Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast swap regret minimization and applications to approximate correlated equilibria

We give a simple and computationally efficient algorithm that, for any constant $\varepsilon>0$, obtains $\varepsilon T$-swap regret within only $T = \mathsf{polylog}(n)$ rounds; this is an exponential improvement compared to the super-linear number of rounds required by the state-of-the-art algorithm, and resolves the main open problem of [Blum and Mansour 2007]. Our algorithm has an exponential dependence on $\varepsilon$, but we prove a new, matching lower bound. Our algorithm for swap regret implies faster convergence to $\varepsilon$-Correlated Equilibrium ($\varepsilon$-CE) in several regimes: For normal form two-player games with $n$ actions, it implies the first uncoupled dynamics that converges to the set of $\varepsilon$-CE in polylogarithmic rounds; a $\mathsf{polylog}(n)$-bit communication protocol for $\varepsilon$-CE in two-player games (resolving an open problem mentioned by [Babichenko-Rubinstein'2017, Goos-Rubinstein'2018, Ganor-CS'2018]); and an $\tilde{O}(n)$-query algorithm for $\varepsilon$-CE (resolving an open problem of [Babichenko'2020] and obtaining the first separation between $\varepsilon$-CE and $\varepsilon$-Nash equilibrium in the query complexity model). For extensive-form games, our algorithm implies a PTAS for $\mathit{normal}$ $\mathit{form}$ $\mathit{correlated}$ $\mathit{equilibria}$, a solution concept often conjectured to be computationally intractable (e.g. [Stengel-Forges'08, Fujii'23]).
Perspectives from India: Challenges and Opportunities for Computational Tools to Enhance Confidence in Published Research

Over the past decade, a crisis of confidence in published scientific findings has catalyzed widespread response from the research community, particularly in the West. These responses have included policy discussions and changes to existing practice as well as computational infrastructure to support and evaluate research. Our work studies Indian researchers' awareness, perceptions, and challenges around research integrity. We explore opportunities for Artificial Intelligence (AI)-powered tools to evaluate reproducibility and replicability, centering cultural perspectives. We discuss requirements for such tools, including signals within papers and metadata to be included, and system hybridity (fully-AI vs. collaborative human-AI). We draw upon 19 semi-structured interviews and 72 follow-up surveys with researchers at universities throughout India. Our findings highlight the need for computational tools to contextualize confidence in published research. In particular, researchers prefer approaches that enable human-AI collaboration. Additionally, our findings emphasize the shortcomings of current incentive structures for publication, funding, and promotion.
Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates

Federated reinforcement learning (FedRL) enables agents to collaboratively train a global policy without sharing their individual data. However, high communication overhead remains a critical bottleneck, particularly for natural policy gradient (NPG) methods, which are second-order. To address this issue, we propose the FedNPG-ADMM framework, which leverages the alternating direction method of multipliers (ADMM) to approximate global NPG directions efficiently. We theoretically demonstrate that using ADMM-based gradient updates reduces communication complexity from ${O}({d^{2}})$ to ${O}({d})$ at each iteration, where $d$ is the number of model parameters. Furthermore, we show that achieving an $\epsilon$-error stationary convergence requires ${O}(\frac{1}{(1-\gamma)^{2}{\epsilon}})$ iterations for discount factor $\gamma$, demonstrating that FedNPG-ADMM maintains the same convergence rate as the standard FedNPG. Through evaluation of the proposed algorithms in MuJoCo environments, we demonstrate that FedNPG-ADMM maintains the reward performance of standard FedNPG, and that its convergence rate improves when the number of federated agents increases.
First Report of Kocuria kristinae in the Skin of a Cuban Boa (Epicrates angulifer)

During a routine clinical examination of a four-year-old female Cuban boa (Epicrates angulifer) belonging to a zoological park located in northern Portugal, a skin lesion was observed. A skin swab was taken. Bacteriological analysis conducted using the Vitek® 2 Compact system identified the presence of the bacteria species Kocuria kristinae, a new bacterial pathogen that may be a potential pathogen in wild animals. This K. kristinae strain was resistant to kanamycin, pradofloxacin, erythromycin, clindamycin, tetracycline, nitrofurantoin, and trimethoprim/sulphamethoxazole and was therefore classified as a multidrug-resistant bacterium. To the authors’ knowledge, this is the first time that K. kristinae has been described in the skin of a Cuban boa. This report serves as a cautionary warning about the importance of recognising and investigating the potential pathogenicity of this agent, as well as contributing to the development of strategies to prevent the spread of antibiotic-resistant microorganisms.
IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation

Skin lesion segmentation plays a crucial role in the computer-aided diagnosis of melanoma. Deep Learning models have shown promise in accurately segmenting skin lesions, but their widespread adoption in real-life clinical settings is hindered by their inherent black-box nature. In domains as critical as healthcare, interpretability is not merely a feature but a fundamental requirement for model adoption. This paper proposes IARS SegNet an advanced segmentation framework built upon the SegNet baseline model. Our approach incorporates three critical components: Skip connections, residual convolutions, and a global attention mechanism onto the baseline Segnet architecture. These elements play a pivotal role in accentuating the significance of clinically relevant regions, particularly the contours of skin lesions. The inclusion of skip connections enhances the model's capacity to learn intricate contour details, while the use of residual convolutions allows for the construction of a deeper model while preserving essential image features. The global attention mechanism further contributes by extracting refined feature maps from each convolutional and deconvolutional block, thereby elevating the model's interpretability. This enhancement highlights critical regions, fosters better understanding, and leads to more accurate skin lesion segmentation for melanoma diagnosis.
SELECTING TOPSOIL SUBSTITUTES FOR FORESTRY MINE SOILS 1

The Forestry Reclamation Approach is a five-step system for reclaiming mined lands to forests. Step 1 of the FRA involves creating a suitable rooting medium for good tree growth using topsoil, weathered sandstone and/or the best available material. Several types of overburden types can be selected to place on the surface as growth media. These spoil types include weathered brown sandstone and unweathered rock materials including sandstones, siltstones, shales, and mixtures of these materials. When sufficient topsoil is not salvageable, reclamation scientists often recommend that, when available, weathered sandstone should be considered as the "best available" topsoil substitute material. Here, we review the scientific evidence that supports such recommendations. Several studies have shown that tree survival was not significantly different among spoil types. Weathered brown sandstone, unweathered gray sandstone, siltstone and shale materials all produced good tree survival (>70%) when compaction and competitive ground covers were reduced. However, growth for most trees (as measured by height, diameter, and volume) was usually significantly greater in weathered brown materials than in unweathered sandstones, siltstones, shales, and mixed spoils. At one site in West Virginia five years after planting, a 10-fold difference in tree volume was found between these two spoil types. Similar results have been found with other studies across Appalachian surface mines. Based on the results of studies summarized herein, the use of weathered brown sandstone is generally recommended, along with topsoil materials when available, to be placed on the surface on sites where hardwood tree species are being planted for forestry post-mining land uses. Weathered brown sandstone spoil materials have a pH, soluble salt content, fine earth content well suited for trees, and sufficient nutrient supplying and water holding capacity that results in superior tree growth compared to other spoil types. The brown sandstone material more closely resembles the native forest soil than the unweathered gray materials.
Development of Small Signal Model and Stability Analysis of PV-Grid Integration System for EV Charging Application

In this article, grid interactive photovoltaic (PV) system is designed for an electric vehicle (EV) charging application, and the stability of the system is analyzed. The small signal model for the system is derived by averaging and linearizing the state space equations, and the condition for stable operation of PV-integrated charger system is identified from the transfer functions. The proposed charger system implements a coordinated control between the converters to maintain a power balance between the sources and load. System stability is examined using root-locus plots and in addition, the controller is designed to improve the overall stability and reliability of the system. The proposed method provides a general framework for modeling EV charging systems which also details the importance of deriving the model with multiple energy sources. Further, proposed topology has bidirectional capability, which transfers excess PV power to the grid during off-charging hours. The efficacy of the proposed method is verified using the MATLAB Simulink environment for the different scenarios, i.e., variation in the irradiation and disturbances in the grid voltage. The experimental study is conducted on a 1.5-kW laboratory prototype using a low-cost digital signal processing controller (launchpad TMS320F28027F) and the measured results authenticate the simulation findings.
CreoleVal: Multilingual Multitask Benchmarks for Creoles

Creoles represent an under-explored and marginalized group of languages, with few available resources for NLP research. While the genealogical ties between Creoles and other highly-resourced languages imply a significant potential for transfer learning, this potential is hampered due to this lack of annotated data. In this work we present CreoleVal, a collection of benchmark datasets spanning 8 different NLP tasks, covering up to 28 Creole languages; it is an aggregate of brand new development datasets for machine comprehension, relation classification, and machine translation for Creoles, in addition to a practical gateway to a handful of preexisting benchmarks. For each benchmark, we conduct baseline experiments in a zero-shot setting in order to further ascertain the capabilities and limitations of transfer learning for Creoles. Ultimately, the goal of CreoleVal is to empower research on Creoles in NLP and computational linguistics. We hope this resource will contribute to technological inclusion for Creole language users around the globe.
A novel characterization of structures in smooth regression curves: from a viewpoint of persistent homology

We characterize structures such as monotonicity, convexity, and modality in smooth regression curves using persistent homology. Persistent homology is a key tool in topological data analysis that detects higher dimensional topological features such as connected components and holes (cycles or loops) in the data. In other words, persistent homology is a multiscale version of homology that characterizes sets based on the connected components and holes. We use super-level sets of functions to extract geometric features via persistent homology. In particular, we explore structures in regression curves via the persistent homology of super-level sets of a function, where the function of interest is - the first derivative of the regression function. In the course of this study, we extend an existing procedure of estimating the persistent homology for the first derivative of a regression function and establish its consistency. Moreover, as an application of the proposed methodology, we demonstrate that the persistent homology of the derivative of a function can reveal hidden structures in the function that are not visible from the persistent homology of the function itself. In addition, we also illustrate that the proposed procedure can be used to compare the shapes of two or more regression curves which is not possible merely from the persistent homology of the function itself.
AMIR: Automated MisInformation Rebuttal - A COVID-19 Vaccination Datasets based Recommendation System

Misinformation has emerged as a major societal threat in recent years in general; specifically in the context of the COVID-19 pandemic, it has wrecked havoc, for instance, by fuelling vaccine hesitancy. Cost-effective, scalable solutions for combating misinformation are the need of the hour. This work explored how existing information obtained from social media and augmented with more curated fact checked data repositories can be harnessed to facilitate automated rebuttal of misinformation at scale. While the ideas herein can be generalized and reapplied in the broader context of misinformation mitigation using a multitude of information sources and catering to the spectrum of social media platforms, this work serves as a proof of concept, and as such, it is confined in its scope to only rebuttal of tweets, and in the specific context of misinformation regarding COVID-19. It leverages two publicly available datasets, viz. FaCov (fact-checked articles) and misleading (social media Twitter) data on COVID-19 Vaccination.
Can we quantify trust? Towards a trust-based resilient SIoT network

The emerging yet promising paradigm of the Social Internet of Things (SIoT) integrates the notion of the Internet of Things with human social networks. In SIoT, objects, i.e., things , have the capability to socialize with the other objects in the SIoT network and can establish their social network autonomously by modeling human behaviour. The notion of trust is imperative in realizing these characteristics of socialization in order to assess the reliability of autonomous collaboration. The perception of trust is evolving in the era of SIoT as an extension to traditional security triads in an attempt to offer secure and reliable services, and is considered as an imperative aspect of any SIoT system for minimizing the probable risk of autonomous decision-making. This research investigates the idea of trust quantification by employing trust measurement in terms of direct trust, indirect trust as a recommendation, and the degree of the SIoT relationships in terms of social similarities (community-of-interest, friendship, and co-work relationships). A weighted sum approach is subsequently employed to synthesize all the trust features in order to ascertain a single trust score. The experimental evaluation demonstrates the effectiveness of the proposed model in segregating the trustworthy and the untrustworthy objects, and illustrates the superior performance of the proposed trust model over state-of-the-art trust models.
Video-Helpful Multimodal Machine Translation

Existing multimodal machine translation (MMT) datasets consist of images and video captions or instructional video subtitles, which rarely contain linguistic ambiguity, making visual information ineffective in generating appropriate translations. Recent work has constructed an ambiguous subtitles dataset to alleviate this problem but is still limited to the problem that videos do not necessarily contribute to disambiguation. We introduce EVA (Extensive training set and Video-helpful evaluation set for Ambiguous subtitles translation), an MMT dataset containing 852k Japanese-English (Ja-En) parallel subtitle pairs, 520k Chinese-English (Zh-En) parallel subtitle pairs, and corresponding video clips collected from movies and TV episodes. In addition to the extensive training set, EVA contains a video-helpful evaluation set in which subtitles are ambiguous, and videos are guaranteed helpful for disambiguation. Furthermore, we propose SAFA, an MMT model based on the Selective Attention model with two novel methods: Frame attention loss and Ambiguity augmentation, aiming to use videos in EVA for disambiguation fully. Experiments on EVA show that visual information and the proposed methods can boost translation performance, and our model performs significantly better than existing MMT models. The EVA dataset and the SAFA model are available at: https://github.com/ku-nlp/video-helpful-MMT.git.
A buoyancy profile for the Earth′s core

SUMMARY We investigate the thermal and chemical buoyancy forces that drive convection in the Earth's liquid outer core and derive a radial buoyancy profile that can be used in geodynamo models. We assume the core is well mixed, adiabatic and cools as a result of mantle convection. The buoyancy profile is developed for a Boussinesq fluid and incorporates secular cooling, latent heat release at the inner core boundary, radiogenic heating, the effect of the adiabat, and compositional buoyancy due to inner core freezing. Surprisingly, these complex effects can be modelled accurately by a simple combination of bottom heating and near-uniform heat sinks, which is implemented using a cotemperature formulation that converts compositional effects into effective thermal effects. The relative importance of internal and bottom heating is then defined by just two parameters, the cooling rate at the core–mantle boundary (CMB) and the uniform rate of internal radiogenic heat production, both of which can be obtained from core evolution calculations. We vary these parameters in geodynamo models and compare basic features of the generated fields with the geomagnetic field; in this manner we link core evolution models, geodynamo simulations and geomagnetic observations. We consider three end-member scenarios for core evolution: (1) rapid cooling and a young inner core; (2) moderate cooling and neutral stability at the CMB; (3) slow cooling and enough radiogenic heating to allow the inner core to be 3.5 Gyr old. We find that compositional buoyancy dominates thermal buoyancy everywhere except near the CMB, even with large amounts of radiogenic heating, and buoyancy forces are far larger at depth than higher up. Reducing the cooling rate and increasing radiogenic heating reduces the drop in the superadiabatic gradient between the inner and outer boundaries: for rapid cooling the drop is by a factor 50; for slow cooling it is a factor of 5. We demonstrate the effects of these different buoyancy profiles in numerical simulations as a function of the Rayleigh number. At low Rayleigh number the internal velocity and magnetic fields vary between the core evolution scenarios, but these differences do not affect the surface field. Significant differences in the surface field emerge when the Rayleigh number is sufficiently large. For rapid cooling we find dipolar magnetic fields in the time average that reverse and are dominated by large-scale features at high latitudes. Moderate cooling results in magnetic fields that are stable and dominantly dipolar. Slow cooling produces multipolar magnetic fields that reverse very frequently. This preliminary study suggests that the generated fields are sufficiently different that geodynamo simulations together with geomagnetic observations could be used to discriminate between different core evolution scenarios.
Harnessing collective radiative phenomena on a photonic Kagome lattice

Photonic lattices enable experimental exploration of transport and localization phenomena, two of the major goals in physics and technology. In particular, the optical excitation of some lattice sites, which evanescently couple to a lattice array, emulates radiation processes in structured reservoirs, a fundamental subject in quantum optics. Moreover, the simultaneous excitation of two sites simulates collective phenomena, leading to phase-controlled enhanced or suppressed radiation, namely super- and subradiance. This work presents an experimental study of collective radiative processes on a photonic Kagome lattice. A single or simultaneous—in-phase or out-of-phase—excitation of the outlying sites controls the radiation dynamics. Specifically, we demonstrate a controllable transition between a fully localized profile at the two outlying sites and a completely dispersed state into the quasi-continuum. Our result presents photonic lattices as a platform to emulate and experimentally explore quantum optical phenomena in two-dimensional structured reservoirs, while harnessing such phenomena for controlling transport dynamics and implementing all-optical switching devices.
Unveiling the Evolution of Mobile Networks: From 1G to 7G

The evolution of cellular networks has played a pivotal role in shaping the modern telecommunications landscape. This paper explores the journey of cellular network generations, beginning with the introduction of Japan's first commercial 1G network by Nippon Telegraph and Telephone (NTT) Corporation in 1979. This analog wireless network quickly expanded to become the country's first national 1G network within a remarkably short period. The transition from analog to digital networks marked a significant turning point in the wireless industry, enabled by advancements in MOSFET (Metal-Oxide-Semiconductor Field Effect Transistor) technology. MOSFET, originally developed at Bell Labs in 1959, underwent modifications to suit cellular networks in the early 1990s, facilitating the shift to digital wireless mobile networks. The advent of the 2G generation brought forth the first commercial digital cellular network in 1991, sparking recognition among manufacturers and mobile network operators of the importance of robust networks and efficient architecture. As the wireless industry continued to experience exponential growth, the significance of effective network infrastructure became increasingly evident. In this research, our aim is to provide a comprehensive overview of the entire spectrum of cellular network generations, ranging from 1G to the potential future of 7G. By tracing the evolution of these networks, we aim to shed light on the transformative developments that have shaped the telecommunications landscape and explore the possibilities that lie ahead in the realm of cellular technology.