Miracle-dz/UniFAR-FTUs huggingface dataset:
Charge doping into spin minority states mediates doubling of $T_\mathrm{C}$ in ferromagnetic CrGeTe$_3$

The recent discovery of the persistence of long-range magnetic order when van der Waals layered magnets are thinned towards the monolayer limit has provided a tunable platform for the engineering of novel magnetic structures and devices. Here, we study the evolution of the electronic structure of CrGeTe$_3$ as a function of electron doping in the surface layer. From angle-resolved photoemission spectroscopy, we observe spectroscopic fingerprints that this electron doping drives a marked increase in $T_\mathrm{C}$, reaching values more than double that of the undoped material, in agreement with recent studies using electrostatic gating. Together with density functional theory calculations and Monte Carlo simulations, we show that, surprisingly, the increased $T_\mathrm{C}$ is mediated by the population of spin-minority Cr $t_{2g}$ states, forming a half-metallic 2D electron gas at the surface. We show how this promotes a novel variant of double exchange, and unlocks a significant influence of the Ge -- which was previously thought to be electronically inert in this system -- in mediating Cr-Cr exchange.
Efficient calculation of the integral equation for simulating 2D TE scattering in a homogeneous medium using the Ewald method and a Gabor frame discretization

We utilize the domain integral equation formulation to simulate two-dimensional transverse electric scattering in a homogeneous medium and a summation of modulated Gaussian functions to approximate the dual Gabor window. Then we apply Ewald Green function transformation to separate the integrals related to x and z in the integral equation, which produce Gaussian functions. These Gaussian functions in the integrands can be integrated analytically, which greatly simplifies the calculation process. Finally, we discuss the convergence and the selection of the Ewald splitting parameter E.
Raising the Bar of AI-generated Image Detection with CLIP

Aim of this work is to explore the potential of pre-trained vision-language models (VLMs) for universal detection of AI-generated images. We develop a lightweight detection strategy based on CLIP features and study its performance in a wide variety of challenging scenarios. We find that, unlike previous belief, it is neither necessary nor convenient to use a large domain-specific dataset for training. On the contrary, by using only a handful of example images from a single generative model, a CLIP-based detector exhibits a surprising generalization ability and high robustness across several different architectures, including recent commercial tools such as Dalle-3, Midjourney v5, and Firefly. We match the SoTA on in-distribution data, and improve largely above it in terms of generalization to out-of-distribution data (+6% in terms of AUC) and robustness to impaired/laundered data (+13%). Our project is available at https://grip-unina.github. io/ClipBased-SyntheticImageDetection/
Online Influence Maximization: Concept and Algorithm

In this survey, we offer an extensive overview of the Online Influence Maximization (IM) problem by covering both theoretical aspects and practical applications. For the integrity of the article and because the online algorithm takes an offline oracle as a subroutine, we first make a clear definition of the Offline IM problem and summarize those commonly used Offline IM algorithms, which include traditional approximation or heuristic algorithms and ML-based algorithms. Then, we give a standard definition of the Online IM problem and a basic Combinatorial Multi-Armed Bandit (CMAB) framework, CMAB-T. Here, we summarize three types of feedback in the CMAB model and discuss in detail how to study the Online IM problem based on the CMAB-T model. This paves the way for solving the Online IM problem by using online learning methods. Furthermore, we have covered almost all Online IM algorithms up to now, focusing on characteristics and theoretical guarantees of online algorithms for different feedback types. Here, we elaborately explain their working principle and how to obtain regret bounds. Besides, we also collect plenty of innovative ideas about problem definition and algorithm designs and pioneering works for variants of the Online IM problem and their corresponding algorithms. Finally, we encapsulate current challenges and outline prospective research directions from four distinct perspectives.
Just add WATER: WebAssembly-based Circumvention Transports

As Internet censors rapidly evolve new blocking techniques, circumvention tools must also adapt and roll out new strategies to remain unblocked. But new strategies can be time consuming for circumventors to develop and deploy, and usually an update to one tool often requires significant additional effort to be ported to others. Moreover, distributing the updated application across different platforms poses its own set of challenges. In this paper, we introduce WATER (WebAssembly Transport Executables Runtime), a novel design that enables applications to use a WebAssembly-based application-layer (e.g., TLS) to wrap network connections and provide network transports. Deploying a new circumvention technique with WATER only requires distributing the WebAssembly Transport Module(WATM) binary and any transport-specific configuration, allowing dynamic transport updates without any change to the application itself. WATMs are also designed to be generic such that different applications using WATER can use the same WATM to rapidly deploy successful circumvention techniques to their own users, facilitating rapid interoperability between independent circumvention tools.
Galaxy Classification: A machine learning approach for classifying shapes using numerical data

The classification of galaxies as spirals or ellipticals is a crucial task in understanding their formation and evolution. With the arrival of large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers now have access to images of a vast number of galaxies. However, the visual inspection of these images is an impossible task for humans due to the sheer number of galaxies to be analyzed. To solve this problem, the Galaxy Zoo project was created to engage thousands of citizen scientists to classify the galaxies based on their visual features. In this paper, we present a machine learning model for galaxy classification using numerical data from the Galaxy Zoo[5] project. Our model utilizes a convolutional neural network architecture to extract features from galaxy images and classify them into spirals or ellipticals. We demonstrate the effectiveness of our model by comparing its performance with that of human classifiers using a subset of the Galaxy Zoo dataset. Our results show that our model achieves high accuracy in classifying galaxies and has the potential to significantly enhance our understanding of the formation and evolution of galaxies.
OPPORTUNITIES AND CHALLENGES FOR HARMONIZATION OF GLOBAL INDICATORS FOR THE CONVENTION ON BIOLOGICAL DIVERSITY AND THE RAMSAR CONVENTION ON WETLANDS

1. As part of his work in response to decision XI/3, paragraph 16, of the Conference of the Parties, the Executive Secretary is herewith making available for comment a submission from the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC). The work will help inform the next consideration of the development and use of indicators and associated monitoring systems by the Subsidiary Body on Scientific, Technical and Technological Advice as mandated in decision XI/3, paragraph 16. Comments may be sent directly to UNEP-WCMC by email to Philip.Bubb@unep-wcmc.org.
Management of low back pain accompanying sagittal plane pathologies in children: Spondylolysis/spondylolisthesis and Scheuermann’s disease

Background: Low back pain in childhood was underappreciated for a long time, but recent studies report higher prevalences, up to 70%. Two of the common causes are spondylolyis/spondylolisthesis and Scheuermann’s disease. These disorders are relevant in a way they both cause significant back pain, and may disrupt the sagittal spinal balance. Purpose: To present the current evidence on the diagnosis, natural history and treatment of these disorders with a special focus on sagittal spinal alignment. Methods: This study is conducted as a literature review. Results and Conclusions: Spondylolysis and low-grade spondylolisthesis have a benign course and are typically treated conservatively. When pars repair is indicated, pedicle screw-based techniques achieve more than 90% fusion with acceptable complication rates. High-grade spondylolisthesis, however, is frequently progressive. Surgical treatment involves fusion, which can be done in situ or after reduction. Reduction is useful for “unbalanced” patients to acquire sagittal spinopelvic balance, and it is important to distinguish these patients. Despite lowering the risk for pseudoarthrosis, reduction brings a risk for neurologic complications. With re-operation rates as high as 40%, these patients definitely require careful preoperative planning. Scheuermann’s disease generally causes back pain in addition to cosmetic discomfort during adolescence. If the kyphosis is lower than 60°, symptoms typically resolve into adulthood with conservative measures only. However, it must be kept in mind that these patients may experience problems with physical performance and have a lower quality of life even when the problem seems to have “resolved”. Severe kyphosis and intractable back pain are the most frequently referred surgical indications, and surgery typically involves fusion. Proper utilization of osteotomies and proper selection of the upper and lower fusion levels are of utmost importance to prevent complications in these patients.
Residents’ attitudes towards tourism development in a non-touristy destination: the moderating role of travel experience

ABSTRACT Residents’ perceptions of tourism development largely determine the success of a tourist destination. In recent years, the concept of Emotional Solidarity, that is, the affective bonds that people have with each other, has been used to explain the resident–tourist relationship. Emotional Solidarity encompasses three factors: welcoming nature, sympathetic understanding, and emotional closeness. The objective of this research is to examine residents’ perceptions about tourism development using the theory of Emotional Solidarity in the context of Rotuma, Fiji. Rotuma has little tourism development. We analyse 310 surveys completed by Rotuma residents. Using PLS-SEM model, we find residents’ emotional closeness with tourists and welcoming nature of tourists (but not sympathetic understanding of tourists) explained tourism’s contribution to the community and support for tourism development. Travel by residents strengthens the relationship between emotional closeness and welcoming nature to their perceptions of contribution to the community and support for tourism development.
A Graph Matching Approach to Tracking Neurons in Freely-Moving C. elegans

Motivation Recent advances in 3D microscopy allow for recording the neurons in freely-moving C. elegans at high frame rates. In order to read out calcium activity, it is necessary to track individual neurons from frame to frame. However, doing this by hand for tens of neurons in a single ten-minute recording requires more than a hundred hours. Moreover, most methods proposed in the literature for tracking neurons focus on immobilized or partially-immobilized worms and fail with freely-behaving worms. Results In this paper we present an approach based on graph matching for tracking fluorescently-marked neurons in freely-moving C. elegans. Neurites (and sometimes neurons) can be oversegmented into pieces at the preprocessing phase; our algorithm allows several segments to match the same reference neuron or neurite. We demon-strate our method on three recordings. We find that with five labeled frames we can typically track the neurons and pieces of neurites with over 75% accuracy, with more reliable annotations for the most distinctive neurons. Availability and Implementation The code and preprocessed data will be made available upon publication. Contact corinne.jones@epfl.ch
Top-down perceptual inference shaping the activity of early visual cortex

Deep discriminative models provide remarkable insights into hierarchical processing in the brain by predicting neural activity along the visual pathway. However, these models differ from biological systems in their computational and architectural properties. Unlike biological systems, they require teaching signals for supervised learning. Moreover, they rely on feed-forward processing of stimuli, which contrasts with the extensive top-down connections in the ventral pathway. Here, we address both issues by developing a hierarchical deep generative model and show that it predicts an extensive set of experimental results in the primary and secondary visual cortices (V1 and V2). We show that the widely documented nonlinear sensitivity of V2 neurons to texture statistics is a consequence of learning a hierarchical representation of natural images. Further, we show that top-down influences are inherent to inference in hierarchical generative models, and explain neuronal responses to illusory contours and systematic modulations of noise correlations in V1.
Assessment of machine-learning predictions for MED25 ACID domain interactions with transactivation domains

Human Mediator complex subunit MED25 binds transactivation domains (TADs) present in various cellular and viral proteins using two binding interfaces found on opposite sides of its ACID domain, and referenced as H1 and H2. Here, we use and compare deep learning methods to characterize Human MED25-TADs interfaces and assess the predicted models to published experimental data. For the H1 interface, AlphaFold produces predictions with high reliability scores that agree well with experimental data, while the H2 interface predictions appear inconsistent, preventing reliable binding modes. Despite these limitations, we experimentally assess the validity of Lana-1 and IE62 MED25 interface predictions. AlphaFold predictions also suggest the existence of a unique hydrophobic pocket for Arabidopsis MED25 ACID domain.
A PhyloFisher utility for nucleotide-based phylogenomic matrix construction; nucl_matrix_constructor.py

Phylogenies built from multiple genes have become a common component of evolutionary biology studies. Molecular phylogenomic matrices used to build multi-gene phylogenies can be built from either nucleotide or protein matrices. Nucleotide-based analyses are often more appropriate for addressing phylogenetic questions in evolutionarily shallow timescales (i.e., less than 100 million years) while protein-based analyses are often more appropriate for addressing deep phylogenetic questions. PhyloFisher is a phylogenomic software package written in Python3. The manually curated PhyloFisher database contains 240 protein-coding genes from 304 eukaryotic taxa. Here we present nucl_matrix_constructor. py, an expansion of the PhyloFisher starting database, and an update to PhyloFisher that maintains DNA sequences. This combination will allow users the ability to easily build nucleotide phylogenomic matrices while retaining the benefits of protein-based pre-processing used to identify contaminants and paralogy.
Identification of conserved gene expression programs activated in multiple modes of torpor across vertebrate clades

Torpor encompasses adaptations to diverse extreme environmental stressors such as hibernation, aestivation, brumation, and daily torpor. Here we introduce StrokeofGenus, an analytic pipeline that identifies distinct transcriptomic states and conservation of gene expression patterns across studies, tissues, and species. We use StrokeofGenus to study multiple and diverse forms of torpor from publicly available RNA-seq datasets that span eight species and two classes. We identify three transcriptionally distinct states during the cycle of heterothermia: euthermia, torpor, and interbout arousal. We also identify torpor-specific gene expression patterns that are conserved both across tissues and between species with over three hundred million years of evolutionary divergence. We further demonstrate the general conservation of gene expression patterns in multiple forms of torpor, implying a common evolutionary origin for this process. Although here we apply StrokeofGenus to analysis of torpor, it can be used to interrogate any other complex physiological processes defined by transient transcriptomic states. HIGHLIGHTS StrokeofGenus integrates orthologue annotation, non-negative matrix factorization, and transfer learning for cross-species analysis. StrokeofGenus identifies conserved topor-related gene expression patterns across divergent species and cell types. Heterothermia has three distinct transcriptomic states. Torpor-specific gene expression patterns are conserved between species and forms of torpor.
Robot to Play Video Games Together

This study proposes a communication robot that plays video games together with the user to create a fun atmosphere for a daily-use robot. In recent years, the lack of conversation in daily life has become a problem, and as a solution to this problem, a dialogue robot have attracted attention as a substitute for a human and as a means of encouraging daily speech. In this study, we propose a dialogue robot that plays a video game with a user in order to produce enjoyable daily communication with the robot. The proposed method not only controls the robot’s speech, but also controls the game development by manipulating the game characters. The proposed robot enhances a user’s enjoyment by adjusting the level of game manipulation to produce a competitive match and by speaking according to the game scene. A robot that produces speech that makes a player feel positive emotions is called a friendly robot. In order to examine the effects of these robots on a user’s enjoyment, we conducted an experiment with 30 subjects in which they were subjectively evaluated by questionnaires in two types of matches: with a friendly robot and alone. The results showed that robots were more enjoyable than playing alone.
Effects of the Behavior of a Para-operated Robot on the Impression of the Operator: a Preliminary Online Study Considering the Robot-operator Distance

While research and applications of operated social robots that can be used as operators’ avatars have been conducted, a para-operated social robot has also been proposed, in which the robot and its operator exist in the same space, and the operator, robot, and interlocutor can interact with each other. From the findings of human-human-robot interaction research, it is known that the robot’s behavior influences the interpersonal relationships of the people involved in the dialog. However, there is no knowledge of how the operator is perceived by the interlocutor when the operations by the operator are unveiled. This paper reports preliminary results from two online surveys conducted to investigate the following question considering the robot-operator distance; Does the impression of the operator improve in unveiled para-operation when the operator operates the robot and has it say favorable utterances? The results of the two video surveys conducted at different distances between the robot and the operator show that the impression of the operator was improved by the robot’s favorable humorous utterances when the distance was close, but not when the distance was further apart. We consider that the findings contribute to the potential application of unveiled para-operated robots and suggest the importance of considering the distance factor in the influence of robots on human-human relationships.
Influence of Stereotypic Humanoid Agents on Robotic Touches in Virtual Reality

Previous research has shown that many factors in a virtual environment influence the way, how we perceive interaction with humanoid agents. It is still unknown if visual stereotypes also translate into human-robot interaction scenarios. To investigate this relationship, we conducted a two-part user study with 26 participants using the Stereotype Content Model (SCM) to identify different stereotypical avatars and visual features in terms of their effect on the perception of robot touch. We found that humanoid avatars which resemble the robot and are perceived as warm in the SCM are more liked and preferred than cold avatars. We also found different effects of visual appearance on touch perception. This suggests that a humanoid-stereotpyical appearance of a touch-care robot in virtual reality can change the overall perception of the interaction. We propose the use of stereotypical warm avatars as described in the SCM for humanity-based applications.
Comparing a Mentalist and an Interactionist Approach for Trust Analysis in Human-Robot Interaction

Trust is an important aspect of a human-robot interaction (HRI) as it mitigates the performance of many activities. Users’ trust may be impacted when robots make mistakes. To be able to properly time trust-reparation actions, robots should detect trust variations during the interaction. There are very few computational models of trust for such a task. The existing ones relied on either Psychological or Sociological theories that gave place to different definitions and analysis tools. We can distinguish two main approaches in the trust literature: the mentalist and the interactionist one. In this paper, we compare both approaches for trust detection, and explore how the adoption of two different assessment tools on an HRI dataset may lead to different results. We identify criteria that set them apart, and provide guidelines on the possibilities that each approach offers depending on the target computational model of trust.
Leveraging Interactional Sociology for Trust Analysis in Multiparty Human-Robot Interaction

By leveraging Interactional Sociology theories, multimodal behavioral features and recurrent neural architectures, we incrementally build computational models for trust analysis in multiparty human-robot interactions (HRI). We show that the model’s performance improves when i) modeling group dynamics with different granularities (i.e. group member, dyadic, and group as a whole), and ii) modeling users-robot interactions as a question-answer sequence.
Championing Design Knowledge in Human-Drone Interaction Research

There are current methodological and epistemological gaps and struggles between Human-Drone Interaction research and the field of design. We highlight five HDI design considerations and suggest a non-exhaustive list of design methods to approach them. We present an appeal to a more diverse and inclusive study of drones where current research can be balanced with a deeply qualitative and critical understanding of these flying robots through embracing design knowledge.