Miracle-dz/UniFAR-FTUs huggingface dataset:
A QoS-Aware Joint Uplink Spectrum and Power Allocation with Link Adaptation for Vehicular Communications in 5G networks

In this work, we have proposed link adaptation based joint spectrum and power allocation algorithms for uplink cellular vehicle-to-everything (C-V2X) communication for OFDMA based 5G systems. In C-V2X, vehicle-to-vehicle (V2V) users share radio resources with vehicle-to-infrastructure (V2I) users. Existing works primarily focus on the optimal pairing of V2V and V2I users under the assumption that each V2I user needs a single resource block (RB); minimizing any interference through power allocation. In contrast, in this work, we have considered that the number of RBs needed by the users is a function of their channel condition and Quality of Service (QoS) - a method called link adaptation. It is effective in compensating for the incessant channel quality fluctuations at the high frequencies of operation of 5G. The first proposed resource allocation scheme of this work greedily allocates RBs to V2I users using link adaptation and then uses Hungarian algorithm to pair V2V with V2I users, while minimizing interference through power allocation. To reduce the complexity, the second scheme groups RBs into resource chunks (RCs); then uses Hungarian algorithm twice - first to allocate RCs to V2I users, and then to pair V2I users with V2V users. Extensive simulations reveal that link adaptation increases the number of satisfied V2I users as well as their sum-rate, while also improving QoS of individual users, thereby making it indispensable for C-V2X system. We have also considered the presence of best-effort users, and the diverse QoS has been provided through the multi-numerology frame structure of 5G.
Critical heat flux diagnosis using conditional generative adversarial networks

The critical heat flux (CHF) is an essential safety boundary in boiling heat transfer processes employed in high heat flux thermal-hydraulic systems. Identifying CHF is vital for preventing equipment damage and ensuring overall system safety, yet it is challenging due to the complexity of the phenomena. For an in-depth understanding of the complicated phenomena, various methodologies have been devised, but the acquisition of high-resolution data is limited by the substantial resource consumption required. This study presents a data-driven, image-to-image translation method for reconstructing thermal data of a boiling system at CHF using conditional generative adversarial networks (cGANs). The supervised learning process relies on paired images, which include total reflection visualizations and infrared thermometry measurements obtained from flow boiling experiments. Our proposed approach has the potential to not only provide evidence connecting phase interface dynamics with thermal distribution but also to simplify the laborious and time-consuming experimental setup and data-reduction procedures associated with infrared thermal imaging, thereby providing an effective solution for CHF diagnosis.
Simple Noisy Environment Augmentation for Reinforcement Learning

Data augmentation is a widely used technique for improving model performance in machine learning, particularly in computer vision and natural language processing. Recently, there has been increasing interest in applying augmentation techniques to reinforcement learning (RL) problems, with a focus on image-based augmentation. In this paper, we explore a set of generic wrappers designed to augment RL environments with noise and encourage agent exploration and improve training data diversity which are applicable to a broad spectrum of RL algorithms and environments. Specifically, we concentrate on augmentations concerning states, rewards, and transition dynamics and introduce two novel augmentation techniques. In addition, we introduce a noise rate hyperparameter for control over the frequency of noise injection. We present experimental results on the impact of these wrappers on return using three popular RL algorithms, Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), and Proximal Policy Optimization (PPO), across five MuJoCo environments. To support the choice of augmentation technique in practice, we also present analysis that explores the performance these techniques across environments. Lastly, we publish the wrappers in our noisyenv repository for use with gym environments.
Quantum phase transition between symmetry enriched topological phases in tensor-network states

Quantum phase transitions between different topologically ordered phases exhibit rich structures and are generically challenging to study in microscopic lattice models. In this work, we propose a tensor-network solvable model that allows us to tune between different symmetry enriched topological (SET) phases. Concretely, we consider a decorated two-dimensional toric code model for which the ground state can be expressed as a two-dimensional tensor-network state with bond dimension $D=3$ and two tunable parameters. We find that the time-reversal (TR) symmetric system exhibits three distinct phases (i) an SET toric code phase in which anyons transform non-trivially under TR, (ii) a toric code phase in which TR does not fractionalize, and (iii) a topologically trivial phase that is adiabatically connected to a product state. We characterize the different phases using the topological entanglement entropy and a membrane order parameter that distinguishes the two SET phases. Along the phase boundary between the SET toric code phase and the toric code phase, the model has an enhanced $U(1)$ symmetry and the ground state is a quantum critical loop gas wavefunction whose squared norm is equivalent to the partition function of the classical $O(2)$ model. By duality transformations, this tensor-network solvable model can also be used to describe transitions between SET double-semion phases and between $\mathbb{Z}_2\times\mathbb{Z}_2^T$ symmetry protected topological phases in two dimensions.
Approximating CKY with Transformers

We investigate the ability of transformer models to approximate the CKY algorithm, using them to directly predict a parse and thus avoid the CKY algorithm's cubic dependence on sentence length. We find that on standard constituency parsing benchmarks this approach achieves competitive or better performance than comparable parsers that make use of CKY, while being faster. We also evaluate the viability of this approach for parsing under random PCFGs. Here we find that performance declines as the grammar becomes more ambiguous, suggesting that the transformer is not fully capturing the CKY computation. However, we also find that incorporating additional inductive bias is helpful, and we propose a novel approach that makes use of gradients with respect to chart representations in predicting the parse, in analogy with the CKY algorithm being the subgradient of a partition function variant with respect to the chart.
Enhancing Precision with the Local Pivotal Method: A General Variance Reduction Approach

The local pivotal method (LPM) is a successful sampling method for taking well-spread samples from discrete populations. We show how the LPM can be utilized to sample from arbitrary continuous distributions and thereby give powerful variance reduction in general cases. The method creates an ``automatic stratification"on any continuous distribution, of any dimension, and selects a ``thin"well-spread sample. We demonstrate the simplicity, generality and effectiveness of the LPM with various examples, including Monte Carlo estimation of integrals, option pricing and stability estimation in non-linear dynamical systems. Additionally, we show how the LPM can be combined with other variance reduction techniques, such as importance sampling, to achieve even greater variance reduction. To facilitate the implementation of the LPM, we provide a quick start guide to using LPM in MATLAB and R, which includes sample code demonstrating how to achieve variance reduction with just a few lines of code.
Disk Wind–Driven Expanding Radio-emitting Shell in Tidal Disruption Events

We study the evolution of a nonrelativistically expanding thin shell in radio-emitting tidal disruption events (TDEs) based on a one-dimensional spherically symmetric model considering the effect of both a time-dependent mass-loss rate of the disk wind and the ambient mass distribution. The analytical solutions are derived in two extreme limits; one is the approximate solution near the origin in the form of the Taylor series, and the other is the asymptotic solution in which the ambient matter is dominant far away from the origin. Our numerical solutions are confirmed to agree with the respective analytical solutions. We find that no simple power law of the time solution exists in early to middle times because the mass-loss rate varies over time, affecting the shell dynamics. We also discuss the application of our model to the observed radio-emitting TDE, AT 2019dsg.
A framework for the emergence and analysis of language in social learning agents

Artificial neural networks (ANNs) are increasingly used as research models, but questions remain about their generalizability and representational invariance. Biological neural networks under social constraints evolved to enable communicable representations, demonstrating generalization capabilities. This study proposes a communication protocol between cooperative agents to analyze the formation of individual and shared abstractions and their impact on task performance. This communication protocol aims to mimic language features by encoding high-dimensional information through low-dimensional representation. Using grid-world mazes and reinforcement learning, teacher ANNs pass a compressed message to a student ANN for better task completion. Through this, the student achieves a higher goal-finding rate and generalizes the goal location across task worlds. Further optimizing message content to maximize student reward improves information encoding, suggesting that an accurate representation in the space of messages requires bi-directional input. This highlights the role of language as a common representation between agents and its implications on generalization capabilities.
Local regularity and finite time singularity for the generalized SQG equation on the half-plane

We show that the generalized SQG equation with $\alpha\in(0,\frac 14]$ is locally well-posed on the half-plane in spaces of bounded integrable solutions that are natural for its dynamic on domains with boundaries, and allow for some power growth of the solution derivative in the normal direction at the boundary. We also show existence of solutions exhibiting finite time blow-up in the whole local well-posedness parameter regime $\alpha\in(0,\frac 14]$, which is the first finite time singularity result for equations (as opposed to patch models) of this type. Moreover, we prove optimality of both these results by showing ill-posedness of the PDE in all the above spaces when $\alpha>\frac 14$.
A Deterministic Construction of a Large Distance Code from the Wozencraft Ensemble

We present an explicit construction of a sequence of rate $1/2$ Wozencraft ensemble codes (over any fixed finite field $\mathbb{F}_q$) that achieve minimum distance $\Omega(\sqrt{k})$ where $k$ is the message length. The coefficients of the Wozencraft ensemble codes are constructed using Sidon Sets and the cyclic structure of $\mathbb{F}_{q^{k}}$ where $k+1$ is prime with $q$ a primitive root modulo $k+1$. Assuming Artin's conjecture, there are infinitely many such $k$ for any prime power $q$.
On differential inclusions arising from some discontinuous systems

The paper deals with systems of ordinary differential equations containing in the right-hand side controls which are discontinuous in phase variables. These controls cause the occurrence of sliding modes. If one uses one of the well-known definitions of the solution of discontinuous systems, then the motion of an object while being on some surface can be described in terms of differential inclusions. With the help of the previously developed apparatus for solving differential inclusions, a method is constructed for finding the trajectories of a system moving in a such a mode. Since some of frequently used discontinuous controls contain nonsmooth functions of phase variables, the paper pays special attention to study the differential properties of such systems. At the end of the paper controls of a slightly different, in contrast to the classical, type are considered which have useful differential properties, and a method is constructed for solving systems with such controls considered both before hitting the required surface and moving in its vicinity.
Towards Causal Representation Learning and Deconfounding from Indefinite Data

Owing to the cross-pollination between causal discovery and deep learning, non-statistical data (e.g., images, text, etc.) encounters significant conflicts in terms of properties and methods with traditional causal data. To unify these data types of varying forms, we redefine causal data from two novel perspectives and then propose three data paradigms. Among them, the indefinite data (like dialogues or video sources) induce low sample utilization and incapability of the distribution assumption, both leading to the fact that learning causal representation from indefinite data is, as of yet, largely unexplored. We design the causal strength variational model to settle down these two problems. Specifically, we leverage the causal strength instead of independent noise as the latent variable to construct evidence lower bound. By this design ethos, The causal strengths of different structures are regarded as a distribution and can be expressed as a 2D matrix. Moreover, considering the latent confounders, we disentangle the causal graph G into two relation subgraphs O and C. O contains pure relations between observed variables, while C represents the relations from latent variables to observed variables. We implement the above designs as a dynamic variational inference model, tailored to learn causal representation from indefinite data under latent confounding. Finally, we conduct comprehensive experiments on synthetic and real-world data to demonstrate the effectiveness of our method.
"Oops, Did I Just Say That?" Testing and Repairing Unethical Suggestions of Large Language Models with Suggest-Critique-Reflect Process

As the popularity of large language models (LLMs) soars across various applications, ensuring their alignment with human values has become a paramount concern. In particular, given that LLMs have great potential to serve as general-purpose AI assistants in daily life, their subtly unethical suggestions become a serious and real concern. Tackling the challenge of automatically testing and repairing unethical suggestions is thus demanding. This paper introduces the first framework for testing and repairing unethical suggestions made by LLMs. We first propose ETHICSSUITE, a test suite that presents complex, contextualized, and realistic moral scenarios to test LLMs. We then propose a novel suggest-critic-reflect (SCR) process, serving as an automated test oracle to detect unethical suggestions. We recast deciding if LLMs yield unethical suggestions (a hard problem; often requiring human expertise and costly to decide) into a PCR task that can be automatically checked for violation. Moreover, we propose a novel on-the-fly (OTF) repairing scheme that repairs unethical suggestions made by LLMs in real-time. The OTF scheme is applicable to LLMs in a black-box API setting with moderate cost. With ETHICSSUITE, our study on seven popular LLMs (e.g., ChatGPT, GPT-4) uncovers in total 109,824 unethical suggestions. We apply our OTF scheme on two LLMs (Llama-13B and ChatGPT), which generates valid repair to a considerable amount of unethical ones, paving the way for more ethically conscious LLMs.
PersonaLLM: Investigating the Ability of GPT-3.5 to Express Personality Traits and Gender Differences

Extended Abstract Large Language Models (LLMs)—by way of their training and design—can be thought of as implicit computational models of humans [2] and studies are already exploring how these LLMs can be seen as effective proxies for speciﬁc human sub-populations [1]. This is largely because these models were designed to respond to prompts in a similar fashion to how a person would react—which makes them very appealing for applications like chatbots. In that context, an appealing property of LLMs is that their adaptivity to take on the character of different individuals based on certain traits, e.g. personality traits. Research shows that designing chatbots with curated personality proﬁles provides an improved personalized and engaging user experience [5]. Despite the need and clear applications, little work has been done to evaluate whether the behavior of LLM-generated personas can reﬂect certain personality traits accurately and consistently. In this work, we design a case study to address this gap. In this work, we consider studying the behavior of LLM-based simulated agents which refer to as LLM personas and aim to answer the following questions: When GPT-3.5 (text-davinci-003) is assigned a Big Five personality type, (1) do LLM personas consistently express the assigned personality traits in personality tests and writing tasks? (2) Does assigning a gender role have an additional effect on LLM personas’ behavior? To investigate these research questions, we build upon prior work in text-based personality analysis [4] by studying the ability of LLMs to generate content with curated personality traits. Speciﬁcally, we create 10 personas (5
Parameterizations of stabilizing compensators by using reduced-order observers

Reduced-order stabilizing compensators are parameterized in terms of doubly coprime factorizations having state-space representations based on a minimal-order observer and its dual observer. A free parameter of the compensators is restricted within strictly proper stable rational functions. The order of the compensators is lower than that of Nett's by the number of outputs or inputs. >
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank

Since its inception, the field of unbiased learning to rank (ULTR) has remained very active and has seen several impactful advancements in recent years. This tutorial provides both an introduction to the core concepts of the field and an overview of recent advancements in its foundations along with several applications of its methods. The tutorial is divided into four parts: Firstly, we give an overview of the different forms of bias that can be addressed with ULTR methods. Secondly, we present a comprehensive discussion of the latest estimation techniques in the ULTR field. Thirdly, we survey published results of ULTR in real-world applications. Fourthly, we discuss the connection between ULTR and fairness in ranking. We end by briefly reflecting on the future of ULTR research and its applications. This tutorial is intended to benefit both researchers and industry practitioners who are interested in developing new ULTR solutions or utilizing them in real-world applications.
A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification

Electrocardiogram (ECG) signals, which capture the heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, particularly for distinguishing among various types of arrhythmias and myocardial infarctions, is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification for enhancing the performance. In the initial stages of our approach, the preprocessing step is followed by the extraction of several significant features from the ECG signals. These extracted features are then provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.
AutoML-GPT: Automatic Machine Learning with GPT

AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human efforts in finding the right model architecture, optimization algorithm, and hyperparameters. Recent advances in large language models (LLMs) like ChatGPT show remarkable capabilities in various aspects of reasoning, comprehension, and interaction. Consequently, we propose developing task-oriented prompts and automatically utilizing LLMs to automate the training pipeline. To implement this concept, we present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyperparameters. AutoML-GPT dynamically takes user requests from the model and data cards and composes the corresponding prompt paragraph. Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct the experiments from data processing to model architecture, hyperparameter tuning, and predicted training log. By leveraging {\ours}'s robust language capabilities and the available AI models, AutoML-GPT can tackle numerous intricate AI tasks across various tasks and datasets. This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas. Extensive experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many AI tasks.
Image Captioners Sometimes Tell More Than Images They See

Image captioning, a.k. a. "image-to-text,"which generates descriptive text from given images, has been rapidly developing throughout the era of deep learning. To what extent is the information in the original image preserved in the descriptive text generated by an image captioner? To answer that question, we have performed experiments involving the classification of images from descriptive text alone, without referring to the images at all, and compared results with those from standard image-based classifiers. We have evaluate several image captioning models with respect to a disaster image classification task, CrisisNLP, and show that descriptive text classifiers can sometimes achieve higher accuracy than standard image-based classifiers. Further, we show that fusing an image-based classifier with a descriptive text classifier can provide improvement in accuracy.