Miracle-dz/UniFAR-FTUs huggingface dataset:
What Attracts Employees to Work Onsite in Times of Increased Remote Working?

COVID-19 pandemic has irreversibly changed the attitude towards office presence. While previously remote workers were met with skepticism and distrust, today the same applies to companies prohibiting remote working. Albeit many workspaces are half empty. In this paper, we offer insights into the role of the office, corporate policies and actions regarding remote work in eight companies: Ericsson, Knowit, SpareBank 1 Utvikling, Spotify, Storebrand, Telenor, Company-X, Company-Y, and their sites in Sweden, Norway and the UK. Our findings are twofold. First, we found that companies indeed struggle with office presence and a large share of corporate space (35-67%) is underutilized. Second, we found that the main motivator for office presence is Connection and community, followed by Material offerings, Preference and Duty. Finally, we summarize actionable advice to promote onsite work, which is likely to help many other companies to rejuvenate life in their offices.
Ecology of the leopard (Panthera pardus) in Bori Wildlife Sanctuary and Satpura National Park, India

The ecology of the leopard (Panthera pardus) was studied from 2002 to 2006 in the Bori Wildlife Sanctuary and Satpura National Park in Madhya Pradesh, India. Density estimates of the potential prey species of leopards and its sympatric carnivores, the tiger (Panthera tigris) and the dhole (Cuon alpinus) were made using the line-transect method annually from 2002 to 2005, and for three habitat types. The results obtained by vehicle transects were compared with those of foot transects for obtaining reliable density estimates. The food habits and prey preference of leopards, tigers and dholes were quantified. Leopard density estimates for three sites in Bori Satpura and one site in Rajasthan, the Sariska Tiger Reserve, were made using camera traps and the mark-recapture method. A predictive habitat suitability map for leopards using Environmental Niche Factor Analysis (ENFA) was made at two scales and its reliability was evaluated. The environmental variables important in describing the habitat for leopards were identified and the extent and location of potential leopard habitat available for conservation action in south-central Madhya Pradesh was quantified. Chital (Axis axis) density was higher in the moist deciduous and teak dominated habitats compared to the dry deciduous habitat. Sambar (Cervus unicolor) density was higher in the teak dominated habitat. The densities of nilgai (Boselaphus tragocamelus), wild pig (Sus scrofa) and muntjac (Muntiacus muntjak) for the three habitat types were not statistically different. Annual density was lower for all prey species in 2005 as compared to 2002. Sambar was the most important prey species in the leopard? s diet. It was also the most preferred prey species by leopards, as well as by tigers and dholes. Density of leopards was estimated at 7.3, 7.5, 8.0 and 9.3 per 100 km2 for the four samples in Satpura Tiger Reserve using the half MMDM method and 4.2, 4.6, 5.3 and 6.2 per 100 km2 for the full MMDM method. The estimates for the sampled area in Sariska Tiger Reserve using the two methods were 30.9 and 20.7 per 100 km2, respectively. The results of the ENFA model showed that habitat use by leopards in Satpura was strongly associated with moist and teak forests, as well as with most prey species and was weakly negatively associated with the distance to villages. At the larger scale, in south-central Madhya Pradesh, leopard habitat was positively associated with terrain ruggedness, sambar availability and percentage of forested areas. Approximately 11500 km2 of habitat in south-central Madhya Pradesh is likely to support leopard populations. The districts with the most optimal habitat were found to be Betul, Hoshangabad and Chhindwara, which have about 2000 km2 of contiguous habitat for leopard conservation. ( en )
Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection

As hate speech continues to proliferate on the web, it is becoming increasingly important to develop computational methods to mitigate it. Reactively, using black-box models to identify hateful content can perplex users as to why their posts were automatically flagged as hateful. On the other hand, proactive mitigation can be achieved by suggesting rephrasing before a post is made public. However, both mitigation techniques require information about which part of a post contains the hateful aspect, i.e., what spans within a text are responsible for conveying hate. Better detection of such spans can significantly reduce explicitly hateful content on the web. To further contribute to this research area, we organized HateNorm at HASOC-FIRE 2023, focusing on explicit span detection in English Tweets. A total of 12 teams participated in the competition, with the highest macro-F1 observed at 0.58.
Canonical triangulations of Dehn fillings of the Borromean rings link complement

The set of canonical decompositions of a cusped hyperbolic 3-manifold is a complete topological invariant. However, there are only a handful of infinite families for which canonical decompositions are known. In this paper, we find canonical triangulations for Dehn fillings of the Borromean rings link complement and two related manifolds obtained by half-twists. The underlying triangulations were shown to be geometric by Ham and Purcell. To obtain canonical triangulations, we show a local convexity result at every face of the geometric triangulation.
Orthogonal prediction of counterfactual outcomes

Orthogonal meta-learners, such as DR-learner, R-learner and IF-learner, are increasingly used to estimate conditional average treatment effects. They improve convergence rates relative to na\"{\i}ve meta-learners (e.g., T-, S- and X-learner) through de-biasing procedures that involve applying standard learners to specifically transformed outcome data. This leads them to disregard the possibly constrained outcome space, which can be particularly problematic for dichotomous outcomes: these typically get transformed to values that are no longer constrained to the unit interval, making it difficult for standard learners to guarantee predictions within the unit interval. To address this, we construct orthogonal meta-learners for the prediction of counterfactual outcomes which respect the outcome space. As such, the obtained i-learner or imputation-learner is more generally expected to outperform existing learners, even when the outcome is unconstrained, as we confirm empirically in simulation studies and an analysis of critical care data. Our development also sheds broader light onto the construction of orthogonal learners for other estimands.
Generating Drug Repurposing Hypotheses through the Combination of Disease-Specific Hypergraphs

The drug development pipeline for a new compound can last 10-20 years and cost over 10 billion. Drug repurposing offers a more time- and cost-effective alternative. Computational approaches based on biomedical knowledge graph representations have recently yielded new drug repurposing hypotheses. In this study, we present a novel, disease-specific hypergraph representation learning technique to derive contextual embeddings of biological pathways of various lengths but that all start at any given drug and all end at the disease of interest. Further, we extend this method to multi-disease hypergraphs. To determine the repurposing potential of each of the 1,522 drugs, we derive drug-specific distributions of cosine similarity values and ultimately consider the median for ranking. Cosine similarity values are computed between (1) all biological pathways starting at the considered drug and ending at the disease of interest and (2) all biological pathways starting at drugs currently prescribed against that disease and ending at the disease of interest. We illustrate our approach with Alzheimer's disease (AD) and two of its risk factors: hypertension (HTN) and type 2 diabetes (T2D). We compare each drug's rank across four hypergraph settings (single- or multi-disease): AD only, AD + HTN, AD + T2D, and AD + HTN + T2D. Notably, our framework led to the identification of two promising drugs whose repurposing potential was significantly higher in hypergraphs combining two diseases: dapagliflozin (antidiabetic; moved up, from top 32$\%$ to top 7$\%$, across all considered drugs) and debrisoquine (antihypertensive; moved up, from top 76$\%$ to top 23$\%$). Our approach serves as a hypothesis generation tool, to be paired with a validation pipeline relying on laboratory experiments and semi-automated parsing of the biomedical literature.
1953: Fermi's"little discovery"and the birth of the numerical experiment

The year 1953 is pivotal for computational physics: the first application of the Monte-Carlo method is published and calculations of the so-called Fermi-Pasta-Ulam-Tsingou experiment are started. It is the beginning of the massive use in the physical sciences of numerical methods implemented on electronic computers and a decisive step in the development of modern nonlinear dynamics. This will lead to an unpredictable development during the following 70 years. We briefly review the unfolding of these events and present some recent results that show how the issues raised are still relevant today
DSR-Diff: Depth Map Super-Resolution with Diffusion Model

Color-guided depth map super-resolution (CDSR) improve the spatial resolution of a low-quality depth map with the corresponding high-quality color map, benefiting various applications such as 3D reconstruction, virtual reality, and augmented reality. While conventional CDSR methods typically rely on convolutional neural networks or transformers, diffusion models (DMs) have demonstrated notable effectiveness in high-level vision tasks. In this work, we present a novel CDSR paradigm that utilizes a diffusion model within the latent space to generate guidance for depth map super-resolution. The proposed method comprises a guidance generation network (GGN), a depth map super-resolution network (DSRN), and a guidance recovery network (GRN). The GGN is specifically designed to generate the guidance while managing its compactness. Additionally, we integrate a simple but effective feature fusion module and a transformer-style feature extraction module into the DSRN, enabling it to leverage guided priors in the extraction, fusion, and reconstruction of multi-model images. Taking into account both accuracy and efficiency, our proposed method has shown superior performance in extensive experiments when compared to state-of-the-art methods. Our codes will be made available at https://github.com/shiyuan7/DSR-Diff.
Towards Pragmatic Awareness in Question Answering: A Case Study in Maternal and Infant Health

Questions posed by information-seeking users often contain implicit false or potentially harmful assumptions. In a high-risk domain such as maternal and infant health, a question-answering system must recognize these pragmatic constraints and go beyond simply answering user questions, examining them in context to respond helpfully. To achieve this, we study pragmatic inferences made when mothers ask questions about pregnancy and infant care. Some of the inferences in these questions evade detection by existing methods, risking the possibility of QA systems failing to address them which can have dangerous health and policy implications. We explore the viability of detecting inferences from questions using large language models and illustrate that informing existing QA pipelines with pragmatic inferences produces responses that can mitigate the propagation of harmful beliefs.
Predicting generalization performance with correctness discriminators

The ability to predict an NLP model's accuracy on unseen, potentially out-of-distribution data is a prerequisite for trustworthiness. We present a novel model that establishes upper and lower bounds on the accuracy, without requiring gold labels for the unseen data. We achieve this by training a discriminator which predicts whether the output of a given sequence-to-sequence model is correct or not. We show across a variety of tagging, parsing, and semantic parsing tasks that the gold accuracy is reliably between the predicted upper and lower bounds, and that these bounds are remarkably close together.
Parallel and Sequential Resources Networks

A large number of real and abstract systems involve the transformation of some basic resource into respective products under the action of multiple processing agents, which can be understood as multiple-agent production systems (MAP). At each discrete time instant, for each agent, a fraction of the resources is assumed to be kept, forwarded to other agents, or converted into work with some efficiency. The present work describes a systematic study of nine basic MAP architectures subdivided into two main groups, namely parallel and sequential distribution of resources from a single respective source. Several types of interconnections among the involved processing agents are also considered. The resulting MAP architectures are studied in terms of the total amount of work, the dispersion of the resources (states) among the agents, and the transition times from the start of operation until the respective steady state. Several interesting results are obtained and discussed, including the observation that some of the parallel designs were able to yield maximum work and minimum state dispersion, achieved at the expense of the transition time and use of several interconnections between the source and the agents. The results obtained for the sequential designs indicate that relatively high performance can be obtained for some specific cases.
FedFusion: Manifold-Driven Federated Learning for Multi-Satellite and Multi-Modality Fusion

Multi-Satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion representation of complex high-dimensional data under limited computational resources. Deep neural networks can reveal the underlying distribution of multimodal remote sensing data, but the in-orbit fusion of multimodal data is more difficult because of the limitations of different sensor imaging characteristics, especially when the multimodal data follow nonindependent identically distribution (Non-IID) distributions. To address this problem while maintaining classification performance, this article proposes a manifold-driven multi-modality fusion framework, FedFusion, which randomly samples local data on each client to jointly estimate the prominent manifold structure of shallow features of each client and explicitly compresses the feature matrices into a low-rank subspace through cascading and additive approaches, which is used as the feature input of the subsequent classifier. Considering the physical space limitations of the satellite constellation, we developed a multimodal federated learning (FL) module designed specifically for manifold data in a deep latent space. This module achieves iterative updating of the subnetwork parameters of each client through global weighted averaging, constructing a framework that can represent compact representations of each client. The proposed framework surpasses existing methods in terms of performance on three multimodal datasets, achieving a classification average accuracy of 94.35% while compressing communication costs by a factor of 4. Furthermore, extensive numerical evaluations of real-world satellite images were conducted on the orbiting edge computing architecture based on Jetson TX2 industrial modules, which demonstrated that FedFusion significantly reduced training time by 48.4 min (15.18%) while optimizing accuracy. The codes will be available at: https://github.com/LDXDU/FedFusion.
Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression

Redundant information transfer in a neural network can increase the complexity of the deep learning model, thus increasing its power consumption. We introduce in this paper a novel spiking neuron, termed Variable Spiking Neuron (VSN), which can reduce the redundant firing using lessons from biological neuron inspired Leaky Integrate and Fire Spiking Neurons (LIF-SN). The proposed VSN blends LIF-SN and artificial neurons. It garners the advantage of intermittent firing from the LIF-SN and utilizes the advantage of continuous activation from the artificial neuron. This property of the proposed VSN makes it suitable for regression tasks, which is a weak point for the vanilla spiking neurons, all while keeping the energy budget low. The proposed VSN is tested against both classification and regression tasks. The results produced advocate favorably towards the efficacy of the proposed spiking neuron, particularly for regression tasks.
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta

Effective user representations are pivotal in personalized advertising. However, stringent constraints on training throughput, serving latency, and memory, often limit the complexity and input feature set of online ads ranking models. This challenge is magnified in extensive systems like Meta's, which encompass hundreds of models with diverse specifications, rendering the tailoring of user representation learning for each model impractical. To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models. SUM leverages a few designated upstream user models to synthesize user embeddings from massive amounts of user features with advanced modeling techniques. These embeddings then serve as inputs to downstream online ads ranking models, promoting efficient representation sharing. To adapt to the dynamic nature of user features and ensure embedding freshness, we designed SUM Online Asynchronous Platform (SOAP), a latency free online serving system complemented with model freshness and embedding stabilization, which enables frequent user model updates and online inference of user embeddings upon each user request. We share our hands-on deployment experiences for the SUM framework and validate its superiority through comprehensive experiments. To date, SUM has been launched to hundreds of ads ranking models in Meta, processing hundreds of billions of user requests daily, yielding significant online metric gains and infrastructure cost savings.
Survey of Rigid Body Simulation with Extended Position Based Dynamics

Interactive real-time rigid body simulation is a crucial tool in any modern game engine or 3D authoring tool. The quest for fast, robust and accurate simulations is ever evolving. PBRBD (Position Based Rigid Body Dynamics), a recent expansion of PBD (Position Based Dynamics), is a novel approach for this issue. This work aims at providing a comprehensible state-of-the art comparison between Position Based methods and other real-time simulation methods used for rigid body dynamics using a custom 3D physics engine for benchmarking and comparing PBRBD (Position Based Rigid Body Dynamics), and some variants, with state-of-the-art simulators commonly used in the gaming industry, PhysX and Havok. Showing that PBRBD can produce simulations that are accurate and stable, excelling at maintaining stable energy levels, and allowing for a variety of constraints, but is limited in its handling of stable stacks of rigid bodies.
Gaussian Volterra Processes as Models of Electricity Markets

We introduce a non-Markovian model for electricity markets where the spot price of electricity is driven by several Gaussian Volterra processes, which can be e.g., fractional Brownian motions (fBms), Riemann-Liouville processes or Gaussian-Volterra driven Ornstein-Uhlenbeck processes. Since in energy markets the spot price is not a tradeable asset, due to the limited storage possibilities, forward contracts are considered as traded products. We ensure necessary and sufficient conditions for the absence of arbitrage that, in this kind of market, reflects the fact that the prices of the forward contracts are (Gaussian) martingales under a risk-neutral measure. Moreover, we characterize the market completeness in terms of the number of forward contracts simultaneously considered and of the kernels of the Gaussian-Volterra processes. Then, we formulate a portfolio optimization problem for an agent who invests in an electricity market, and we solve it explicitly in the case of CRRA utility functions. We also find closed formula for the price of options written on forward contracts, together with the hedging strategy. Finally, we provide a novel representation of Ornstein-Uhlenbeck (OU) processes driven by Gaussian Volterra processes. Also exploiting this result, we show analytically that, for some kinds of Gaussian-Volterra processes driving the spot prices, under conditions ensuring the absence of arbitrage, the market is complete.
Dancing with the stars: a review on stellar multiplicity

Stars like company. They are mostly formed in clusters and their lives are often altered by the presence of one or more companions. Interaction processes between components may lead to complex outcomes like Algols, blue stragglers, chemically altered stars, type Ia supernovae, as well as progenitors of gravitational wave sources, to cite a few. Observational astronomy has entered the era of big data, and thanks to large surveys like spatial missions Kepler, TESS, Gaia, and ground-based spectroscopic surveys like RAVE, Gaia-ESO, APOGEE, LAMOST, GALAH (to name a few) the field is going through a true revolution, as illustrated by the recent detection of stellar black holes and neutron stars as companions of massive but also low-mass stars. In this review, I will present why it is important to care about stellar multiples, what are the main large surveys in which many binaries are harvested, and finally present some features related to the largest catalogue of astrometric, spectroscopic and eclipsing binaries provided by the Non-Single Star catalogue of Gaia, which is, to date, the largest homogeneous catalogue of stellar binaries.
Shared Growth of Graph Neural Networks via Free-direction Knowledge Distillation

Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper teacher GNN into a shallower student GNN. However, it is often quite challenging to train a satisfactory deeper GNN due to the well-known over-parametrized and over-smoothing issues, leading to invalid knowledge transfer in practical applications. In this paper, we propose the first Free-direction Knowledge Distillation framework via reinforcement learning for GNNs, called FreeKD, which is no longer required to provide a deeper well-optimized teacher GNN. Our core idea is to collaboratively learn two shallower GNNs to exchange knowledge between them. As we observe that one typical GNN model often exhibits better and worse performances at different nodes during training, we devise a dynamic and free-direction knowledge transfer strategy that involves two levels of actions: 1) node-level action determines the directions of knowledge transfer between the corresponding nodes of two networks; and then 2) structure-level action determines which of the local structures generated by the node-level actions to be propagated. Additionally, considering that different augmented graphs can potentially capture distinct perspectives of the graph data, we propose FreeKD-Prompt that learns undistorted and diverse augmentations based on prompt learning for exchanging varied knowledge. Furthermore, instead of confining knowledge exchange within two GNNs, we develop FreeKD++ to enable free-direction knowledge transfer among multiple GNNs. Extensive experiments on five benchmark datasets demonstrate our approaches outperform the base GNNs in a large margin. More surprisingly, our FreeKD has comparable or even better performance than traditional KD algorithms that distill knowledge from a deeper and stronger teacher GNN.