1 m x 1 m. Begonia ditemukan pada kisaran ketinggian 19-1.289 m dpl. Tumbuh pada kemiringan lahan 10-80%, pH 5,8-6,2 sedangkan kelembaban (rH) 60-100%. Hasil penelitian ini diharapkan dapat memberikan informasi yang bermanfaat dalam melakukan aklimatisasi dan konservasi Begonia . Kombinasi variable lingkungan yang lebih berpengaruuh terhadap komposisi jenis yaitu pH, rH (kelembaban) dan intensitas cahaya sebesar 0,246 sedangkan pH dan intensitas cahaya sebesar 0,241. Kelembaban 100% paling banyak dijumpai jenis-jenis Begonia yaitu sebanyak enam jenis. Sebanyak 10 jenis Begonia dapat dijumpai pada ketinggian 0-500 mdpl. , sedangkan lima jenis pada ketinggian 501-1.000 mdpl. dan empat jenis pada ketinggian 1001-1.500 mdpl.
The dynamics of casual groups can keep free-riders at bay

Understanding the conditions for maintaining cooperation in groups of unrelated individuals despite the presence of non-cooperative members is a major research topic in contemporary biological, sociological, and economic theory. The $N$-person snowdrift game models the type of social dilemma where cooperative actions are costly, but there is a reward for performing them. We study this game in a scenario where players move between play groups following the casual group dynamics, where groups grow by recruiting isolates and shrink by losing individuals who then become isolates. This describes the size distribution of spontaneous human groups and also the formation of sleeping groups in monkeys. We consider three scenarios according to the probability of isolates joining a group. We find that for appropriate choices of the cost-benefit ratio of cooperation and the aggregation-disaggregation ratio in the formation of casual groups, free-riders can be completely eliminated from the population. If individuals are more attracted to large groups, we find that cooperators persist in the population even when the mean group size diverges. We also point out the remarkable similarity between the replicator equation approach to public goods games and the trait group formulation of structured demes.
STAC: Leveraging Spatio-Temporal Data Associations For Efficient Cross-Camera Streaming and Analytics

We propose an efficient cross-cameras surveillance system called,STAC, that leverages spatio-temporal associations between multiple cameras to provide real-time analytics and inference under constrained network environments. STAC is built using the proposed omni-scale feature learning people reidentification (reid) algorithm that allows accurate detection, tracking and re-identification of people across cameras using the spatio-temporal characteristics of video frames. We integrate STAC with frame filtering and state-of-the-art compression for streaming technique (that is, ffmpeg libx264 codec) to remove redundant information from cross-camera frames. This helps in optimizing the cost of video transmission as well as compute/processing, while maintaining high accuracy for real-time query inference. The introduction of AICity Challenge 2023 Data [1] by NVIDIA has allowed exploration of systems utilizing multi-camera people tracking algorithms. We evaluate the performance of STAC using this dataset to measure the accuracy metrics and inference rate for reid. Additionally, we quantify the reduction in video streams achieved through frame filtering and compression using FFmpeg compared to the raw camera streams. For completeness, we make available our repository to reproduce the results, available at https://github.com/VolodymyrVakhniuk/CS444_Final_Project.
Multi-LLM Collaboration + Data-Centric Innovation = 2x Better Vulnerability Repair

The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system. To address this, we propose VulMaster, a Transformer-based neural network model that excels at generating vulnerability repairs through data-centric innovation. Specifically, VulMaster introduces the utilization and combination of various types of input data, including complete vulnerable code of any size, vulnerable code structures, and expert knowledge from the CWE system. Additionally, VulMaster leverages the collaboration between two Large Language Models (LLMs), CodeT5 and ChatGPT: CodeT5 acts as the customizable backbone LLM, fine-tuned with the training data, while ChatGPT supplements by providing missing relevant inputs to CodeT5. We evaluated VulMaster on a real-world C/C++ vulnerability repair dataset comprising 1,754 projects with 5,800 vulnerable functions. The experimental results demonstrated that VulMaster exhibits substantial improvements compared to the learning-based state-of-the-art vulnerability repair approach. Specifically, VulMaster improves the EM, BLEU, and CodeBLEU scores from 10.2\% to 20.0\%, 21.3\% to 29.3\%, and 32.5\% to 40.9\%, respectively.
LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning

Most existing Legal Judgment Prediction (LJP) models focus on discovering the legal triggers in the criminal fact description. However, in real-world scenarios, a professional judge not only needs to assimilate the law case experience that thrives on past sentenced legal judgments but also depends on the professional legal grounded reasoning that learned from professional legal knowledge. In this paper, we propose a LegalDuet model, which pretrains language models to learn a tailored embedding space for making legal judgments. It proposes a dual-view legal clue reasoning mechanism, which derives from two reasoning chains of judges: 1) Law Case Reasoning, which makes legal judgments according to the judgment experiences learned from analogy/confusing legal cases; 2) Legal Ground Reasoning, which lies in matching the legal clues between criminal cases and legal decisions. Our experiments show that LegalDuet achieves state-of-the-art performance on the CAIL2018 dataset and outperforms baselines with about 4% improvements on average. Our dual-view reasoning based pretraining can capture critical legal clues to learn a tailored embedding space to distinguish criminal cases. It reduces LegalDuet's uncertainty during prediction and brings pretraining advances to the confusing/low frequent charges. All codes are available at https://github.com/NEUIR/LegalDuet.
Exact solutions for a coherent phenomenon of condensation in conservative Hamiltonian systems

While it is known that Hamiltonian systems may undergo a phenomenon of condensation akin to Bose-Einstein condensation, not all the manifestations of this phenomenon have been uncovered yet. In this work, we present a novel form of condensation in conservative Hamiltonian systems, which happens through coherent states and exploits the discreteness of our system. Both features markedly differ from well-known condensation processes in the literature. Our result is based on a deterministic approach to obtain exact explicit solutions representing the dynamical formation of condensates in finite time. We reveal a dual-cascade behavior during the process, featuring inverse and direct transfer of conserved quantities across the spectrum. The direct cascade yields the excitation of high modes in finite time, a phenomenon quantified through the blow-up of Sobolev norms. We provide a fully analytic description of all the processes involved.
Face to Cartoon Incremental Super-Resolution using Knowledge Distillation

Facial super-resolution/hallucination is an important area of research that seeks to enhance low-resolution facial images for a variety of applications. While Generative Adversarial Networks (GANs) have shown promise in this area, their ability to adapt to new, unseen data remains a challenge. This paper addresses this problem by proposing an incremental super-resolution using GANs with knowledge distillation (ISR-KD) for face to cartoon. Previous research in this area has not investigated incremental learning, which is critical for real-world applications where new data is continually being generated. The proposed ISR-KD aims to develop a novel unified framework for facial super-resolution that can handle different settings, including different types of faces such as cartoon face and various levels of detail. To achieve this, a GAN-based super-resolution network was pre-trained on the CelebA dataset and then incrementally trained on the iCartoonFace dataset, using knowledge distillation to retain performance on the CelebA test set while improving the performance on iCartoonFace test set. Our experiments demonstrate the effectiveness of knowledge distillation in incrementally adding capability to the model for cartoon face super-resolution while retaining the learned knowledge for facial hallucination tasks in GANs.
Scalable Qualitative Coding with LLMs: Chain-of-Thought Reasoning Matches Human Performance in Some Hermeneutic Tasks

Qualitative coding, or content analysis, extracts meaning from text to discern quantitative patterns across a corpus of texts. Recently, advances in the interpretive abilities of large language models (LLMs) offer potential for automating the coding process (applying category labels to texts), thereby enabling human researchers to concentrate on more creative research aspects, while delegating these interpretive tasks to AI. Our case study comprises a set of socio-historical codes on dense, paragraph-long passages representative of a humanistic study. We show that GPT-4 is capable of human-equivalent interpretations, whereas GPT-3.5 is not. Compared to our human-derived gold standard, GPT-4 delivers excellent intercoder reliability (Cohen's $\kappa \geq 0.79$) for 3 of 9 codes, and substantial reliability ($\kappa \geq 0.6$) for 8 of 9 codes. In contrast, GPT-3.5 greatly underperforms for all codes ($mean(\kappa) = 0.34$; $max(\kappa) = 0.55$). Importantly, we find that coding fidelity improves considerably when the LLM is prompted to give rationale justifying its coding decisions (chain-of-thought reasoning). We present these and other findings along with a set of best practices for adapting traditional codebooks for LLMs. Our results indicate that for certain codebooks, state-of-the-art LLMs are already adept at large-scale content analysis. Furthermore, they suggest the next generation of models will likely render AI coding a viable option for a majority of codebooks.
Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning

Federated reinforcement learning (FRL) has emerged as a promising paradigm for reducing the sample complexity of reinforcement learning tasks by exploiting information from different agents. However, when each agent interacts with a potentially different environment, little to nothing is known theoretically about the non-asymptotic performance of FRL algorithms. The lack of such results can be attributed to various technical challenges and their intricate interplay: Markovian sampling, linear function approximation, multiple local updates to save communication, heterogeneity in the reward functions and transition kernels of the agents' MDPs, and continuous state-action spaces. Moreover, in the on-policy setting, the behavior policies vary with time, further complicating the analysis. In response, we introduce FedSARSA, a novel federated on-policy reinforcement learning scheme, equipped with linear function approximation, to address these challenges and provide a comprehensive finite-time error analysis. Notably, we establish that FedSARSA converges to a policy that is near-optimal for all agents, with the extent of near-optimality proportional to the level of heterogeneity. Furthermore, we prove that FedSARSA leverages agent collaboration to enable linear speedups as the number of agents increases, which holds for both fixed and adaptive step-size configurations.
Generalized Fluctuation Theorem for Quantum Heat

Nonequilibrium fluctuation relation is a cornerstone of quantum thermodynamics. It is widely believed that the system-bath heat exchange obeys the famous Jarzynski-W\'{o}jcik fluctuation theorem. However, this theorem is established in the Born-Markovian approximation under the weak coupling condition. Via studying the quantum heat of a harmonic oscillator coupled to a bath in the non-Markovian dynamics, we here establish a generalized fluctuation theorem for quantum heat being valid for arbitrary system-bath coupling strength. The Jarzynski-W\'{o}jcik fluctuation theorem is recovered in the weak-coupling limit. We also find the average quantum heat exhibits rich nonequilibrum characters when different numbers of system-bath bound states are formed, which suggests a useful way to control the quantum heat. Deepening our understanding to the fluctuation relation in quantum thermodynamics, our result lays a foundation to design high-efficiency quantum heat engines.
Nonlinear electroosmosis of traveling wave-charged capillaries

Traveling wave charges lying on the insulating walls of an electrolyte-filled capillary, give rise to oscillatory modes which vanish when averaged over the period of oscillation. In this paper we show that they also give rise to a zero velocity mode which does not vanish. The latter is a nonlinear effect caused by continuous symmetry-breaking and the commensurate fluid velocity varies quadratically with respect to the electric field generated by the charge distribution, in contrast to the linear field dependence present in conventional electroosmosis formulations. As the effect becomes more pronounced by reducing the characteristic lengthscale of the system, it can be employed for unidirectional transport of electrolytes in small capillaries. Certain results from the literature are recovered as special cases of our formulation. The hydrodynamic theory developed here is based on a semi-infinite space, on a finite channel and on cylindrical capillary configurations. General relations derived, expressing the zero mode velocity in terms of the electric potential only, are independent of the boundary conditions imposed on the charge and potential and can thus be adopted to suit alternative experimental conditions.
A New Framework to Predict and Visualize Technology Acceptance: A Case Study of Shared Autonomous Vehicles

The burgeoning field of Shared Autonomous Vehicles (SAVs) presents transformative potential for the transport sector, subject to public acceptance. Traditional acceptance models, primarily reliant on Structural Equation Modelling (SEM), often fall short of capturing the complex, non-linear dynamics underlying this acceptance. To address these limitations, this paper proposes a Machine Learning (ML) approach to predict public acceptance of SAVs and employs a chord diagram to visualize the influence of different predictors. This approach reveals nuanced, non-linear relationships between factors at both macro and micro levels, and identifies attitude as the primary predictor of SAV usage intention, followed by perceived risk, perceived usefulness, trust, and perceived ease of use. The framework also uncovers divergent perceptions of these factors among SAV adopters and non-adopters, providing granular insights for strategic initiatives to enhance SAV acceptance. Using SAV acceptance as a case study, our findings contribute a novel, machine learning-based perspective to the discourse on technology acceptance, underscoring the importance of nuanced, data-driven approaches in understanding and fostering public acceptance of emerging transport technologies.
Preparation and Analysis of Polar Hawthorn Berry Extracts, Industrial Application in Poultry Processing

For years, it is an actuality in the poultry processing industry, that the body and the cardiovascular system of the chickens are not strong enough due to the shortened rearing time. This induces, that the quality of the chicken breast is not appropriate for selling, since during the processing procedure, frequently blood spots appear inside the meat. These damaged parts need to be removed, causing huge economic loss. In this paper a solution for this problem is searched using hawthorn, a traditional herb, with well-known beneficial effects to the cardiovascular system. Improvement of the product quality and the reducing amount of the removed meat are supposed by applying a treatment with a polyphenol-rich drinking tincture made from the plant. Beside the preparation of the drinking liquid, including pretreatment, extraction and dilution steps, chemical analysis of the phenolic compounds is also conducted. The result of the drinking test shows that the extent of the blood spots, and thus the quantity of the "cut off" meat decreased significantly, however the reduction of the meat weight and a slight change in the death rate are also needed to take into consideration. It has been found, that the total phenolic contents of the berry are similar to literature data, while they show moderate antioxidant efficiency. Several phenolic acids, procyanidins and flavonoids were identified as the main phenol compounds of the applied extract.
On the asymptotic behaviour of the Vasconcelos invariant for graded modules

The notion of Vasconcelos invariant, known in the literature as v-number, of a homogeneous ideal in a polynomial ring over a field was introduced in 2020 to study the asymptotic behaviour of the minimum distance of projective Reed-Muller type codes. We initiate the study of this invariant for graded modules. Let $R$ be a Noetherian $\mathbb{N}$-graded ring, and $M$ be a finitely generated graded $R$-module. The v-number $v(M)$ can be defined as the least possible degree of a homogeneous element $x$ of $M$ for which $(0:_Rx)$ is a prime ideal of $R$. For a homogeneous ideal $I$ of $R$, we mainly prove that $v(I^nM)$ and $v(I^nM/I^{n+1}M)$ are eventually linear functions of $n$. In addition, if $(0:_M I)=0$, then $v(M/I^{n}M)$ is also eventually linear with the same leading coefficient as that of $v(I^nM/I^{n+1}M)$. These leading coefficients are described explicitly. The result on the linearity of $v(M/I^{n}M)$ considerably strengthens a recent result of Conca which was shown when $R$ is a domain and $M=R$, and Ficarra-Sgroi where the polynomial case is treated.
Reference Coverage Analysis of OpenAlex compared to Web of Science and Scopus

OpenAlex is a promising open source of scholarly metadata, and competitor to the established proprietary sources, the Web of Science and Scopus. As OpenAlex provides its data freely and openly, it permits researchers to perform bibliometric studies that can be reproduced in the community without licensing barriers. However, as OpenAlex is a rapidly evolving source and the data contained within is expanding and also quickly changing, the question naturally arises as to the trustworthiness of its data. In this empirical paper, we will study the reference and metadata coverage within each database and compare them with each other to help address this open question in bibliometrics. In our large-scale study, we demonstrate that, when restricted to a cleaned dataset of 16,788,282 recent publications shared by all three databases, OpenAlex has average reference numbers comparable to both Web of Science and Scopus. We also demonstrate that the comparison of other core metadata covered by OpenAlex shows mixed results, with OpenAlex capturing more ORCID identifiers, fewer abstracts and a similar number of Open Access information per article when compared to both Web of Science and Scopus.
Kubota-type formulas and supports of mixed measures

Motivated by a problem for mixed Monge-Amp\`ere measures of convex functions, we address a special case of a conjecture of Schneider and show that for every convex body $K$ the support of the mixed area measure $S(K[j],B_L^{n-1}[n-1-j],\cdot)$ is given by the set of $(K[j],B_L^{n-1}[n-1-j])$-extreme unit normal vectors, where $B_L^{n-1}$ denotes the $(n-1)$-dimensional Euclidean unit ball in a hyperplane $L$. As a consequence, we see that the supports of these measures are nested. Our proof introduces a Kubota-type formula for mixed area measures, which involves integration over $j$-dimensional linear subspaces that contain a fixed $1$-dimensional subspace. We transfer these results to the analytic setting, where we obtain corresponding statements for (conjugate) mixed Monge-Amp\`ere measures of convex functions. Thereby, we establish a fundamental property for functional intrinsic volumes. In addition, we study connections between mixed Monge-Amp\`ere measures of convex functions and mixed area measures and mixed volumes of convex bodies.
Complexity results on locally-balanced $2$-partitions of graphs

A \emph{$2$-partition of a graph $G$} is a function $f:V(G)\rightarrow \{0,1\}$. A $2$-partition $f$ of a graph $G$ is a \emph{locally-balanced with an open neighborhood} if for every $v\in V(G)$, $$\left\vert \vert \{u\in N_{G}(v)\colon\,f(u)=0\}\vert - \vert \{u\in N_{G}(v)\colon\,f(u)=1\}\vert \right\vert\leq 1. $$ A $2$-partition $f^{\prime}$ of a graph $G$ is a \emph{locally-balanced with a closed neighborhood} if for every $v\in V(G)$, $$\left\vert \vert \{u\in N_{G}[v]\colon\,f^{\prime}(u)=0\}\vert - \vert \{u\in N_{G}[v]\colon\,f^{\prime}(u)=1\}\vert \right\vert\leq 1. $$ In this paper we prove that the problem of the existence of locally-balanced $2$-partition with an open (closed) neighborhood is $NP$-complete for some restricted classes of graphs. In particular, we show that the problem of deciding if a given graph has a locally-balanced $2$-partition with an open neighborhood is $NP$-complete for biregular bipartite graphs and even bipartite graphs with maximum degree $4$, and the problem of deciding if a given graph has a locally-balanced $2$-partition with a closed neighborhood is $NP$-complete even for subcubic bipartite graphs and odd graphs with maximum degree $3$. Last results prove a conjecture of Balikyan and Kamalian.