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arXiv 2024年

作者： Guo, Gabe Saidi, Tristan Luca Terban, Maxwell W. Valsecchi, Michele Billinge, Simon J.L. Lipson, Hod Columbia University Department of Computer Science New YorkNY10027 United States Momentum Transfer Mannheim68169 Germany Columbia University Department of Chemical Engineering New YorkNY10027 United States Columbia University Department of Applied Physics and Applied Mathematics New YorkNY10027 United States Columbia University Department of Mechanical Engineering New YorkNY10027 United States Stanford University Department of Computer Science StanfordCA94305 United States

A major challenge in materials science is the determination of the structure of nanometer sized objects. Here we present a novel approach that uses a generative machine learning model based on diffusion processes that is trained on 45,229 known structures. The model factors both the measured diffraction pattern as well as relevant statistical priors on the unit cell of atomic cluster structures. Conditioned only on the chemical formula and the information-scarce finite-size broadened powder diffraction pattern, we find that our model, PXRDNET, can successfully solve simulated nanocrystals as small as 10 Å across 200 materials of varying symmetry and complexity, including structures from all seven crystal systems. We show that our model can successfully and verifiably determine structural candidates four out of five times, with average error among these candidates being only 7% (as measured by post-Rietveld refinement R-factor). Furthermore, PXRDNET is capable of solving structures from noisy diffraction patterns gathered in real-world experiments. We suggest that data driven approaches, bootstrapped from theoretical simulation, will ultimately provide a path towards determining the structure of previously unsolved nano-materials. Copyright © 2024, The Authors. All rights reserved.

关键词： Rietveld refinement

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High-Capacity Freestanding Supercapacitor Electrode Based on Electrospun Ti3c2tx Mxene/Pani/Pvdf Composite

SSRN

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SSRN 2024年

作者： Mohammadi, Somayeh Ahmadi, Shayan Navid, Hamid Azadvari, Reza Sanaee, Zeinab Moeini, Mohammadreza Applied Engineering Science Research Center School of Engineering Science College of Engineering University of Tehran Tehran Iran Department of Chemistry Materials and Chemical Engineering ''GIULIO NATTA'' School of Industrial and Information Engineering Politecnico di Milano Milan Italy Energy Storage Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

In this study, a high-capacity freestanding supercapacitor electrode was developed by the electrospinning of a Ti3C2Tx MXene/Polyaniline (PANI)/Polyvinylidene fluoride (PVDF) composite. To benefit from the synergistic effect of highly conductive two-dimensional (2D) Ti3C2Tx MXene and pseudo-capacitive PANI, MXene/PANI composite was achieved through a facile synthesis. Ti3C2Tx MXene was produced through selective etching of Ti3AlC2 using hydrofluoric (HF) acid, which was then mixed by PANI Emeraldine salt in N-Methyl-2-Pyrrolidone (NMP). PVDF was added to the composite as a flexible binder to facilitate the electro-spinning and produce a freestanding electrode. The specific capacitance of the freestanding MXene/PANI/PVDF electrode is 740 Fg-1 at a scan rate of 2 mVs-1, and 895 Fg-1 at a charge-discharge current density of 0.5 Ag-1, which was significantly higher than the specific capacitance of MXene (67 Fg-1) and PANI (54 Fg-1) electrospun electrodes. © 2024, The Authors. All rights reserved.

关键词： Supercapacitor

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Fairness in Ranking: Robustness through Randomization without the Protected Attribute

Fairness in Ranking: Robustness through Randomization withou...

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International Conference on Data engineering Workshops

作者： Andrii Kliachkin Eleni Psaroudaki Jakub Mareček Dimitris Fotakis Department of Mathematics Università degli Studi di Padova Padua Italy School of Electrical & Computer Engineering National Technical University of Athens Greece Athena Research & Innovation Center in Information Communication & Knowledge Technologies Athens Greece The Department of Computer Science Czech Technical University in Prague the Czech Republic

ISBN: (数字)9798350384031

ISBN: (纸本)9798350384048

There has been great interest in fairness in machine learning, especially in relation to classification problems. In ranking-related problems, such as in online advertising, recommender systems, and HR automation, much work on fairness remains to be done. Two complications arise: first, the protected attribute may not be available in many applications. Second, there are multiple measures of fairness of rankings, and optimization-based methods utilizing a single measure of fairness of rankings may produce rankings that are unfair with respect to other measures. In this work, we propose a randomized method for post-processing rankings, which do not require the availability of the protected attribute. In an extensive numerical study, we show the robustness of our methods with respect to P-Fairness and effectiveness with respect to Normalized Discounted Cumulative Gain (NDCG) from the baseline ranking, improving on previously proposed methods.

关键词： Privacy Systematics Conferences Noise Machine learning Data engineering Robustness

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Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems

arXiv

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arXiv 2023年

作者： Ji, Xiang Wang, Huazheng Chen, Minshuo Zhao, Tuo Wang, Mengdi Department of Electrical and Computer Engineering School of Engineering and Applied Science Princeton University PrincetonNJ08544 United States School of Electrical Engineering and Computer Science College of Engineering Oregon State University Oregon CorvallisOR97331 United States H. Milton Stewart School of Industrial and Systems Engineering College of Engineering Georgia Institute of Technology AtlantaGA30332 United States

For a real-world decision-making problem, the reward function often needs to be engineered or learned. A popular approach is to utilize human feedback to learn a reward function for training. The most straightforward way to do so is to ask humans to provide ratings for state-action pairs on an absolute scale and take these ratings as reward samples directly. Another popular way is to ask humans to rank a small set of state-action pairs by preference and learn a reward function from these preference data. Recently, preference-based methods have demonstrated substantial success in empirical applications such as InstructGPT. In this work, we develop a theoretical comparison between these human feedback approaches in offline contextual bandits and show how human bias and uncertainty in feedback modelings can affect the theoretical guarantees of these approaches. Through this, our results seek to provide a theoretical explanation for the empirical successes of preference-based methods from a modeling perspective. © 2023, CC BY.

关键词： Decision making

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Automated Segmentation of Multiple Sclerosis Lesions using Deep Learning

Automated Segmentation of Multiple Sclerosis Lesions using D...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Tanzim Hossain Md. Shakib Shahariar Junayed Kazi Shahriar Sanjid Abir Hossain Rohan Mohammad A Khan Sheikh Anisul Haque M. Monir Uddin Department of Electrical & Computer Engineering North South University Dhaka Bangladesh Department of Transfusion Medicine Khwaja Yunus Ali Medical College Sirajganj Bangladesh Department of Mathematics & Physics North South University Dhaka Bangladesh

This research introduces a pioneering Multiple Sclerosis (MS) lesion segmentation approach, employing a multi-scale UNet framework. The model architecture, comprised of Contraction and Expansive Paths, adeptly captures intricate details within input images. Leveraging a dataset of 60 patients MRI scans, manually segmented across three sequences, the study’s preprocessing involves slicing 3D data into 2D images, standardizing dimensions and enhancing image quality via sharpening and intensity normalization. Notably, the multi-scale UNet model achieved outstanding results, showcasing a remarkable Dice Similarity Coefficient (DSC) of 99.78% for lesion segmentation. Although the model showed decreased loss values over the training phase. The suggested approach outperformed existing algorithms like CNN, Acu-Net and ALLNet, according to a rigorous comparison analysis that looked at a number of different parameters. This study improves how doctors make decisions by making a big advance in automatically finding MS lesions. Notwithstanding challenges in conducting direct comparisons because of differences in datasets, the methodology’s proven robustness and efficacy point to its potential to revolutionize MS diagnosis and treatment.

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An equivalent reformulation and multi-proximity gradient algorithms for a class of nonsmooth fractional programming

arXiv

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arXiv 2023年

作者： Zhou, Junpeng Zhang, Na Li, Qia School of Computer Sciences and Engineering Sun Yat-Sen University Guangzhou510006 China Department of Applied Mathematics College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China School of Computer Sciences and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University Guangzhou510006 China

In this paper, we consider a class of structured fractional programs, where the numerator part is the sum of a block-separable (possibly nonsmooth nonconvex) function and a locally Lipschitz differentiable (possibly nonconvex) function, while the denominator is a convex (possibly nonsmooth) function. We first present a novel reformulation for the original problem and show the relationship between optimal solutions, critical points and KL exponents of these two problems. Inspired by the reformulation, we propose a flexible framework of multi-proximity gradient algorithms (MPGA), which computes the proximity operator with respect to the Fenchel conjugate associated with the convex denominator of the original problem rather than evaluating its subgradient as in the existing methods. Also, MPGA employs a nonmonotone linear-search scheme in its gradient descent step, since the smooth part in the numerator of the original problem is not globally Lipschitz differentiable. Based on the framework of MPGA, we develop two specific algorithms, namely, cyclic MPGA and randomized MPGA, and establish their subsequential convergence under mild conditions. Moreover, the sequential convergence of cyclic MPGA with the monotone line-search (CMPGA ML) is guaranteed if the extended objective associated with the reformulated problem satisfies the Kurdyka-Lojasiewicz (KL) property and some other mild assumptions. In particular, we prove that the corresponding KL exponents are 12 for several special cases of the fractional programs, and so, CMPGA ML exhibits a linear convergence rate. Finally, some preliminary numerical experiments are performed to demonstrate the efficiency of our proposed algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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Risk detection in wireless body sensor networks for health monitoring using hybrid deep learning 2nd

Risk detection in wireless body sensor networks for health m...

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2nd International Conference on electrical and Electronics engineering, ICEEE 2021

作者： Rajawat, Anand Singh Barhanpurkar, Kanishk Shaw, Rabindra Nath Ghosh, Ankush Department of Computer Science Engineering Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India Department of Computer Science and Engineering Sambhram Institute of Technology Bengaluru Karnataka India Department of Electrical Electronics and Communication Engineering Galgotias University Greater Noida India School of Engineering and Applied Sciences The Neotia University Sarisha West Bengal India

ISBN: (纸本)9789811607486

The Internet of Things (IoT) idea has arisen as interconnected components of the healthcare tracking facilities of smart linked healthcare networks. Hard sensor-based data aggregation with the help of devices in the form of wearables or intrusive samples attached with the acquisition of soft sensors like crowd sensor results in the aggregated sensor data being concealed in patterns. This problem is tackled through several secret stages of interpretation of deep learning techniques. In this research work, we proposed hybrid deep learning (HDL) techniques to develop estimation and enhance quality of smart health services on health monitoring data. We also showed a detailed comparison of methods on the basis of health surveillance types. Hence, our proposed models work for risk detection in health information which will help us to increase the efficiency of existing healthcare industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Internet of things

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A mathematical model with numerical simulations for malaria transmission dynamics with differential susceptibility and partial immunity

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Healthcare Analytics 2023年 3卷 100165-100165页

作者： Tchoumi, S.Y. Rwezaura, H. Tchuenche, J.M. Department of Mathematics and Computer Sciences ENSAI – University of N'Gaoundéré N'Gaoundéré Cameroon Department of Mathematics and Applied Mathematics University of Pretoria South Africa Mathematics Department University of Dar es Salaam Dar es Salaam Tanzania School of Computational and Communication Sciences and Engineering Nelson Mandela African Institution of Science and Technology Arusha Tanzania School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa

Malaria is a deadly vector-borne infectious disease with high prevalence in the world's endemic tropical and subtropical regions. Differences in individuals’ disease susceptibility may lead to their differentiation of susceptibility to infection. We formulate a mathematical model for malaria transmission dynamics that accounts for the host's differential susceptibility, where partial immunity is acquired after infection. As customary, the explicit formula for the basic reproduction number is derived and used to determine the local stability of the model's equilibria. An analysis of a special case with two susceptible classes shows that the model could have two endemic equilibria when the disease threshold parameter is less than unity. Numerical simulations are provided for a differential susceptibility when individuals are re-infected seven times after the initial infection. Graphical representations show that the transient transmission dynamics of the infected components are indistinguishable when there is no inflow into the susceptible classes. When there is an inflow into the various susceptible classes, the graphs of the infected component of the model are fundamentally different, showing that individuals who have been infected multiple times tend to be less infected over time. Knowledge of the inflow rate and the infection reduction rate due to prior infection in each class could be key drivers to mitigate the burden of malaria in a community. © 2023 The Author(s)

关键词： Basic reproduction number Differential susceptibility Immunity Malaria Mathematical modeling Numerical simulation

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Inferring Ventilation Rates with Quantified Uncertainty in Operational Rooms Using Point Measurements of Carbon Dioxide: Classrooms as a Case Study

SSRN

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SSRN 2023年

作者： Finneran, Joshua Burridge, Henry Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University Loughborough United Kingdom Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Department of Civil and Environmental Engineering Imperial College London London United Kingdom

We present a robust integral method to estimate the daily mean per-person ventilation rate Qpp based on carbon dioxide (CO2) concentration measurements in operational spaces, and limited other data. The method makes no assumptions regarding the ventilation provision throughout the day, nor requires the room to be in a steady state, nor the air within to be well-mixed. We demonstrate that several integral parameters remain reliably close to a value of unity, despite large variations in room conditions. Evaluating the likely distributions of integral parameters provides a method to quantify the uncertainty bounds and therefore assess the reliability of these ventilation estimates. Taking school classrooms as a case study, estimates of Qpp based on measured CO2 are shown to exhibit uncertainty bounds (confidence intervals of 95%) of around ±24% if no other data than the classroom timetable, is available. Deploying four sensors within a classroom is expected to halve the uncertainty bounds to around ±12%. Moreover, when the same classroom is occupied similarly on different days then the framework evidences that simply taking the ratio of the integral of excess CO2 for the two days is a direct measure of the relative inter-day per-person ventilation rates for the two days. These significant findings offer great scope to facilitate more reliable ventilation estimates, particularly from large-scale data sets of CO2 measured in operational spaces, to better inform assessments of indoor air quality. © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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Environmental and genome-wide association study on children anxiety and depression

Environmental and genome-wide association study on children ...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Thapaliya, Bishal Calhoun, Vince D. Liu, Jingyu Department of Computer Science Georgia State University Atlanta United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States Department of Psychology Computer Science Neurosciences Mathematics and Statistics Georgia State University Atlanta United States

ISBN: (纸本)9781665401265

Anxiety and Depression are currently among the most common mental disorders in children and adolescents. Both genetics and environments play an important role in the development and progress of disorders. This study aimed to understand the effect of multiscale environmental factors on anxiety and depression in school-age children, to refine the identification of genetic variants contributing to susceptibility to anxiety and depression, and to evaluate the genetic heritability. We analyzed data from the Adolescent Brain and Cognitive Development study and computed one principal factor to present the overall anxiety and depression scale in 11,875 participants with ages between 9 and 10 years old. Linear mixed-effect models along with the recursive feature elimination regression and LASSO regression models were used to determine the environmental effects from the macro scale (population density, air pollution), meso scale (neighborhood, school), to micro scale (family and individual experience). Genome-wide association analyses were then performed controlling for environmental factors and sample relatedness to determine the susceptible genetic variants. Furthermore, heritability was calculated for the white population. The results showed that six environmental factors (early life stress, household income, population density, area crime, neighborhood safety, and school risk) and sex had significant effect on anxiety/depression score. Genome-wide association tests showed no SNPs reached a genome-wide significance (p=5e-08), but some genetic mutations including SNPs in SCN1A showed promising effects, and the heritability was estimated close to 15% in the white population. © 2021 IEEE.

关键词： Population statistics

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