cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Noncompliance and instrumental variables for 2K factorial experiments*
Creators: name: Blackwell, Matthew
name: Pashley, Nicole E.
Publisher: name: Taylor & Francis
Year: 2021
Subjects: subject: Neuroscience
subject: Pharmacology
subject: Biotechnology
subject: Developmental Biology
subject: Science Policy
subject: Mental Health
subject: Hematology
subject: Infectious Diseases
subject: FOS: Health sciences

description: Factorial experiments are widely used to assess the marginal, joint, and interactive effects of multiple concurrent factors. While a robust literature covers the design and analysis of these experiments, there is less work on how to handle treatment noncompliance in this setting. To fill this gap, we introduce a new methodology that uses the potential outcomes framework for analyzing 2K factorial experiments with noncompliance on any number of factors. This framework builds on and extends the literature on both instrumental variables and factorial experiments in several ways. First, we define novel, complier-specific quantities of interest for this setting and show how to generalize key instrumental variables assumptions. Second, we show how partial compliance across factors gives researchers a choice over different types of compliers to target in estimation. Third, we show how to conduct inference for these new estimands from both the finite-population and superpopulation asymptotic perspectives. Finally, we illustrate these techniques by applying them to a field experiment on the effectiveness of different forms of get-out-the-vote canvassing. New easy-to-use, open-source software implements the methodology.
Topological order in higher composites
Creators: name: Babaev, Egor
Publisher: name: arXiv
Year: 2024
Subjects: subject: Quantum Gases (cond-mat.quant-gas)
subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: We introduce the concept of composite topological order in multicomponent systems. In such a state topological order appears only in higher-than-usual composites, with no topological order in elementary fields. We propose that such a state can be realized in Bose-Fermi mixtures in ultracold atoms.
description: v3: published version (tiny changes)
Fast Network Community Detection With Profile-Pseudo Likelihood Methods
Creators: name: Wang, Jiangzhou
name: Zhang, Jingfei
name: Liu, Binghui
name: Zhu, Ji
name: Guo, Jianhua
Publisher: name: Taylor & Francis
Year: 2021
Subjects: Biophysics
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified
Cancer

description: The stochastic block model is one of the most studied network models for community detection, and fitting its likelihood function on large-scale networks is known to be challenging. One prominent work that overcomes this computational challenge is the fast pseudo-likelihood approach proposed by Amini et al. for fitting stochastic block models to large sparse networks. However, this approach does not have convergence guarantee, and may not be well suited for small and medium scale networks. In this article, we propose a novel likelihood based approach that decouples row and column labels in the likelihood function, enabling a fast alternating maximization. This new method is computationally efficient, performs well for both small- and large-scale networks, and has provable convergence guarantee. We show that our method provides strongly consistent estimates of communities in a stochastic block model. We further consider extensions of our proposed method to handle networks with degree heterogeneity and bipartite properties. Supplementary materials for this article are available online.
Dust formation in common envelope binary interactions -- II: 3D simulations with self-consistent dust formation
Creators: name: Bermúdez-Bustamante, Luis C.
name: De Marco, Orsola
name: Siess, Lionel
name: Price, Daniel J.
name: González-Bolívar, Miguel
name: Lau, Mike Y. M.
name: Mu, Chunliang
name: Hirai, Ryosuke
name: Danilovich, Taïssa
name: Kasliwal, Mansi M.
Publisher: name: arXiv
Year: 2024
Subjects: subject: Solar and Stellar Astrophysics (astro-ph.SR)
subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: We performed numerical simulations of the common envelope (CE) interaction between thermally-pulsing asymptotic giant branch (AGB) stars of 1.7~\Msun and 3.7~\Msun, respectively, and a 0.6~\Msun compact companion. We use tabulated equations of state to take into account recombination energy. For the first time, formation and growth of dust is calculated explicitly, using a carbon dust nucleation network with a C/O abundance ratio of 2.5 (by number). The first dust grains appear within $\sim$1--3~yrs after the onset of the CE, forming an optically thick shell at $\sim$10--20~au, growing in thickness and radius to values of $\sim$400--500~au over $\sim$40~yrs, with temperatures around 400~K. Most dust is formed in unbound material, having little effect on mass ejection or orbital evolution. By the end of the simulations, the total dust yield is $\sim8.4\times10^{-3}$~\Msun and $\sim2.2\times10^{-2}$~\Msun for the CE with a 1.7~\Msun and a 3.7~\Msun AGB star, respectively, corresponding to a nucleation efficiency close to 100\%, if no dust destruction mechanism is considered. Despite comparable dust yields to single AGB stars, \textit{in CE ejections the dust forms a thousand times faster, over tens of years as opposed to tens of thousands of years}. This rapid dust formation may account for the shift in the infrared of the spectral energy distribution of some optical transients known as luminous red novae. Simulated dusty CEs support the idea that extreme carbon stars and "water fountains" may be objects observed after a CE event.
description: 18 pages, 17 figures, 2 tables
Finite Element Simulation of Residual Stresses from Welding and High Frequency Hammer Peening
Creators: name: Gkatzogiannis, Stefanos
Publisher: name: KIT Scientific Publishing
Year: 2021
Subjects: FE Simulation
Ermüdung
höherfrequentes Hämmerverfahren
Eigenspannungen
Schweißsimulation
Fatigue
High Frequency Mechanical Impact
Residual Stresses
Weld Simulation

description: Research goal of the present monograph is the establishment of an efficient engineering approach, which will include straightforward but accurate simulation models, in order to estimate the residual stress fields of welded joints introduced during welding and their post-weld treatment with High Frequency Hammer Peening. The present subject lies on the intersection of structural engineering, material science and computational mechanics.
Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution Under Random Designs
Creators: name: Chen, Yuxin
name: Fan, Jianqing
name: Wang, Bingyan
name: Yan, Yuling
Publisher: name: Taylor & Francis
Year: 2023
Subjects: subject: Space Science
subject: Medicine
subject: Neuroscience
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Computational Biology

description: We investigate the effectiveness of convex relaxation and nonconvex optimization in solving bilinear systems of equations under two different designs (i.e., a sort of random Fourier design and Gaussian design). Despite the wide applicability, the theoretical understanding about these two paradigms remains largely inadequate in the presence of random noise. The current article makes two contributions by demonstrating that (i) a two-stage nonconvex algorithm attains minimax-optimal accuracy within a logarithmic number of iterations, and (ii) convex relaxation also achieves minimax-optimal statistical accuracy vis-à-vis random noise. Both results significantly improve upon the state-of-the-art theoretical guarantees. Supplementary materials for this article are available online.
Hard X-Ray Spectral Properties Of Distant Agn In The Nustar Surveys
Creators: name: Del Moro, Agnese
Publisher: name: Zenodo
Year: 2016
Subjects: Active Galactic Nuclei

description: I will present a study on the average broad X-ray band (~0.5-30 keV) spectral properties of the NuSTAR sources detected in the ECDF-S, EGS and COSMOS fields. Constructing the rest-frame composite spectra of AGN in different hydrogen column density (NH) and 10-40 keV luminosity bins, using Chandra and NuSTAR data, we investigate the typical spectral parameters of the AGN population, such as the photon index, NH, strength of the iron emission line (~6.4 keV) and of the Compton reflection  at ~20-30 keV. Placing constraints on the reflection fraction (R) is of particular importance for the synthesis models of the cosmic X-ray background (CXB), as this parameter is strongly linked with the fraction of Compton-thick AGN needed to fit the CXB spectrum. Thanks to its sensitivity at ~20-30 keV, NuSTAR allows for the first time, to directly place such constraints for non-local AGN.

We find typical reflection fractions of R~1-1.5, consistent the AGN in the local Universe, with a tentative evidence for the most obscured AGN to have, on average, stronger Compton reflection compared to unobscured AGN. Moreover, contrary to previous works, we do not find significant evidence for a decrease of the reflection strength with luminosity for typical $\Gamma=1.8-1.9$. Our results support CXB models that require a relatively small fraction of CT AGN, of the order of ~10-15%.
Axisymmetric capillary water waves with vorticity and swirl connecting to static unduloid configurations
Creators: name: Otsetova, Anna-Mariya
name: Wahlén, Erik
name: Weber, Jörg
Publisher: name: arXiv
Year: 2024
Subjects: subject: Analysis of PDEs (math.AP)
subject: FOS: Mathematics
subject: 33E05, 35B07, 76B15, 76B45, 76B47

description: We study steady axisymmetric water waves with general vorticity and swirl, subject to the influence of surface tension. Explicit solutions to such a water wave problem are static configurations where the surface is an unduloid, that is, a periodic surface of revolution with constant mean curvature. We prove that to any such configuration there connects a global continuum of non-static solutions by means of a global implicit function theorem. To prove this, the key is strict monotonicity of a certain function describing the mean curvature of an unduloid and involving complete elliptic integrals. From this point of view, this paper is an interesting interplay between water waves, geometry, and properties of elliptic integrals.
Derandomizing Knockoffs
Creators: name: Ren, Zhimei
name: Wei, Yuting
name: Candès, Emmanuel
Publisher: name: Taylor & Francis
Year: 2021
Subjects: subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Sociology
subject: FOS: Sociology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer

description: Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously controlling the number or fraction of false positives. Model-X knockoffs is a randomized procedure which relies on the one-time construction of synthetic (random) variables. This paper introduces a derandomization method by aggregating the selection results across multiple runs of the knockoffs algorithm. The derandomization step is designed to be flexible and can be adapted to any variable selection base procedure to yield stable decisions without compromising statistical power. When applied to the base procedure of Janson and Su (2016), we prove that derandomized knockoffs controls both the per family error rate (PFER) and the k family-wise error rate (k-FWER). Further, we carry out extensive numerical studies demonstrating tight type-I error control and markedly enhanced power when compared with alternative variable selection algorithms. Finally, we apply our approach to multi-stage genome-wide association studies of prostate cancer and report locations on the genome that are significantly associated with the disease. When cross-referenced with other studies, we find that the reported associations have been replicated.
A Survey on Cross-Domain Sequential Recommendation
Creators: name: Chen, Shu
name: Xu, Zitao
name: Pan, Weike
name: Yang, Qiang
name: Ming, Zhong
Publisher: name: arXiv
Year: 2024
Subjects: subject: Information Retrieval (cs.IR)
subject: FOS: Computer and information sciences

description: Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain). In this survey, we first define the CDSR problem using a four-dimensional tensor and then analyze its multi-type input representations under multidirectional dimensionality reductions. Following that, we provide a systematic overview from both macro and micro views. From a macro view, we abstract the multi-level fusion structures of various models across domains and discuss their bridges for fusion. From a micro view, focusing on the existing models, we first discuss the basic technologies and then explain the auxiliary learning technologies. Finally, we exhibit the available public datasets and the representative experimental results as well as provide some insights into future directions for research in CDSR.
description: Accepted to the IJCAI 2024 Survey Track
Is a Classification Procedure Good Enough?—A Goodness-of-Fit Assessment Tool for Classification Learning
Creators: name: Zhang, Jiawei
name: Ding, Jie
name: Yang, Yuhong
Publisher: name: Taylor & Francis
Year: 2022
Subjects: subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Mathematical Sciences not elsewhere classified

description: In recent years, many nontraditional classification methods, such as random forest, boosting, and neural network, have been widely used in applications. Their performance is typically measured in terms of classification accuracy. While the classification error rate and the like are important, they do not address a fundamental question: Is the classification method underfitted? To our best knowledge, there is no existing method that can assess the goodness of fit of a general classification procedure. Indeed, the lack of a parametric assumption makes it challenging to construct proper tests. To overcome this difficulty, we propose a methodology called BAGofT that splits the data into a training set and a validation set. First, the classification procedure to assess is applied to the training set, which is also used to adaptively find a data grouping that reveals the most severe regions of underfitting. Then, based on this grouping, we calculate a test statistic by comparing the estimated success probabilities and the actual observed responses from the validation set. The data splitting guarantees that the size of the test is controlled under the null hypothesis, and the power of the test goes to one as the sample size increases under the alternative hypothesis. For testing parametric classification models, the BAGofT has a broader scope than the existing methods since it is not restricted to specific parametric models (e.g., logistic regression). Extensive simulation studies show the utility of the BAGofT when assessing general classification procedures and its strengths over some existing methods when testing parametric classification models. Supplementary materials for this article are available online.
Variability-Selected Agn
Creators: name: De Marco, Barbara
Publisher: name: Zenodo
Year: 2016
Subjects: Active Galactic Nuclei

description: Variability is one of the defining properties of active galactic nuclei (AGN) as a class. Significant flux variations are observed in different wavebands and over a wide range of time scales, from hours to years. I will review our current knowledge of AGN variability, in particular discussing interband correlations, scaling relationships, and models for the origin of the observed flux variations. I will then focus on variability as a survey technique, discussing strategies to use variability as an independent tool for selecting AGN candidates for spectroscopic follow-up.
Fast Network Community Detection with Profile-Pseudo Likelihood Methods
Creators: name: Wang, Jiangzhou
name: Zhang, Jingfei
name: Liu, Binghui
name: Zhu, Ji
name: Guo, Jianhua
Publisher: name: Taylor & Francis
Year: 2021
Subjects: Biophysics
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
Mathematical Sciences not elsewhere classified
Cancer

description: The stochastic block model is one of the most studied network models for community detection, and fitting its likelihood function on large-scale networks is known to be challenging. One prominent work that overcomes this computational challenge is Amini et al. (2013), which proposed a fast pseudo-likelihood approach for fitting stochastic block models to large sparse networks. However, this approach does not have convergence guarantee, and may not be well suited for small and medium scale networks. In this article, we propose a novel likelihood based approach that decouples row and column labels in the likelihood function, enabling a fast alternating maximization. This new method is computationally efficient, performs well for both small and large scale networks, and has provable convergence guarantee. We show that our method provides strongly consistent estimates of communities in a stochastic block model. We further consider extensions of our proposed method to handle networks with degree heterogeneity and bipartite properties.
Convex and Nonconvex Optimization Are Both Minimax-Optimal for Noisy Blind Deconvolution under Random Designs
Creators: name: Chen, Yuxin
name: Fan, Jianqing
name: Wang, Bingyan
name: Yan, Yuling
Publisher: name: Taylor & Francis
Year: 2021
Subjects: subject: Space Science
subject: Physical Sciences not elsewhere classified
subject: Medicine
subject: Cell Biology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Computational Biology

description: We investigate the effectiveness of convex relaxation and nonconvex optimization in solving bilinear systems of equations under two different designs (i.e. a sort of random Fourier design and Gaussian design). Despite the wide applicability, the theoretical understanding about these two paradigms remains largely inadequate in the presence of random noise. The current paper makes two contributions by demonstrating that: (1) a two-stage nonconvex algorithm attains minimax-optimal accuracy within a logarithmic number of iterations, and (2) convex relaxation also achieves minimax-optimal statistical accuracy vis-à-vis random noise. Both results significantly improve upon the state-of-the-art theoretical guarantees.
Schauder estimates for parabolic equations with degenerate or singular weights
Creators: name: Audrito, Alessandro
name: Fioravanti, Gabriele
name: Vita, Stefano
Publisher: name: arXiv
Year: 2024
Subjects: subject: Analysis of PDEs (math.AP)
subject: FOS: Mathematics
subject: 35B65

description: We establish some $C^{0,α}$ and $C^{1,α}$ regularity estimates for a class of weighted parabolic problems in divergence form. The main novelty is that the weights may vanish or explode on a characteristic hyperplane $Σ$ as a power $a > -1$ of the distance to $Σ$. The estimates we obtain are sharp with respect to the assumptions on coefficients and data. Our methods rely on a regularization of the equation and some uniform regularity estimates combined with a Liouville theorem and an approximation argument. As a corollary of our main result, we obtain similar $C^{1,α}$ estimates when the degeneracy/singularity of the weight occurs on a regular hypersurface of cylindrical type.
description: Add some references
Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks
Creators: name: Blücher, Stefan
name: Vielhaben, Johanna
name: Strodthoff, Nils
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Artificial Intelligence (cs.AI)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: Feature removal is a central building block for eXplainable AI (XAI), both for occlusion-based explanations (Shapley values) as well as their evaluation (pixel flipping, PF). However, occlusion strategies can vary significantly from simple mean replacement up to inpainting with state-of-the-art diffusion models. This ambiguity limits the usefulness of occlusion-based approaches. For example, PF benchmarks lead to contradicting rankings. This is amplified by competing PF measures: Features are either removed starting with most influential first (MIF) or least influential first (LIF). This study proposes two complementary perspectives to resolve this disagreement problem. Firstly, we address the common criticism of occlusion-based XAI, that artificial samples lead to unreliable model evaluations. We propose to measure the reliability by the R(eference)-Out-of-Model-Scope (OMS) score. The R-OMS score enables a systematic comparison of occlusion strategies and resolves the disagreement problem by grouping consistent PF rankings. Secondly, we show that the insightfulness of MIF and LIF is conversely dependent on the R-OMS score. To leverage this, we combine the MIF and LIF measures into the symmetric relevance gain (SRG) measure. This breaks the inherent connection to the underlying occlusion strategy and leads to consistent rankings. This resolves the disagreement problem, which we verify for a set of 40 different occlusion strategies.
description: 28 pages, 8 figures
title: Efficient Processing and Learning on Unstructured Data
Creators: name: Groh, Fabian
Publisher: name: Universität Tübingen
Year: 2025
Subjects: subject: Deep Learning , Maschinelles Lernen , Informatik , Maschinelles Sehen , Computergrafik
subject: 004

description: Inferring knowledge by visually perceiving the world is the fundamental goal of computer vision. The primary input types are images and videos, particularly in the era of deep learning. Such structured data representations are defined on a fixed grid, implicitly assigning positional information to each data point. Hence, finding neighbors is a simple lookup; data must be defined everywhere by the same resolution. On the other hand, unstructured data representations like point clouds, feature spaces, or graphs do not have such an underlying structure. However, they can efficiently embed information from multiple sources into a common representation, leveraging knowledge and resolutions otherwise impossible to achieve from a single source. In particular, higher dimensional data is infeasible to represent in a densely structured way. Unfortunately, losing the implicit grid structure renders many algorithms fundamental to computer vision unusable. In particular, the advancements in vision-based deep learning heavily depend on the highly efficient frameworks providing implementations and routines only defined on structured data. This thesis highlights the importance of unstructured data representations and presents efficient processing and learning techniques in this context. Four projects are forming the core of this work, demonstrating different aspects: 1) A high-precision 3D reconstruction scheme with active illumination. 2) A framework to enable fully-convolutional deep learning on point cloud data. 3) An end-to-end Fisher Vector embedding for object recognition. 4) A superfast proximity graph construction and query for nearest neighbor search on GPUs. It is shown that Efficient Processing and Learning on Unstructured Data is possible when algorithms are designed from the ground up in a massively parallelizable fashion and domain knowledge is incorporated. Generally, high efficiency is achieved by scaling up the simple but robust and effective methods through GPUs. The presented projects are evaluated against large-scale, real-world data to showcase their robustness, performance, and application capabilities.
Finding Privacy-relevant Source Code
Creators: name: Tang, Feiyang
name: Østvold, Bjarte M.
Publisher: name: arXiv
Year: 2024
Subjects: subject: Software Engineering (cs.SE)
subject: Cryptography and Security (cs.CR)
subject: FOS: Computer and information sciences

description: Privacy code review is a critical process that enables developers and legal experts to ensure compliance with data protection regulations. However, the task is challenging due to resource constraints. To address this, we introduce the concept of privacy-relevant methods - specific methods in code that are directly involved in the processing of personal data. We then present an automated approach to assist in code review by identifying and categorizing these privacy-relevant methods in source code.
 Using static analysis, we identify a set of methods based on their occurrences in 50 commonly used libraries. We then rank these methods according to their frequency of invocation with actual personal data in the top 30 GitHub applications. The highest-ranked methods are the ones we designate as privacy-relevant in practice. For our evaluation, we examined 100 open-source applications and found that our approach identifies fewer than 5% of the methods as privacy-relevant for personal data processing. This reduces the time required for code reviews. Case studies on Signal Desktop and Cal.com further validate the effectiveness of our approach in aiding code reviewers to produce enhanced reports that facilitate compliance with privacy regulations.
description: Accepted by the 2nd International Workshop on Mining Software Repositories Applications for Privacy and Security
A Complete Hard X-Ray Selected Sample Of Local, Luminous Agns
Creators: name: Burtscher, Leonard
name: Davies, Ric
name: Lin, Ming-yi
name: Orban de Xivry, Gilles
name: Rosario, David
Publisher: name: Zenodo
Year: 2016
Subjects: Active Galactic Nuclei

description: Choosing a very well defined sample is essential for studying the AGN phenomenon. Only the most luminous AGNs can be expected to require a coherent feeding mechanism to sustain their activity and since host galaxy properties and AGN activity are essentially uncorrelated, nuclear scales must be resolved in order to shed light on the feeding mechanisms of AGNs. For these reasons we are compiling a sample of the most powerful, local AGNs. In this talk we present our on-going programme to observe a complete volume limited sample of nearby active galaxies selected by their 14–195 keV luminosity, and outline its rationale for studying the mechanisms regulating gas inflow and outflow.
Kernel-Based Partial Permutation Test for Detecting Heterogeneous Functional Relationship
Creators: name: Li, Xinran
name: Jiang, Bo
name: Liu, Jun S.
Publisher: name: Taylor & Francis
Year: 2022
Subjects: subject: Neuroscience
subject: Physiology
subject: FOS: Biological sciences
subject: Ecology
subject: Biological Sciences not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer

description: We propose a kernel-based partial permutation test for checking the equality of functional relationship between response and covariates among different groups. The main idea, which is intuitive and easy to implement, is to keep the projections of the response vector Y on leading principle components of a kernel matrix fixed and permute Y’s projections on the remaining principle components. The proposed test allows for different choices of kernels, corresponding to different classes of functions under the null hypothesis. First, using linear or polynomial kernels, our partial permutation tests are exactly valid in finite samples for linear or polynomial regression models with Gaussian noise; similar results straightforwardly extend to kernels with finite feature spaces. Second, by allowing the kernel feature space to diverge with the sample size, the test can be large-sample valid for a wider class of functions. Third, for general kernels with possibly infinite-dimensional feature space, the partial permutation test is exactly valid when the covariates are exactly balanced across all groups, or asymptotically valid when the underlying function follows certain regularized Gaussian processes. We further suggest test statistics using likelihood ratio between two (nested) Gaussian process regression models, and propose computationally efficient algorithms utilizing the EM algorithm and Newton’s method, where the latter also involves Fisher scoring and quadratic programming and is particularly useful when EM suffers from slow convergence. Extensions to correlated and non-Gaussian noises have also been investigated theoretically or numerically. Furthermore, the test can be extended to use multiple kernels together and can thus enjoy properties from each kernel. Both simulation study and application illustrate the properties of the proposed test.
The Hunt For Cosmic Accelerators: Neutrinos
Creators: name: Resconi, Elisa
Publisher: name: Zenodo
Year: 2016
Subjects: Active Galactic Nuclei

description: The recent discovery of high energy cosmic neutrinos from the IceCube Neutrino Observatory opens new opportunities for particle and astrophysics. We report here the IceCube observation of a diffuse neutrino background and the on-going searches for counterparts.
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis
Creators: name: Liu, Zhiwei
name: Yang, Kailai
name: Zhang, Tianlin
name: Xie, Qianqian
name: Ananiadou, Sophia
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computation and Language (cs.CL)
subject: FOS: Computer and information sciences

description: Sentiment analysis and emotion detection are important research topics in natural language processing (NLP) and benefit many downstream tasks. With the widespread application of LLMs, researchers have started exploring the application of LLMs based on instruction-tuning in the field of sentiment analysis. However, these models only focus on single aspects of affective classification tasks (e.g. sentimental polarity or categorical emotions), and overlook the regression tasks (e.g. sentiment strength or emotion intensity), which leads to poor performance in downstream tasks. The main reason is the lack of comprehensive affective instruction tuning datasets and evaluation benchmarks, which cover various affective classification and regression tasks. Moreover, although emotional information is useful for downstream tasks, existing downstream datasets lack high-quality and comprehensive affective annotations. In this paper, we propose EmoLLMs, the first series of open-sourced instruction-following LLMs for comprehensive affective analysis based on fine-tuning various LLMs with instruction data, the first multi-task affective analysis instruction dataset (AAID) with 234K data samples based on various classification and regression tasks to support LLM instruction tuning, and a comprehensive affective evaluation benchmark (AEB) with 14 tasks from various sources and domains to test the generalization ability of LLMs. We propose a series of EmoLLMs by fine-tuning LLMs with AAID to solve various affective instruction tasks. We compare our model with a variety of LLMs on AEB, where our models outperform all other open-sourced LLMs, and surpass ChatGPT and GPT-4 in most tasks, which shows that the series of EmoLLMs achieve the ChatGPT-level and GPT-4-level generalization capabilities on affective analysis tasks, and demonstrates our models can be used as affective annotation tools.
description: Accepted by KDD 2024
Kernel-based Partial Permutation Test for Detecting Heterogeneous Functional Relationship
Creators: name: Li, Xinran
name: Jiang, Bo
name: Liu, Jun S.
Publisher: name: Taylor & Francis
Year: 2021
Subjects: subject: Neuroscience
subject: Physiology
subject: FOS: Biological sciences
subject: Ecology
subject: Biological Sciences not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer

description: We propose a kernel-based partial permutation test for checking the equality of functional relationship between response and covariates among different groups. The main idea, which is intuitive and easy to implement, is to keep the projections of the response vector Y on leading principle components of a kernel matrix fixed and permute Y’s projections on the remaining principle components. The proposed test allows for different choices of kernels, corresponding to different classes of functions under the null hypothesis. First, using linear or polynomial kernels, our partial permutation tests are exactly valid in finite samples for linear or polynomial regression models with Gaussian noise; similar results straightforwardly extend to kernels with finite feature spaces. Second, by allowing the kernel feature space to diverge with the sample size, the test can be large-sample valid for a wider class of functions. Third, for general kernels with possibly infinite-dimensional feature space, the partial permutation test is exactly valid when the covariates are exactly balanced across all groups, or asymptotically valid when the underlying function follows certain regularized Gaussian processes. We further suggest test statistics using likelihood ratio between two (nested) GPR models, and propose computationally efficient algorithms utilizing the EM algorithm and Newton’s method, where the latter also involves Fisher scoring and quadratic programming and is particularly useful when EM suffers from slow convergence. Extensions to correlated and non-Gaussian noises have also been investigated theoretically or numerically. Furthermore, the test can be extended to use multiple kernels together and can thus enjoy properties from each kernel. Both simulation study and application illustrate the properties of the proposed test.
A Kaczmarz-inspired approach to accelerate the optimization of neural network wavefunctions
Creators: name: Goldshlager, Gil
name: Abrahamsen, Nilin
name: Lin, Lin
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computational Physics (physics.comp-ph)
subject: Machine Learning (cs.LG)
subject: Chemical Physics (physics.chem-ph)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences

description: Neural network wavefunctions optimized using the variational Monte Carlo method have been shown to produce highly accurate results for the electronic structure of atoms and small molecules, but the high cost of optimizing such wavefunctions prevents their application to larger systems. We propose the Subsampled Projected-Increment Natural Gradient Descent (SPRING) optimizer to reduce this bottleneck. SPRING combines ideas from the recently introduced minimum-step stochastic reconfiguration optimizer (MinSR) and the classical randomized Kaczmarz method for solving linear least-squares problems. We demonstrate that SPRING outperforms both MinSR and the popular Kronecker-Factored Approximate Curvature method (KFAC) across a number of small atoms and molecules, given that the learning rates of all methods are optimally tuned. For example, on the oxygen atom, SPRING attains chemical accuracy after forty thousand training iterations, whereas both MinSR and KFAC fail to do so even after one hundred thousand iterations.
description: Final proof for Journal of Computational Physics
A Particle Method for Solving Fredholm Equations of the First Kind
Creators: name: Crucinio, Francesca R.
name: Doucet, Arnaud
name: Johansen, Adam M.
Publisher: name: Taylor & Francis
Year: 2023
Subjects: subject: Biophysics
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Evolutionary Biology
subject: Mathematical Sciences not elsewhere classified
subject: Inorganic Chemistry
subject: FOS: Chemical sciences

description: Fredholm integral equations of the first kind are the prototypical example of ill-posed linear inverse problems. They model, among other things, reconstruction of distorted noisy observations and indirect density estimation and also appear in instrumental variable regression. However, their numerical solution remains a challenging problem. Many techniques currently available require a preliminary discretization of the domain of the solution and make strong assumptions about its regularity. For example, the popular expectation maximization smoothing (EMS) scheme requires the assumption of piecewise constant solutions which is inappropriate for most applications. We propose here a novel particle method that circumvents these two issues. This algorithm can be thought of as a Monte Carlo approximation of the EMS scheme which not only performs an adaptive stochastic discretization of the domain but also results in smooth approximate solutions. We analyze the theoretical properties of the EMS iteration and of the corresponding particle algorithm. Compared to standard EMS, we show experimentally that our novel particle method provides state-of-the-art performance for realistic systems, including motion deblurring and reconstruction of cross-section images of the brain from positron emission tomography.
Flexible Parallel Neural Network Architecture Model for Early Prediction of Lithium Battery Life
Creators: name: Jiang, Lidang
name: Li, Zhuoxiang
name: Hu, Changyan
name: Huang, Qingsong
name: He, Ge
Publisher: name: arXiv
Year: 2024
Subjects: subject: Machine Learning (cs.LG)
subject: Artificial Intelligence (cs.AI)
subject: Computational Engineering, Finance, and Science (cs.CE)
subject: FOS: Computer and information sciences

description: The early prediction of battery life (EPBL) is vital for enhancing the efficiency and extending the lifespan of lithium batteries. Traditional models with fixed architectures often encounter underfitting or overfitting issues due to the diverse data distributions in different EPBL tasks. An interpretable deep learning model of flexible parallel neural network (FPNN) is proposed, which includes an InceptionBlock, a 3D convolutional neural network (CNN), a 2D CNN, and a dual-stream network. The proposed model effectively extracts electrochemical features from video-like formatted data using the 3D CNN and achieves advanced multi-scale feature abstraction through the InceptionBlock. The FPNN can adaptively adjust the number of InceptionBlocks to flexibly handle tasks of varying complexity in EPBL. The test on the MIT dataset shows that the FPNN model achieves outstanding predictive accuracy in EPBL tasks, with MAPEs of 2.47%, 1.29%, 1.08%, and 0.88% when the input cyclic data volumes are 10, 20, 30, and 40, respectively. The interpretability of the FPNN is mainly reflected in its flexible unit structure and parameter selection: its diverse branching structure enables the model to capture features at different scales, thus allowing the machine to learn informative features. The approach presented herein provides an accurate, adaptable, and comprehensible solution for early life prediction of lithium batteries, opening new possibilities in the field of battery health monitoring.
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data
Creators: name: Ziaei, Navid
name: Nazari, Behzad
name: Eden, Uri T.
name: Widge, Alik
name: Yousefi, Ali
Publisher: name: arXiv
Year: 2024
Subjects: subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences
subject: I.5.1; G.3

description: Extracting meaningful information from high-dimensional data poses a formidable modeling challenge, particularly when the data is obscured by noise or represented through different modalities. This research proposes a novel non-parametric modeling approach, leveraging the Gaussian process (GP), to characterize high-dimensional data by mapping it to a latent low-dimensional manifold. This model, named the latent discriminative generative decoder (LDGD), employs both the data and associated labels in the manifold discovery process. We derive a Bayesian solution to infer the latent variables, allowing LDGD to effectively capture inherent stochasticity in the data. We demonstrate applications of LDGD on both synthetic and benchmark datasets. Not only does LDGD infer the manifold accurately, but its accuracy in predicting data points' labels surpasses state-of-the-art approaches. In the development of LDGD, we have incorporated inducing points to reduce the computational complexity of Gaussian processes for large datasets, enabling batch training for enhanced efficient processing and scalability. Additionally, we show that LDGD can robustly infer manifold and precisely predict labels for scenarios in that data size is limited, demonstrating its capability to efficiently characterize high-dimensional data with limited samples. These collective attributes highlight the importance of developing non-parametric modeling approaches to analyze high-dimensional data.
description: 40 pages, 6 figures
Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy
Creators: name: Slautin, Boris N.
name: Liu, Yongtao
name: Funakubo, Hiroshi
name: Kalinin, Sergei V.
Publisher: name: arXiv
Year: 2024
Subjects: subject: Machine Learning (cs.LG)
subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Computer and information sciences
subject: FOS: Physical sciences

description: The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the AE process, including strategies for hyperparameter tuning and high-level human interventions within the workflow loop. This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in Scanning Probe Microscopy. We explore the concept of 'seed effect', where the initial experiment setup has a substantial impact on the subsequent learning trajectory. Additionally, we introduce an approach of the seed point interventions in AE allowing the operator to influence the exploration process. Using a dataset from Piezoresponse Force Microscopy (PFM) on PbTiO3 thin films, we illustrate the impact of the 'seed effect' and in-loop seed interventions on the effectiveness of DKL in predicting material properties. The study highlights the importance of initial choices and adaptive interventions in optimizing learning rates and enhancing the efficiency of automated material characterization. This work offers valuable insights into designing more robust and effective AE workflows in microscopy with potential applications across various characterization techniques. The analysis code that supports the funding is publicly available at https://github.com/Slautin/2024_Seed_effect_DKL_BO.
description: 24 pages, 11 figures
C4Q: A Chatbot for Quantum
Creators: name: Aragonés-Soria, Yaiza
name: Oriol, Manuel
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computation and Language (cs.CL)
subject: Quantum Physics (quant-ph)
subject: FOS: Computer and information sciences
subject: FOS: Physical sciences

description: Quantum computing is a growing field that promises many real-world applications such as quantum cryptography or quantum finance. The number of people able to use quantum computing is however still very small. This limitation comes from the difficulty to understand the concepts and to know how to start coding. Therefore, there is a need for tools that can assist non-expert in overcoming this complexity. One possibility would be to use existing conversational agents. Unfortunately ChatGPT and other Large-Language Models produce inaccurate results. This article presents C4Q, a chatbot that answers accurately basic questions and guides users when trying to code quantum programs. Contrary to other approaches C4Q uses a pre-trained large language model only to discover and classify user requests. It then generates an accurate answer using an own engine. Thanks to this architectural design, C4Q's answers are always correct, and thus C4Q can become a support tool that makes quantum computing more available to non-experts.
description: Paper accepted in the 5th International Workshop on Quantum Software Engineering (Q-SE 2024)
ISIS Neutron and Muon Source - Scientific Data Management
Creators: name: Gonzalez-Beltran, Alejandra
Publisher: name: Zenodo
Year: 2021
Subjects: data management, large-scale facilities, ISIS neutron and muon source

description: This presentation is part of the "ExPaNDS session for gap and issue assessment" workshop. https://indico.maxiv.lu.se/event/5012/timetable/#20211207
eDNAqua-Plan Milestone 9: Operational input from all use cases on existing workflows and tools to be used in WP3
Creators: name: Derycke, Sofie
name: Beermann, Arne
name: Picazo Mozo, Antonio
name: Camacho, Antonio
name: Leese, Florian
Publisher: name: Zenodo
Year: 2025

description: Milestone 9 gathers feedback from freshwater and marine use cases from Work Package 4 to the tasks embedded in Work Package 3. Challenges faced by scientists when using and creating taxonomic reference databases, when submitting high throughput sequencing data to public repositories or DNA-based occurrence data to biodiversity portals are documented and translated into a set of recommendations that should be considered by WP3 when creating the digital ecosystem for DNA-based aquatic datasets.

The digital ecosystem for DNA-based biodiversity analyses of the future needs to address these issues to greatly improve the transparency, accessibility, and usability of DNA-based datasets for biodiversity research and monitoring.
Co-orchestration of Multiple Instruments to Uncover Structure-Property Relationships in Combinatorial Libraries
Creators: name: Slautin, Boris N.
name: Pratiush, Utkarsh
name: Ivanov, Ilia N.
name: Liu, Yongtao
name: Pant, Rohit
name: Zhang, Xiaohang
name: Takeuchi, Ichiro
name: Ziatdinov, Maxim A.
name: Kalinin, Sergei V.
Publisher: name: arXiv
Year: 2024
Subjects: subject: Materials Science (cond-mat.mtrl-sci)
subject: Machine Learning (cs.LG)
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences

description: The rapid growth of automated and autonomous instrumentations brings forth an opportunity for the co-orchestration of multimodal tools, equipped with multiple sequential detection methods, or several characterization tools to explore identical samples. This can be exemplified by the combinatorial libraries that can be explored in multiple locations by multiple tools simultaneously, or downstream characterization in automated synthesis systems. In the co-orchestration approaches, information gained in one modality should accelerate the discovery of other modalities. Correspondingly, the orchestrating agent should select the measurement modality based on the anticipated knowledge gain and measurement cost. Here, we propose and implement a co-orchestration approach for conducting measurements with complex observables such as spectra or images. The method relies on combining dimensionality reduction by variational autoencoders with representation learning for control over the latent space structure, and integrated into iterative workflow via multi-task Gaussian Processes (GP). This approach further allows for the native incorporation of the system's physics via a probabilistic model as a mean function of the GP. We illustrated this method for different modalities of piezoresponse force microscopy and micro-Raman on combinatorial $Sm-BiFeO_3$ library. However, the proposed framework is general and can be extended to multiple measurement modalities and arbitrary dimensionality of measured signals. The analysis code that supports the funding is publicly available at https://github.com/Slautin/2024_Co-orchestration.
description: 22 pages, 9 figures