Miracle-dz/UniFAR-FTUs huggingface dataset:
MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare

Federated learning (FL) has attracted increasing attention to building models without accessing raw user data, especially in healthcare. In real applications, different federations can seldom work together due to possible reasons such as data heterogeneity and distrust/inexistence of the central server. In this article, we propose a novel framework called MetaFed to facilitate trustworthy FL between different federations. obtains a personalized model for each federation without a central server via the proposed cyclic knowledge distillation. Specifically, treats each federation as a meta distribution and aggregates knowledge of each federation in a cyclic manner. The training is split into two parts: common knowledge accumulation and personalization. Comprehensive experiments on seven benchmarks demonstrate that without a server achieves better accuracy compared with state-of-the-art methods e.g., 10 %+ accuracy improvement compared with the baseline for physical activity monitoring dataset (PAMAP2) with fewer communication costs. More importantly, shows remarkable performance in real-healthcare-related applications.
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks

Deep Learning neural networks are pervasive, but traditional computer architectures are reaching the limits of being able to efficiently execute them for today’s large workloads. They are limited by the von Neumann bottleneck: the high cost in energy and latency incurred in moving data between memory and the compute engine. Today, special CMOS designs address this bottleneck. The next generation of computing hardware will need to eliminate or dramatically mitigate this bottleneck. We discuss how compute-in-memory can play an important part in this development. Here, a non-volatile memory based cross-bar architecture forms the heart of an engine that uses an analog process to parallelize the matrix vector multiplication operation, repeatedly used in all neural network workloads. The cross-bar architecture, at times referred to as a neuromorphic approach, can be a key hardware element in future computing machines. In the first part of this review we take a co-design view of the design constraints and the demands it places on the new materials and memory devices that anchor the cross-bar architecture. In the second part, we review what is knows about the different new non-volatile memory materials and devices suited for compute in-memory, and discuss the outlook and challenges.
Efficiently Learning Single-Arm Fling Motions to Smooth Garments

Recent work has shown that 2-arm"fling"motions can be effective for garment smoothing. We consider single-arm fling motions. Unlike 2-arm fling motions, which require little robot trajectory parameter tuning, single-arm fling motions are very sensitive to trajectory parameters. We consider a single 6-DOF robot arm that learns fling trajectories to achieve high garment coverage. Given a garment grasp point, the robot explores different parameterized fling trajectories in physical experiments. To improve learning efficiency, we propose a coarse-to-fine learning method that first uses a multi-armed bandit (MAB) framework to efficiently find a candidate fling action, which it then refines via a continuous optimization method. Further, we propose novel training and execution-time stopping criteria based on fling outcome uncertainty; the training-time stopping criterion increases data efficiency while the execution-time stopping criteria leverage repeated fling actions to increase performance. Compared to baselines, the proposed method significantly accelerates learning. Moreover, with prior experience on similar garments collected through self-supervision, the MAB learning time for a new garment is reduced by up to 87%. We evaluate on 36 real garments: towels, T-shirts, long-sleeve shirts, dresses, sweat pants, and jeans. Results suggest that using prior experience, a robot requires under 30 minutes to learn a fling action for a novel garment that achieves 60-94% coverage.
Solving small-scale clustering problems in approximate light-cone mocks

Realistic light-cone mocks are important in the clustering analyses of large galaxy surveys. For simulations where only the snapshots are available, it is common to create approximate light-cones by joining together the snapshots in spherical shells. We assess the two-point clustering measurements of central galaxies in approximate light-cones built from the Millennium-XXL simulation, which are constructed using different numbers of snapshots. The monopole and quadrupole of the real-space correlation function is strongly boosted on small scales below 1 h−1 Mpc, due to some galaxies being duplicated at the boundaries between snapshots in the light-cone. When more snapshots are used, the total number of duplicated galaxies is approximately constant, but they are pushed to smaller separations. The effect of this in redshift space is small, as long as the snapshots are cut into shells in real space. Randomly removing duplicated galaxies is able to reduce the excess clustering signal. Including satellite galaxies will reduce the impact of the duplicates, since many small-scale pairs come from satellites in the same halo. Galaxies that are missing from the light-cone at the boundaries can be added to the light-cone by having a small overlap between each shell. This effect will impact analyses that use very small-scale clustering measurements, and when using mocks to test the impact of fibre collisions.
I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences

Machine-Learning-as-a-Service (MLaaS) has become a widespread paradigm, making even the most complex Machine Learning models available for clients via, e.g., a pay-per-query principle. This allows users to avoid time-consuming processes of data collection, hyperparameter tuning, and model training. However, by giving their customers access to the (predictions of their) models, MLaaS providers endanger their intellectual property such as sensitive training data, optimised hyperparameters, or learned model parameters. In some cases, adversaries can create a copy of the model with (almost) identical behaviour using the the prediction labels only. While many variants of this attack have been described, only scattered defence strategies that address isolated threats have been proposed. To arrive at a comprehensive understanding why these attacks are successful and how they could be holistically defended against, a thorough systematisation of the field of model stealing is necessary. We address this by categorising and comparing model stealing attacks, assessing their performance, and exploring corresponding defence techniques in different settings. We propose a taxonomy for attack and defence approaches and provide guidelines on how to select the right attack or defence strategy based on the goal and available resources. Finally, we analyse which defences are rendered less effective by current attack strategies.
EyeNeRF

A unique challenge in creating high-quality animatable and relightable 3D avatars of real people is modeling human eyes, particularly in conjunction with the surrounding periocular face region. The challenge of synthesizing eyes is multifold as it requires 1) appropriate representations for the various components of the eye and the periocular region for coherent viewpoint synthesis, capable of representing diffuse, refractive and highly reflective surfaces, 2) disentangling skin and eye appearance from environmental illumination such that it may be rendered under novel lighting conditions, and 3) capturing eyeball motion and the deformation of the surrounding skin to enable re-gazing. These challenges have traditionally necessitated the use of expensive and cumbersome capture setups to obtain high-quality results, and even then, modeling of the full eye region holistically has remained elusive. We present a novel geometry and appearance representation that enables high-fidelity capture and photorealistic animation, view synthesis and relighting of the eye region using only a sparse set of lights and cameras. Our hybrid representation combines an explicit parametric surface model for the eyeball surface with implicit deformable volumetric representations for the periocular region and the interior of the eye. This novel hybrid model has been designed specifically to address the various parts of that exceptionally challenging facial area - the explicit eyeball surface allows modeling refraction and high frequency specular reflection at the cornea, whereas the implicit representation is well suited to model lower frequency skin reflection via spherical harmonics and can represent non-surface structures such as hair (i.e. eyebrows) or highly diffuse volumetric bodies (i.e. sclera), both of which are a challenge for explicit surface models. Tightly integrating the two representations in a joint framework allows controlled photoreal image synthesis and joint optimization of both the geometry parameters of the eyeball and the implicit neural network in continuous 3D space. We show that for high-resolution close-ups of the human eye, our model can synthesize high-fidelity animated gaze from novel views under unseen illumination conditions, allowing to generate visually rich eye imagery.
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge

Autonomous agents have made great strides in specialist domains like Atari games and Go. However, they typically learn tabula rasa in isolated environments with limited and manually conceived objectives, thus failing to generalize across a wide spectrum of tasks and capabilities. Inspired by how humans continually learn and adapt in the open world, we advocate a trinity of ingredients for building generalist agents: 1) an environment that supports a multitude of tasks and goals, 2) a large-scale database of multimodal knowledge, and 3) a flexible and scalable agent architecture. We introduce MineDojo, a new framework built on the popular Minecraft game that features a simulation suite with thousands of diverse open-ended tasks and an internet-scale knowledge base with Minecraft videos, tutorials, wiki pages, and forum discussions. Using MineDojo's data, we propose a novel agent learning algorithm that leverages large pre-trained video-language models as a learned reward function. Our agent is able to solve a variety of open-ended tasks specified in free-form language without any manually designed dense shaping reward. We open-source the simulation suite, knowledge bases, algorithm implementation, and pretrained models (https://minedojo.org) to promote research towards the goal of generally capable embodied agents.
Moment Generating Stirling Numbers and Applications

In this paper, we investigate certain combinatorial numbers, the moment generating Stirling numbers . They are a special case of Hsu’s generalized Stirling numbers and satisfy many more properties and combinatorial identities than are known in the general case. As application, we provide the computation of the moments and central moments of the phase type distribution, the recurrence time in Markov chains, the geometric distribution, the negative binomial distribution and of a class of distributions generalizing the negative binomial distribution. All computations can be performed in closed form without recursion. We also present the relationship to the Markov renewal process.
Thompson Sampling for Robust Transfer in Multi-Task Bandits

We study the problem of online multi-task learning where the tasks are performed within similar but not necessarily identical multi-armed bandit environments. In particular, we study how a learner can improve its overall performance across multiple related tasks through robust transfer of knowledge. While an upper confidence bound (UCB)-based algorithm has recently been shown to achieve nearly-optimal performance guarantees in a setting where all tasks are solved concurrently, it remains unclear whether Thompson sampling (TS) algorithms, which have superior empirical performance in general, share similar theoretical properties. In this work, we present a TS-type algorithm for a more general online multi-task learning protocol, which extends the concurrent setting. We provide its frequentist analysis and prove that it is also nearly-optimal using a novel concentration inequality for multi-task data aggregation at random stopping times. Finally, we evaluate the algorithm on synthetic data and show that the TS-type algorithm enjoys superior empirical performance in comparison with the UCB-based algorithm and a baseline algorithm that performs TS for each individual task without transfer.
Popular decision tree algorithms are provably noise tolerant

Using the framework of boosting, we prove that all impurity-based decision tree learning algorithms, including the classic ID3, C4.5, and CART, are highly noise tolerant. Our guarantees hold under the strongest noise model of nasty noise, and we provide near-matching upper and lower bounds on the allowable noise rate. We further show that these algorithms, which are simple and have long been central to everyday machine learning, enjoy provable guarantees in the noisy setting that are unmatched by existing algorithms in the theoretical literature on decision tree learning. Taken together, our results add to an ongoing line of research that seeks to place the empirical success of these practical decision tree algorithms on firm theoretical footing.
Cross-sectional explanatory power of ESG features

We systematically investigate the links between price returns and ESG features. We propose a cross-validation scheme with random company-wise validation to mitigate the relative initial lack of quantity and quality of ESG data, which allows us to use most of the latest and best data to both train and validate our models. Boosted trees successfully explain a single bit of annual price returns not accounted for in the traditional market factor. We check with benchmark features that ESG features do contain signiﬁcantly more information than basic fundamental features alone. The most relevant sub-ESG feature encodes controversies. Finally, we ﬁnd opposite eﬀects of better ESG scores on the price returns of small and large capitalization companies: better ESG scores are generally associated with larger price returns for the latter, and reversely for the former.
TeV Scale Modified Type-II Seesaw and Dark Matter in a Gauged $U(1)_{\rm B-L}$ Symmetric Model

Gauged U(1)B−L Symmetric Model Purusottam Ghosh, ∗ Satyabrata Mahapatra, † Nimmala Narendra, ‡ and Narendra Sahu § Regional Centre for Accelerator-based Particle Physics, Harish-Chandra Research Institute, HBNI, Chhatnag Road, Jhunsi, Allahabad 211 019, India Department of Physics, Indian Institute of Technology Hyderabad, Kandi, Sangareddy 502285, Telangana, India Theoretical Physics Division, Physical Research Laboratory, Ahmedabad 380009, India Abstract In an endeavor to explain the light neutrino masses and dark matter (DM) simultaneously, we study a gauged U(1)B−L extension of the standard model (SM). The neutrino masses are generated through a variant of type-II seesaw mechanism in which one of the scalar triplets has a mass in a scale that is accessible at the present generation colliders. Three SM singlet right chiral fermions χiR(i = e, μ, τ) with B − L charges -4, -4, +5 are invoked to cancel the B − L gauge anomalies and the lightest one among these three fermions becomes a viable DM candidate as their stability is guaranteed by a remnant Z2 symmetry to which U(1)B−L gauge symmetry gets spontaneously broken. Interestingly in this scenario, the neutrino mass and the co-annihilation of DM are interlinked through the breaking of U(1)B−L symmetry. Apart from giving rise to the observed neutrino mass and dark matter abundance, the model also predicts exciting signals at the colliders. Especially we see a significant enhancement in the production cross-section of the TeV scale doubly charged scalar in presence of the ZBL gauge boson. We discuss all the relevant constraints on model parameters from observed DM abundance and null detection of DM at direct and indirect search experiments as well as the constraints on the B − L gauge boson from recent colliders.
Feature and Parameter Selection in Stochastic Linear Bandits

We study two model selection settings in stochastic linear bandits (LB). In the first setting, which we refer to as feature selection, the expected reward of the LB problem is in the linear span of at least one of $M$ feature maps (models). In the second setting, the reward parameter of the LB problem is arbitrarily selected from $M$ models represented as (possibly) overlapping balls in $\mathbb R^d$. However, the agent only has access to misspecified models, i.e.,~estimates of the centers and radii of the balls. We refer to this setting as parameter selection. For each setting, we develop and analyze a computationally efficient algorithm that is based on a reduction from bandits to full-information problems. This allows us to obtain regret bounds that are not worse (up to a $\sqrt{\log M}$ factor) than the case where the true model is known. This is the best-reported dependence on the number of models $M$ in these settings. Finally, we empirically show the effectiveness of our algorithms using synthetic and real-world experiments.
Athlete Health Status Assessment Based on Edge Computing Model and Large-Scale Multimedia Health Data Analysis

This study explores the athlete's health status evaluation system based on an edge computing model and large-scale multimedia health data analysis in order to improve the assessment effect of athletes' current health state. In addition to that, this research builds a wireless signal propagation model in edge areas using bicubic polynomial interpolation, improves the accuracy of DEM data by using bicubic polynomial interpolation, and digitally represents the sports field by utilizing DEM data. In addition to that, the model and DEM data are utilized in this paper to realize the site selection process for base station networking. The experimental analysis demonstrates that the athletes' health status evaluation system suggested in this research, which is based on the edge computing model and the large-scale multimedia health data analysis, has a good effect on the assessment of athletes' health.
KAJIAN MENGENAI PENERIMAAN TEKNOLOGI DAN INFORMASI MENGGUNAKAN TECHNOLOGY ACCAPTANCE MODEL (TAM)

Salah satu kunci awal bagi keberhasilan implementasi teknologi informasi dan komunikasi dalam perusahaan adalah kemauan untuk menerima teknologi tersebut dikalangan pengguna. Salah satu metode pendekatan untuk memahami sikap pengguna terhadap teknologi adalah Technology Acceptance Model (TAM). Model TAM sediri mendefinisikan dua hal yang memengaruhi penerimaan pengguna terhadap teknologi yaitu persepsi pengguna terhadap manfaat dari teknologi dan kemudahan dalam menggunakan teknologi. Penelitian ini akan mengkaji perbaikan model awal yang dimulai dari model Theory of Reason Action (TRA) dan Theory Plannded Behaviour (TPB). Kata kunci: Technology Accaptance Model (TAM), Theory of Reason Action (TRA) dan Theory Plannded Behaviour (TPB) , Teknologi Informasi dan Komunikasi. Abstract One of the key starting point for the successful implementation of information and communication technologies in the enterprise is the willingness to accept these technologies among users. One method of approach to understanding the user’s attitude toward technology is the Technology Acceptance Model (TAM). Sediri TAM model defines two things that affect user acceptance of technology that is user perception of the benefits of technology and ease of use of technology. This study will assess the improvements that began early models of models Theory of Reason Action (TRA) and Theory Plannded Behaviour (TPB). Keywords: Technology Acceptance Model (TAM), Theory of Reason Action (TRA) and Theory Plannded Behaviour (TPB), Information and Communication Technology.
Smartphone‐based home workout program for shift‐work nurses working during the COVID‐19 pandemic

Abstract This study aimed to develop a smartphone‐based home workout program for shift‐work nurses to increase their levels of exercise and examine its effects on health (sleep disturbance, fatigue, musculoskeletal problems, and resilience) and nursing performance. For this quasiexperimental study with a nonequivalent control group, 54 shift‐work nurses were recruited from two general wards at a hospital in Korea and assigned to the intervention and control groups. Nurses in the intervention group were encouraged to exercise regularly using the home workout application for 18 weeks. For the first 12 weeks, text‐message counseling and environmental improvement were carried out; only environmental improvement was implemented in the remaining 12–18 weeks. The control group did not receive any intervention. After excluding dropouts, the data of the final 25 participants in the intervention group and 24 participants in the control group were analyzed. Compared with the control group, the intervention group showed statistically significant improvements in physical and psychological health. Hospital organizations could adopt a smartphone‐based home workout program to overcome obstacles to exercise, which could lead to positive health outcomes for shift‐work nurses.
Hierarchical regulation of Burkholderia glumae type III secretion system by GluR response regulator and Lon protease

Abstract Expression of type III secretion system (T3SS) genes, which are important for the virulence of phytopathogenic bacteria, is induced in the plant apoplastic environment or artificially amended growth conditions. Wild‐type Burkholderia glumae BGR1, which causes rice panicle blight, induced a hypersensitive response (HR) in tobacco plants, whereas the T3SS genes were not significantly expressed in the commonly used hrp induction medium. T3SS gene expression in B. glumae was dependent on HrpB, a well known T3SS gene transcriptional regulator. Here, we report a stepwise mechanism of T3SS gene regulation by the GluR response regulator and Lon protease in addition to HrpB‐mediated control of T3SS genes in B. glumae. The gluR mutant showed no HR in tobacco plants and exhibited attenuated virulence in rice plants. GluR directly activated hrpB expression, indicating that hrpB belongs to the GluR regulon. The lon mutation allowed high expression of the T3SS genes in nutrient‐rich media. Lon directly activated gluR expression but repressed hrpB expression, indicating that Lon acts as a regulator rather than a protease. However, the lon mutant failed to induce an HR and virulence, suggesting that Lon not only acts as a negative regulator, but also has an essential, yet to be determined role for T3SS. Our results demonstrate the involvement of the two‐component system response regulator GluR and Lon in T3SS gene regulation, providing new insight into the complex interplay mechanisms of regulators involved in T3SS gene expression in bacteria–plant interactions.
A FAST FUZZY-TUNED MULTI-OBJECTIVE OPTIMIZATION FOR SIZING PROBLEMS

The most recent approaches of multi-objective optimization constitute application of meta-heuristic algorithms for which, parameter tuning is still a challenge. The present work hybridizes swarm intelligence with fuzzy operators to extend crisp values of the main control parameters into especial fuzzy sets that are constructed based on a number of prescribed facts. Such parameter-less particle swarm optimization is employed as the core of a multi-objective optimization framework with a repository to save Pareto solutions. The proposed method is tested on a variety of benchmark functions and structural sizing examples. Results show that it can provide Pareto front by lower computational time in competition with some other popular multi-objective algorithms