Miracle-dz/UniFAR-FTUs huggingface dataset:
Xaminer: An Internet Cross-Layer Resilience Analysis Tool

A resilient Internet infrastructure is critical in our highly interconnected society. However, the Internet faces several vulnerabilities, ranging from natural disasters to human activities, that can impact the physical layer and, in turn, the higher network layers, such as IP links. In this paper, we introduce Xaminer, the first Internet cross-layer resilience analysis tool, to evaluate the interplay between physical- and network-layer failures. Using a cross-layer Internet map and a failure event model, Xaminer generates a risk profile encompassing a cross-layer impact report, critical infrastructure identification at each layer, and the discovery of trends and patterns under different failure event settings. Xaminer's key strengths lie in its adaptability to diverse disaster scenarios, the ability to assess risks at various granularities, and the capability to generate joint risk profiles for multiple events. We demonstrate Xaminer's capabilities in cross-layer analysis across a spectrum of disaster event models and regions, showcasing its potential role in facilitating well-informed decision-making for resilience planning and deployments. The Xaminer tool is available as open-source software.
The Pitfalls of Defining Hallucination

Despite impressive advances in Natural Language Generation (NLG) and Large Language Models (LLMs), researchers are still unclear about important aspects of NLG evaluation. To substantiate this claim, I examine current classifications of hallucination and omission in Datatext NLG, and I propose a logic-based synthesis of these classfications. I conclude by highlighting some remaining limitations of all current thinking about hallucination and by discussing implications for LLMs.
Stellar Cycle and Evolution of Polar Spots in an M+WD Binary

Stellar activity cycles reveal continuous relaxation and induction of magnetic fields. The activity cycle is typically traced through the observation of cyclic variations in total brightness or Ca H&K emission flux of stars, as well as cyclic variations in the orbital periods of binary systems. In this work, we report the identification of a semidetached binary system (TIC 16320250) consisting of a white dwarf (0.67 M ⊙) and an active M dwarf (0.56 M ⊙). The long-term multiband optical light curves spanning twenty years revealed three repeated patterns, suggestive of a possible activity cycle of about 10 years of the M dwarf. Light-curve fitting indicates the repeated variation is caused by the evolution, particularly the motion, of polar spots. The significant Ca H&K, Hα, ultra-violet, and X-ray emissions imply that the M dwarf is one of the most magnetically active stars. We propose that in the era of large time-domain photometric sky surveys (e.g., ASAS-SN, Zwicky Transient Facility, LSST, Sitian), long-term light-curve modeling can be a valuable tool for tracing and revealing stellar activity cycle, especially for stars in binary systems.
Generative AI-enabled Quantum Computing Networks and Intelligent Resource Allocation

Quantum computing networks enable scalable collaboration and secure information exchange among multiple classical and quantum computing nodes while executing large-scale generative AI computation tasks and advanced quantum algorithms. Quantum computing networks overcome limitations such as the number of qubits and coherence time of entangled pairs and offer advantages for generative AI infrastructure, including enhanced noise reduction through distributed processing and improved scalability by connecting multiple quantum devices. However, efficient resource allocation in quantum computing networks is a critical challenge due to factors including qubit variability and network complexity. In this article, we propose an intelligent resource allocation framework for quantum computing networks to improve network scalability with minimized resource costs. To achieve scalability in quantum computing networks, we formulate the resource allocation problem as stochastic programming, accounting for the uncertain fidelities of qubits and entangled pairs. Furthermore, we introduce state-of-the-art reinforcement learning (RL) algorithms, from generative learning to quantum machine learning for optimal quantum resource allocation to resolve the proposed stochastic resource allocation problem efficiently. Finally, we optimize the resource allocation in heterogeneous quantum computing networks supporting quantum generative learning applications and propose a multi-agent RL-based algorithm to learn the optimal resource allocation policies without prior knowledge.
PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traits

The new wave of Large Language Models (LLM) has offered an efficient tool to curate sizeable conversational datasets. So far studies have mainly focused on task-oriented or generic open-domain dialogs, and have not fully explored the ability of LLMs in following complicated prompts. In this work, we focus on personalization, and employ LLMs to curate a dataset which is difficult and costly to crowd-source: PersonalityChat is a synthetic conversational dataset based upon the popular PersonaChat dataset, but conditioned on both personas and (Big-5) personality traits. Evaluating models fine-tuned on this dataset, we show that the personality trait labels can be used for trait-based personalization of generative dialogue models. We also perform a head-to-head comparison between PersonalityChat and PersonaChat, and show that training on the distilled dataset results in more fluent and coherent dialog agents in the small-model regime.
PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes

Decoding the core of procedural texts, exemplified by cooking recipes, is crucial for intelligent reasoning and instruction automation. Procedural texts can be comprehensively defined as a sequential chain of steps to accomplish a task employing resources. From a cooking perspective, these instructions can be interpreted as a series of modifications to a food preparation, which initially comprises a set of ingredients. These changes involve transformations of comestible resources. For a model to effectively reason about cooking recipes, it must accurately discern and understand the inputs and outputs of intermediate steps within the recipe. Aiming to address this, we present a new corpus of cooking recipes enriched with descriptions of intermediate steps of the recipes that explicate the input and output for each step. We discuss the data collection process, investigate and provide baseline models based on T5 and GPT-3.5. This work presents a challenging task and insight into commonsense reasoning and procedural text generation.
ADMIn: Attacks on Dataset, Model and Input. A Threat Model for AI Based Software

Machine learning (ML) and artificial intelligence (AI) techniques have now become commonplace in software products and services. When threat modelling a system, it is therefore important that we consider threats unique to ML and AI techniques, in addition to threats to our software. In this paper, we present a threat model that can be used to systematically uncover threats to AI based software. The threat model consists of two main parts, a model of the software development process for AI based software and an attack taxonomy that has been developed using attacks found in adversarial AI research. We apply the threat model to two real life AI based software and discuss the process and the threats found.
Type I multivariate P\'olya-Aeppli distributions with applications

An extensive body of literature exists that specifically addresses the univariate case of zero-inflated count models. In contrast, research pertaining to multivariate models is notably less developed. We proposed two new parsimonious multivariate models which can be used to model correlated multivariate overdispersed count data. Furthermore, for different parameter settings and sample sizes, various simulations are performed. In conclusion, we demonstrated the performance of the newly proposed multivariate candidates on two benchmark datasets, which surpasses that of several alternative approaches.
Fully Decentralized Design of Initialization-free Distributed Network Size Estimation

In this paper, we propose a distributed scheme for estimating the network size, which refers to the total number of agents in a network. By leveraging a synchronization technique for multi-agent systems, we devise an agent dynamics that ensures convergence to an equilibrium point located near the network size regardless of its initial condition. Our approach is based on an assumption that each agent has a unique identifier, and an estimation algorithm for obtaining the largest identifier value. By adopting this approach, we successfully implement the agent dynamics in a fully decentralized manner, ensuring accurate network size estimation even when some agents join or leave the network.
A Comparative Analysis of Smartphone and Standard Tools for Touch Perception Assessment Across Multiple Body Sites

Tactile perception plays an important role in activities of daily living, and it can be impaired in individuals with certain medical conditions. The most common tools used to assess tactile sensation, the Semmes-Weinstein monofilaments and the 128 Hz tuning fork, have poor repeatability and resolution. Long term, we aim to provide a repeatable, high-resolution testing platform that can be used to assess vibrotactile perception through smartphones without the need for an experimenter to be present to conduct the test. We present a smartphone-based vibration perception measurement platform and compare its performance to measurements from standard monofilament and tuning fork tests. We conducted a user study with 36 healthy adults in which we tested each tool on the hand, wrist, and foot, to assess how well our smartphone-based vibration perception thresholds (VPTs) detect known trends obtained from standard tests. The smartphone platform detected statistically significant changes in VPT between the index finger and foot and also between the feet of younger adults and older adults. Our smartphone-based VPT had a moderate correlation to tuning fork-based VPT. Our overarching objective is to develop an accessible smartphone-based platform that can eventually be used to measure disease progression and regression.
Inroads to a Structured Data Natural Language Bijection and the role of LLM annotation

This work finds limited evidence supporting the theory that using multiple tasks with sequence-to-sequence transformer language models can improve performance on some metrics. In particular, the multi-task generalist t5-small outperforms the specialist t5-small with a $F_1$ of $0.771$ up from $0.692$, which may point to underlying cross-task knowledge generalization. This further suggests that even with the same network,"re-using"the same data in a different way may lead to higher performance in some metrics. However, the inverse task alone is likely only an optimization strategy, since it does not yield a significant general improvement at the model sizes explored in this work. Also, adding $\approx 4500$ LLM annotated records (interlaced with the $12800$ WebNLG training records) does not substantially change automatic metric performance compared to the same t5-small model without the synthetic data. This may be due to a learning capacity bottleneck on account of model size, and decreases observed may be due to distributional differences in the corpora. Future research using larger models or human evaluation is required to more fully explain the mechanisms contributing to performance on these tasks.
Leveraging the power of transformers for guilt detection in text

In recent years, language models and deep learning techniques have revolutionized natural language processing tasks, including emotion detection. However, the specific emotion of guilt has received limited attention in this field. In this research, we explore the applicability of three transformer-based language models for detecting guilt in text and compare their performance for general emotion detection and guilt detection. Our proposed model outformed BERT and RoBERTa models by two and one points respectively. Additionally, we analyze the challenges in developing accurate guilt-detection models and evaluate our model's effectiveness in detecting related emotions like"shame"through qualitative analysis of results.
An approach to the moments subset sum problem through systems of diagonal equations over finite fields

Let $\mathbb{F}_q$ be the finite field of $q$ elements, for a given subset $D\subset \mathbb{F}_q$, $m\in \mathbb{N}$, an integer $k\leq |D|$ and $\boldsymbol{b}\in \mathbb{F}_q^m$ we are interested in determining the existence of a subset $S\subset D$ of cardinality $k$ such that $\sum_{a\in S}a^i=b_i$ for $i=1,\ldots, m$. This problem is known as the moment subset sum problem and it is $NP$-complete for a general $D$. We make a novel approach of this problem trough algebraic geometry tools analyzing the underlying variety and employing combinatorial techniques to estimate the number of $\mathbb{F}_q$-rational points on certain varieties. We managed to give estimates on the number of $\mathbb{F}_q$-rational points on certain diagonal equations and use this results to give estimations and existence results for the subset sum problem.
Cross-Attention Watermarking of Large Language Models

A new approach to linguistic watermarking of language models is presented in which information is imperceptibly inserted into the output text while preserving its readability and original meaning. A cross-attention mechanism is used to embed watermarks in the text during inference. Two methods using cross-attention are presented that minimize the effect of watermarking on the performance of a pretrained model. Exploration of different training strategies for optimizing the watermarking and of the challenges and implications of applying this approach in real-world scenarios clarified the tradeoff between watermark robustness and text quality. Watermark selection substantially affects the generated output for high entropy sentences. This proactive watermarking approach has potential application in future model development.
A Strong Inductive Bias: Gzip for binary image classification

Deep learning networks have become the de-facto standard in Computer Vision for industry and research. However, recent developments in their cousin, Natural Language Processing (NLP), have shown that there are areas where parameter-less models with strong inductive biases can serve as computationally cheaper and simpler alternatives. We propose such a model for binary image classification: a nearest neighbor classifier combined with a general purpose compressor like Gzip. We test and compare it against popular deep learning networks like Resnet, EfficientNet and Mobilenet and show that it achieves better accuracy and utilizes significantly less space, more than two order of magnitude, within a few-shot setting. As a result, we believe that this underlines the untapped potential of models with stronger inductive biases in few-shot scenarios.
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step

Large language models (LLM) have achieved remarkable performance on various NLP tasks and are augmented by tools for broader applications. Yet, how to evaluate and analyze the tool-utilization capability of LLMs is still under-explored. In contrast to previous works that evaluate models holistically, we comprehensively decompose the tool utilization into multiple sub-processes, including instruction following, planning, reasoning, retrieval, understanding, and review. Based on that, we further introduce T-Eval to evaluate the tool utilization capability step by step. T-Eval disentangles the tool utilization evaluation into several sub-domains along model capabilities, facilitating the inner understanding of both holistic and isolated competency of LLMs. We conduct extensive experiments on T-Eval and in-depth analysis of various LLMs. T-Eval not only exhibits consistency with the outcome-oriented evaluation but also provides a more fine-grained analysis of the capabilities of LLMs, providing a new perspective in LLM evaluation on tool-utilization ability. The benchmark will be available at https://github.com/open-compass/T-Eval.
Genetic and behavioural factors affecting interpopulation colour pattern variation in two congeneric chameleon species

We conducted a study on interpopulation variation of colour patterns in two congeneric chameleon species, which have an analogous life history. Both species are able to rapidly change colour pattern, and their context-dependent colour patterns often vary across a wide geographical range. Specifically, we tested four hypotheses that can explain the observed interpopulation variation of colour patterns by a series of behavioural field trials where the colour patterns of individuals were recorded and later analysed by a deep neural network algorithm. We used redundancy analysis to relate genetic, spectral and behavioural predictors to interpopulation colour pattern distance. Our results showed that both isolation by distance (IBD) and alternative mating tactics were significant predictors for interpopulation colour pattern variation in Chamaeleo chamaeleon males. By contrast, in Chamaeleo dilepis, the interpopulation colour pattern variation was largely explained by IBD, and evidence for alternative mating tactics was absent. In both chameleon species, the environmental colours showed no evidence of influencing chameleon interpopulation colour pattern variation, regardless of sex or behavioural context. This contrasting finding suggests that interpopulation context-dependent colour pattern variations in each species are maintained under a different set of selective pressures or circumstances.
A novel approach to low-cost, rapid and simultaneous colorimetric detection of multiple analytes using 3D printed microfluidic channels

This research paper presents an inventive technique to swiftly create microfluidic channels on distinct membrane papers, enabling colorimetric drug detection. Using a modified DIY RepRap 3D printer with a syringe pump, microfluidic channels (µPADs) are crafted on a flexible nylon-based substrate. This allows simultaneous detection of four common drugs with a single reagent. An optimized blend of polydimethylsiloxane (PDMS) dissolved in hexane is used to create hydrophobic channels on various filter papers. The PDMS-hexane mixture infiltrates the paper's pores, forming hydrophobic barriers that confine liquids within the channels. These barriers are cured on the printer's hot plate, controlling channel width and preventing spreading. Capillary action drives fluid along these paths without spreading. This novel approach provides a versatile solution for rapid microfluidic channel creation on membrane papers. The DIY RepRap 3D printer integration offers precise control and faster curing. The PDMS-hexane solution accurately forms hydrophobic barriers, containing liquids within desired channels. The resulting microfluidic system holds potential for portable, cost-effective drug detection and various sensing applications.
Therapeutic advances in rheumatoid arthritis

Abstract Rheumatoid arthritis (RA) is one of the most common immune mediated inflammatory diseases. People with rheumatoid arthritis present with pain, swelling, and stiffness that typically affects symmetrically distributed small and large joints. Without effective treatment, significant joint damage, disability, and work loss develop, owing to chronic inflammation of the joint lining (synovium). Over the past 25 years, the management of this condition has been revolutionized, resulting in substantially higher levels of disease remission and better long term outcomes. This improvement reflects a paradigm shift towards early and aggressive pharmacological intervention coupled with a proliferation in treatment choice, in turn related to enhanced pathobiological understanding and the advent of new drugs for rheumatoid arthritis. Following an overview of these developments from a historical perspective, and with a general audience in mind, this review focuses on newer, targeted treatments in an ever evolving landscape. The review highlights ongoing areas of debate and unmet need, including the proportion of patients with persistent, difficult-to-treat disease, despite recent advances. Also discussed are personalized, strategic approaches to individual patients, the role for imaging in clinical decision making, and the goal of sustained, drug free remission and disease prevention in the future.
Tracing the Footprints of AI in Radiology Literature: A Detailed Analysis of Journal Abstracts

Abstract Purpose To assess and compare the probabilities of AI-generated content within scientific abstracts from selected Q1 journals in the fields of radiology, nuclear medicine, and imaging, published between May and August 2022 and May and August 2023. Materials and Methods An extensive list of Q1 journals was acquired from Scopus in the fields of radiology, nuclear medicine, and imaging. All articles in these journals were acquired from the Medline databases, focusing on articles published between May and August in 2022 and 2023. The study specifically compared abstracts for limitations of the AI detection tool in terms of word constraints. Extracted abstracts from the two different periods were categorized into two groups, and each abstract was analyzed using the AI detection tool, a system capable of distinguishing between human and AI-generated content with a validated accuracy of 97.06 %. This tool assessed the probability of each abstract being AI-generated, enabling an in-depth comparison between the two groups in terms of the prevalence of AI-generated content probability. Results Group 1 and Group 2 exhibit significant variations in the characteristics of AI-generated content probability. Group 1, consisting of 4,727 abstracts, has a median AI-generated content probability of 3.8 % (IQR1.9–9.9 %) and peaks at 49.9 %, with the computation times contained within a range of 2 to 10 seconds (IQR 3–8 s). In contrast, Group 2, which is composed of 3,917 abstracts, displays a significantly higher median AI-generated content probability at 5.7 % (IQR2.8–12.9 %) surging to a maximum of 69.9 %, with computation times spanning from 2 to 14 seconds (IQR 4–11 s). This comparison yields a statistically significant difference in median AI-generated content probability between the two groups (p = 0.005). No significant correlation was observed between word count and AI probability, as well as between article type, primarily original articles and reviews, and AI probability, indicating that AI probability is independent of these factors. Conclusion The comprehensive analysis reveals significant differences and variations in AI-generated content probabilities between 2022 and 2023, indicating a growing presence of AI-generated content. However, it also illustrates that abstract length or article type does not impact the likelihood of content being AI-generated. Key Points: The study examines AI-generated content probability in scientific abstracts from Q1 journals between 2022 to 2023. The AI detector tool indicates an increased median AI content probability from 3.8 % to 5.7 %. No correlation was found between abstract length or article type and AI probability.