Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Stellar Atmospheric Parameters with Light Gradient Boosting Machine Algorithm and Principal Component Analysis

In this paper, we propose a new method to estimate stellar atmospheric parameters with photometric data, which is based on principal component analysis (PCA) and light gradient boosting machine (LightGBM) algorithms. We first use PCA to transform all band photometric data (u, v, g, r, i, and z) and then utilize LightGBM to estimate stellar atmospheric parameters. The experimental results show that the root mean square errors of the method for estimating the effective temperature, surface gravity, and metallicity are 90 K, 0.40 dex, and 0.20 dex, respectively. We then compare PCA + LightGBM with the original photometry data (OPD) + LightGBM and the color index data (CID) + LightGBM. The experimental results show that the performance of PCA + LightGBM is better than that of CID + LightGBM and OPD + LightGBM, and PCA + LightGBM can solve the problems of model instability and inaccurate estimation results caused by direct use of OPD or CID as input for LightGBM. We believe the new features obtained by PCA can be used on photometric data collected by the Chinese Space Station Telescope.
Focusing on transient chaos

Recent advances in the field of complex, transiently chaotic dynamics are reviewed, based on the results published in the focus issue of J. Phys. Complex. on this topic. One group of achievements concerns network dynamics where transient features are intimately related to the degree and stability of synchronization, as well as to the network topology. A plethora of various applications of transient chaos are described, ranging from the collective motion of active particles, through the operation of power grids, cardiac arrhythmias, and magnetohydrodynamical dynamos, to the use of machine learning to predict time evolutions. Nontraditional forms of transient chaos are also explored, such as the temporal change of the chaoticity in the transients (called doubly transient chaos), as well as transients in systems subjected to parameter drift, the paradigm of which is climate change.
Concrete Analysis of Approximate Ideal-SIVP to Decision Ring-LWE Reduction

A seminal 2013 paper by Lyubashevsky, Peikert, and Regev proposed basing post-quantum cryptography on ideal lattices and supported this proposal by giving a polynomial-time security reduction from the approximate Shortest Independent Vectors Problem (SIVP) to the Decision Learning With Errors (DLWE) problem in ideal lattices. We give a concrete analysis of this multi-step reduction. We find that the tightness gap in the reduction is so great as to vitiate any meaningful security guarantee, and we find reasons to doubt the feasibility in the foreseeable future of the quantum part of the reduction. In addition, when we make the reduction concrete it appears that the approximation factor in the SIVP problem is far larger than expected, a circumstance that causes the corresponding approximate-SIVP problem most likely not to be hard for proposed cryptosystem parameters. We also discuss implications for systems such as Kyber and SABER that are based on module-DLWE.
Personalization of Mechanical Ventilation Treatment using Deep Conservative Reinforcement Learning

Mechanical ventilation is a key form of life support for patients with pulmonary impairment. Nonetheless, the optimal treatment regime is often unknown, leading to sub-optimal care and increased risks of complications. This work aims to develop a decision support tool to personalize mechanical ventilation. We present DeepVent, an off-policy deep reinforcement learning model that determines the best ventilator settings throughout a patient’s stay. We evaluate our model using Fit-ted Q Evaluation, and show that it is predicted to outperform physicians. Moreover, we address the challenge of policy value overestimation in out-of-distribution settings using Conservative Q-Learning and show that it leads to safe recommendations for patients. We also design an intermediate reward based on the Apache II score to further improve our model’s performance.
Just One Hour Reduction in Online Gaming Time May Enhance Mental Health in Indian University Students: A COVID Pandemic Study

Background: College students have taken to massive multiplayer online games (MMOG) for entertainment during this COVID pandemic. However, it could jeopardize their mental health risk if unregulated. Aim: This study aims to examine the relationship between gaming hours and key components of mental health viz self-compassion and online/offline integration, along with the role of time as a moderator in the relationship. Methods: A sample of 515 students who play MMOGs completed a set of standardized questionnaires. Descriptive, inferential analyses and moderated multiple regression were performed to study these relationships. Result: The results demonstrated a positive correlation between low game time, self-compassion and integration of online/offline life. Gamers with high self-compassion devoted lesser number of hours and tended to be well integrated in terms of their identity, social relations and functions in both online and offline lives as compared to gamers with low self-compassion who played for extensive hours. Conclusion: Limiting the game time by a couple of hours and incorporation of self-compassion intervention can evade the risks of gaming addiction and other related psychological disorders © This work is licensed under a Creative Commons Attribution-No Derivative Works 2.5 India License
An Improved Hybrid System for The Prediction of Debit and Credit Card Fraud

This work presents a design for the prediction of debit card fraud using a hybrid approach that involves generating synthetic data from a sample of real transaction data using the RtoSynR approach. The generated synthetic data is combined with the available sample of real data to form RtoSynR data and used to train a Genetic Algorithm optimized Random Forests model. The built model is then used to predict the genuineness of incoming transactions. The system used Object Oriented Analysis and Design and was designed with Python programming language while the synthetic data generation module was implemented by integrating R into a python designed interface. The various tests conducted with the model showed an improvement in the accuracy of the prediction over usual Random Forests model with real data bringing down the total number of misclassifications in the system from 2504 to 9.
How Practical Are Fault Injection Attacks, Really?

Fault injection attacks (FIA) are a class of active physical attacks, mostly used for malicious purposes such as extraction of cryptographic keys, privilege escalation, attacks on neural network implementations. There are many techniques that can be used to cause the faults in integrated circuits, many of them coming from the area of failure analysis. In this paper we tackle the topic of practicality of FIA. We analyze the most commonly used techniques that can be found in the literature, such as voltage/clock glitching, electromagnetic pulses, lasers, and Rowhammer attacks. To summarize, FIA can be mounted on most commonly used architectures from ARM, Intel, AMD, by utilizing injection devices that are often below the thousand dollar mark. Therefore, we believe these attacks can be considered practical in many scenarios, especially when the attacker can physically access the target device.
Hearables as a Gateway to Hearing Health

Copyright © 2022 by Korean Society of Otorhinolaryngology-Head and Neck Surgery. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Clinical and Experimental Otorhinolaryngology 2022 March 4 [Epub ahead of print] https://doi.org/10.21053/ceo.2021. 01662
Optimal Receivers Location for Passive Indoor Positioning Based on BLE

Indoor Positioning Systems are attracting many researchers, being Bluetooth Low Energy gaining some attention recently. The recent need to track people, ensuring low density of people in some areas, made the research community to provide unintrusive tracking. In contrast to traditional direct � ngerprinting, where the user utilizes their mobile to estimate its location based on the BLE infrastructure, we propose the other way around. The users wear a beacon which is sensed by the monitors deployed and inverse � ngerprints are generated for a particular object of interest. We propose in this paper a simulation-based approach to retrieve the most interesting monitor distribution, providing a real-world validation. The results show “passive” positioning errors of 3 meters in an o � ce sensed with 5 monitors.
An Approach To Multi-Level Encryption

: In this era, the most important thing for user as well as for an organization is network security where data of most of companies/organization is stored on clouds. So we must have to find a solution so that data must be safe during network transmission. In this regards most efficient way is to encrypt the data which we transmit from source to destination. Then at destination again decrypt the original data form encrypted data. Some of the data is more critical like some government data or military communication, banking transactions etc. When data falls in wrong hand may leads to big devastating. In this paper, a solution for above situation a multi-level encryption is given. In this multilevel encryption, data is more secure than the conventional encryption which involves multiple rounds of encryption with same or different keys and this make the algorithm complex and more powerful.
Complex spastic paraplegia as a neurological phenotype in an opa1 patient

https://doi.org/10.11606/issn.2176-7262.rmrp. 2021.182946 Introduction: Autosomal dominant optic atrophy (ADOA) is one of the most common forms of inherited optic atrophies and is caused by mutations in the OPA1 gene. Patients affected by this disease usually present visual loss in the first decade of life, and may present extra-ophthalmologic manifestations over the years, configuring a syndrome called OPA1 plus or ADOA-plus. Objectives: to report the case of a patient with ADOA-plus syndrome, establishing correlations with cases described in the literature, Case report: a 30-year-old female patient was referred for evaluation of progressive optic atrophy associated with symptoms of peripheral neuropathy. At two years of age, she was diagnosed with partial visual loss during a childcare visit. She reported no other associated symptoms during childhood and adolescence. At the age of 20, she presented with difficulty walking, lower limb weakness, and poor balance. At 25, after extensive investigation, a pathological mutation in the OPA1 gene was identified through exome sequencing, confirming the diagnosis of ADOA-plus, and treatment with Coenzyme Q10 was initiated. Currently the patient reports sensory ataxia, progressive decrease in visual acuity, fasciculations and cramps in the lower limbs, dysphagia and dyspnea. Discussion: Many patients with ADOA-plus present sensorineural deafness as the most common extra-ophthalmologic symptom, in addition to parkinsonism and dementia, ataxia and ptosis. The patient reported is a case of optic atrophy associated with peripheral neuropathy, ataxia and myopathy. Due to the wide clinical variability of this disease, OPA1 mutations should be investigated in cases of progressive spastic paraparesis associated with optic atrophy, since the possibility of treatment with Coenzyme Q10. ABSTRACT
Deep Rectangling for Image Stitching: A Learning Baseline

Stitched images provide a wide field-of-view (FoV) but suffer from unpleasant irregular boundaries. To deal with this problem, existing image rectangling methods devote to searching an initial mesh and optimizing a target mesh to form the mesh deformation in two stages. Then rectangu-lar images can be generated by warping stitched images. However, these solutions only work for images with rich linear structures, leading to noticeable distortions for por-traits and landscapes with non-linear objects. In this paper, we address these issues by proposing the first deep learning solution to image rectangling. Con-cretely, we predefine a rigid target mesh and only estimate an initial mesh to form the mesh deformation, contributing to a compact one-stage solution. The initial mesh is predicted using a fully convolutional network with a resid-ual progressive regression strategy. To obtain results with high content fidelity, a comprehensive objective function is proposed to simultaneously encourage the boundary rect-angular, mesh shape-preserving, and content perceptually natural. Besides, we build the first image stitching rectan-gling dataset with a large diversity in irregular boundaries and scenes. Experiments demonstrate our superiority over traditional methods both quantitatively and qualitatively.
Towards the Development of Low Cost Calibration Apparatus for 3D Reconstruction of Spine

Three-dimensional reconstruction of the human spine is very much essential in diagnosis, planning of surgery and treatment of several spinal deformities. This can be achieved using stereo-radiographic methods. Calibration of a stereo-radiographic setup is a necessary step in this process. It provides metric information from 2D images. Many calibration methods that are available in literature result in cumbersome calibration objects. Hence difficult to use in the clinical valuation of spinal deformities. 3-D reconstruction without calibration or with small calibration objects is considered to be unreliable. It also results in large reconstruction errors. A review of different calibration techniques is performed and compared. The best suitable technique is proposed which gives least reconstruction error and produces minimum undesirable objects in radiographs.
Robot Learning of Mobile Manipulation With Reachability Behavior Priors

Mobile Manipulation (MM) systems are ideal candidates for taking up the role of personal assistants in unstructured real-world environments. Among other challenges, Mobile Manipulation (MM) requires effective coordination of the robot’s embodiments for executing tasks that require both mobility and manipulation. Reinforcement Learning (RL) holds the promise of endowing robots with adaptive behaviors, but most methods require prohibitively large amounts of data for learning a useful control policy. In this work, we study the integration of robotic reachability priors in actor-critic RL methods for accelerating the learning of MM for reaching and fetching tasks. Namely, we consider the problem of optimal base placement and the subsequent decision of whether to activate the arm for reaching a 6D target. For this, we devise a novel Hybrid RL (HyRL) method that handles discrete and continuous actions jointly, resorting to the Gumbel-Softmax reparameterization. Next, we train a reachability prior using data from the operational robot workspace, inspired by classical methods. Subsequently, we derive Boosted HyRL (BHyRL), a novel actor-critic algorithm that benefits from modeling Q-functions as a sum of residual approximators. Every time a new task needs to be learned, we can transfer our learned residuals and learn the component of the Q-function that is task-specific, hence, maintaining the task structure from prior behaviors. Moreover, we find that regularizing the target policy with a prior policy yields more expressive behaviors. We evaluate our method in simulation in reaching and fetching tasks of increasing difficulty, and we show the superior performance of BHyRL against baseline methods. Finally, we zero-transfer our learned 6D fetching policy with BHyRL to our MM robot: TIAGo++.
Trust in AI and Implications for the AEC Research: A Literature Analysis

Engendering trust in technically acceptable and psychologically embraceable systems requires domain-specific research to capture unique characteristics of the field of application. The architecture, engineering, and construction (AEC) research community has been recently harnessing advanced solutions offered by artificial intelligence (AI) to improve project workflows. Despite the unique characteristics of work, workers, and workplaces in the AEC industry, the concept of trust in AI has received very little attention in the literature. This paper presents a comprehensive analysis of the academic literature in two main areas of trust in AI and AI in the AEC, to explore the interplay between AEC projects unique aspects and the sociotechnical concepts that lead to trust in AI. A total of 490 peer-reviewed scholarly articles are analyzed in this study. The main constituents of human trust in AI are identified from the literature and are characterized within the AEC project types, processes, and technologies.
A gap in the double white dwarf separation distribution caused by the common-envelope evolution: astrometric evidence from Gaia

The trajectory of the center of light of an unresolved binary is different from that of its center of mass. Binary-induced stellar centroid wobbling can therefore be detected as an excess in the goodness-of-fit of the single-star astrometric model. We use reduced χ2 of the astrometric fit in the Gaia Early Data Release 3 to detect the likely unresolved double white dwarfs (DWDs). Using parallax-based distances we convert the excess of reduced χ2 into the amplitude of the centroid wobble δa, which is proportional to the binary separation a. The measured δa distribution drops towards larger wobble amplitudes and shows a break around log10δa ≈ −0.7 where it steepens. The integral of the distribution yields DWD fraction of 6.5 ± 3.7 per cent in the range 0.01 < a (au) < 2. Using synthetic models of the Galactic DWDs we demonstrate that the break in the δa distribution corresponds to one side of a deep gap in the DWD separation distribution at around a ≈ 1 au. Model DWDs with separations less than several au shrink dramatically due to (at least one) common envelope phase, reshaping the original separation distribution, clearing a gap and creating a pile-up of systems with a ≈ 0.01 au and log10δa < −2. Our models reproduce the overall shape of the observed δa distribution and its normalization, however the predicted drop in the numbers of DWDs beyond the break is steeper than in the data.
Adaptive Cross-Layer Attention for Image Restoration

Non-local attention module has been proven to be crucial for image restoration. Conventional non-local attention processes features of each layer separately, so it risks missing correlation between features among different layers. To address this problem, we aim to design attention modules that aggregate information from different layers. Instead of finding correlated key pixels within the same layer, each query pixel is encouraged to attend to key pixels at multiple previous layers of the network. In order to efficiently embed such attention design into neural network backbones, we propose a novel Adaptive Cross-Layer Attention (ACLA) module. Two adaptive designs are proposed for ACLA: (1) adaptively selecting the keys for non-local attention at each layer; (2) automatically searching for the insertion locations for ACLA modules. By these two adaptive designs, ACLA dynamically selects a flexible number of keys to be aggregated for non-local attention at previous layer while maintaining a compact neural network with compelling performance. Extensive experiments on image restoration tasks, including single image super-resolution, image denoising, image demosaicing, and image compression artifacts reduction, validate the effectiveness and efficiency of ACLA. The code of ACLA is available at \url{https://github.com/SDL-ASU/ACLA}.
COLA: Consistent Learning with Opponent-Learning Awareness

Learning in general-sum games is unstable and frequently leads to socially undesirable (Pareto-dominated) outcomes. To mitigate this, Learning with Opponent-Learning Awareness (LOLA) introduced opponent shaping to this setting, by accounting for each agent's influence on their opponents' anticipated learning steps. However, the original LOLA formulation (and follow-up work) is inconsistent because LOLA models other agents as naive learners rather than LOLA agents. In previous work, this inconsistency was suggested as a cause of LOLA's failure to preserve stable fixed points (SFPs). First, we formalize consistency and show that higher-order LOLA (HOLA) solves LOLA's inconsistency problem if it converges. Second, we correct a claim made in the literature by Sch\"afer and Anandkumar (2019), proving that Competitive Gradient Descent (CGD) does not recover HOLA as a series expansion (and fails to solve the consistency problem). Third, we propose a new method called Consistent LOLA (COLA), which learns update functions that are consistent under mutual opponent shaping. It requires no more than second-order derivatives and learns consistent update functions even when HOLA fails to converge. However, we also prove that even consistent update functions do not preserve SFPs, contradicting the hypothesis that this shortcoming is caused by LOLA's inconsistency. Finally, in an empirical evaluation on a set of general-sum games, we find that COLA finds prosocial solutions and that it converges under a wider range of learning rates than HOLA and LOLA. We support the latter finding with a theoretical result for a simple game.
Leveraging Randomized Smoothing for Optimal Control of Nonsmooth Dynamical Systems

Optimal control (OC) algorithms such as Differential Dynamic Programming (DDP) take advantage of the derivatives of the dynamics to efficiently control physical systems. Yet, in the presence of nonsmooth dynamical systems, such class of algorithms are likely to fail due, for instance, to the presence of discontinuities in the dynamics derivatives or because of non-informative gradient. On the contrary, reinforcement learning (RL) algorithms have shown better empirical results in scenarios exhibiting non-smooth effects (contacts, frictions, etc). Our approach leverages recent works on randomized smoothing (RS) to tackle non-smoothness issues commonly encountered in optimal control, and provides key insights on the interplay between RL and OC through the prism of RS methods. This naturally leads us to introduce the randomized Differential Dynamic Programming (R-DDP) algorithm accounting for deterministic but non-smooth dynamics in a very sample-efficient way. The experiments demonstrate that our method is able to solve classic robotic problems with dry friction and frictional contacts, where classical OC algorithms are likely to fail and RL algorithms require in practice a prohibitive number of samples to find an optimal solution.
Towards Building an Open-Domain Dialogue System Incorporated With Internet Memes

In recent years, Internet memes have been widely used in online chatting. Compared with text-based communication, conversations become more expressive and attractive when Internet memes are incorporated. This article presents our solutions for the Meme incorporated Open-domain Dialogue (MOD) Challenge of DSTC10, where three tasks are involved: text response modeling, meme retrieval, and meme emotion classification. Firstly, we leverage a large-scale pre-trained dialogue model for coherent and informative response generation. Secondly, based on interaction-based text-matching, our approach can retrieve appropriate memes with good generalization ability. Thirdly, we propose to model the emotion flow (EF) in conversations and introduce an auxiliary task of emotion description prediction (EDP) to boost the performance of meme emotion classification. Experimental results on the MOD dataset demonstrate that our methods can incorporate Internet memes into dialogue systems effectively.