Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of detectors at the electron ion collider with artificial intelligence

Artificial Intelligence (AI) for design is a relatively new but active area of research across many disciplines. Surprisingly when it comes to designing detectors with AI this is an area at its infancy. The electron ion collider is the ultimate machine to study the strong force. The EIC is a large-scale experiment with an integrated detector that extends for about ±35 meters to include the central, far-forward, and far-backward regions. The design of the central detector is made by multiple sub-detectors, each in principle characterized by a multidimensional design space and multiple design criteria also called objectives. Simulations with Geant4 are typically compute intensive, and the optimization of the detector design may include non-differentiable terms as well as noisy objectives. In this context, AI can offer state of the art solutions to solve complex combinatorial problems in an efficient way. In particular, one of the proto-collaborations, ECCE, has explored during the detector proposal the possibility of using multi-objective optimization to design the tracking system of the EIC detector. This document provides an overview of these techniques and recent progress made during the EIC detector proposal. Future high energy nuclear physics experiments can leverage AI-based strategies to design more efficient detectors by optimizing their performance driven by physics criteria and minimizing costs for their realization.
Unusual phase formation in reactively sputtered La-Co-O perovskite thin films

La-based perovskites are a versatile class of materials that are of interest for solid oxide fuel cells and electrocatalytic water splitting. During fabrication of composition spread materials libraries of La-Co-based oxide systems for the discovery of new catalytic materials, an unusual phase formation phenomenon was observed: instead of the expected continuous composition gradient, regions with homogeneous composition and single-phase structure (La2O3 or stoichiometric La-perovskite) form. This phenomenon occurs during reactive co-sputtering and is dependent on O2-flux and substrate temperature, investigated from room temperature up to 700 °C and is independent of the used substrate. It can be described as a self-organized growth, where excess transition metal cannot be incorporated into the growing film and the forming single-phase regions. It is hypothesized that due to the high reactivity of La and the significantly low formation energies of La2O3 and La-perovskites, the reactive sputter deposition of La-based oxide films can turn – regarding film growth – into a partial CVD-like process which results in the unusual self-organized growth of single-phase regions. This phenomenon can be leveraged for the exploration of multinary perovskite thin film libraries, where the B-site atoms of Laperovskites are systematically substituted.
3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs

In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner. This is achieved by seamlessly combining standard convolutional neural networks (CNNs) with Graph Convolution Networks (GCNs). By iteratively and hierarchically fusing the features across different layers and stages of the CNNs and GCNs, our approach can provide a dense face alignment and 3D face reconstruction simultaneously for the benefit of direct feature learning of 3D face mesh. Experiments on several challenging datasets demonstrate that our method outperforms state-of-the-art approaches on both 2D and 3D face alignment tasks.
Tune your Place Recognition: Self-Supervised Domain Calibration via Robust SLAM

—Visual place recognition techniques based on deep learning, which have imposed themselves as the state-of-the-art in recent years, do not always generalize well to environments that are visually diﬀerent from the training set. Thus, to achieve top performance, it is sometimes necessary to ﬁne-tune the networks to the target environment. To this end, we propose a completely self-supervised domain calibration procedure based on robust pose graph estimation from Simultaneous Localization and Mapping (SLAM) as the supervision signal without requiring GPS or manual labeling. We ﬁrst show that the training samples produced by our technique are suﬃcient to train a visual place recognition system from a pre-trained classiﬁcation model. Then, we show that our approach can improve the performance of a state-of-the-art technique on a target environment dissimilar from the training set. We believe that this approach will help practitioners to deploy more robust place recognition solutions in real-world applications.
User's guide to SMATR : standardised major axis tests and routines version 2.0, copyright 2006

SMATR is a freeware program used for fitting bivariate lines to data and for making inferences about such lines. A line can be fitted using standardised major axis (SMA), major axis (MA) or ordinary least squares regression (OLS) techniques. The routines available in SMATR allow you to: - fit lines to groups of data using SMA, MA or OLS methods - construct exact confidence intervals around slope and intercept parameters and compare these to hypothesised values - test for a common a common slope or elevation among lines fitted to several groups - test for a shift along a common axis among lines fitted to several groups - fit lines forced through the origin (appropriate for phylogenetic analyses) - incorporate measurement error into parameter estimates. Details about the use of these statistics and about the underlying formulas and their validation can be found in: Warton, DI, IJ Wright, DS Falster, and M Westoby (2006) Bivariate line-fitting methods for allometry.
Spectral Efficiency of Unicast and Multigroup Multicast Transmission in Cell-Free Distributed Massive MIMO Systems

In this article, we consider a joint unicast and multi-group multicast cell-free distributed massive multiple-input multiple-output (MIMO) system, while accounting for co-pilot assignment strategy based channel estimation, pilot contamination and different precoding schemes. Under the co-pilot assignment strategy, we derive the minimum-mean-square error (MMSE) channel state information (CSI) estimation for unicast and multicast users. Given the acquired CSI, the closed-form expressions for downlink achievable rates with maximum ratio transmission (MRT), zero-forcing (ZF) and MMSE beamforming are derived. Based on these expressions, we propose an efficient power allocation scheme by solving a multi-objective optimization problem (MOOP) between maximizing the minimum spectral efficiency (SE) of multicast users and maximizing the average SE of unicast users with non-dominated sorting genetic algorithm II (NSGA-II). Moreover, the MOOP is converted into a deep learning (DL) problem and solved by an unsupervised learning method to further promote computational efficiency. Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the joint unicast and multigroup multicast transmission scheme in cell-free distributed massive MIMO systems. The SE analysis under various system parameters and the trade-off regions between these two conflicting optimization objectives offers numerous flexibilities for system optimization.
Monitoring Time Series With Missing Values: a Deep Probabilistic Approach

Systems are commonly monitored for health and security through collection and streaming of multivariate time series. Advances in time series forecasting due to adoption of multilayer recurrent neural network architectures make it possible to forecast in high-dimensional time series, and identify and classify novelties early, based on subtle changes in the trends. However, mainstream approaches to multi-variate time series predictions do not handle well cases when the ongoing forecast must include uncertainty, nor they are robust to missing data. We introduce a new architecture for time series monitoring based on combination of state-of-the-art methods of forecasting in high-dimensional time series with full probabilistic handling of uncertainty. We demonstrate advantage of the architecture for time series forecasting and novelty detection, in particular with partially missing data, and empirically evaluate and compare the architecture to state-of-the-art approaches on a real-world data set.
Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees

Deep neural network-based classifiers have been shown to be vulnerable to imperceptible perturbations to their input, such as $\ell_p$-bounded norm adversarial attacks. This has motivated the development of many defense methods, which are then broken by new attacks, and so on. This paper focuses on a different but related problem of reverse engineering adversarial attacks. Specifically, given an attacked signal, we study conditions under which one can determine the type of attack ($\ell_1$, $\ell_2$ or $\ell_\infty$) and recover the clean signal. We pose this problem as a block-sparse recovery problem, where both the signal and the attack are assumed to lie in a union of subspaces that includes one subspace per class and one subspace per attack type. We derive geometric conditions on the subspaces under which any attacked signal can be decomposed as the sum of a clean signal plus an attack. In addition, by determining the subspaces that contain the signal and the attack, we can also classify the signal and determine the attack type. Experiments on digit and face classification demonstrate the effectiveness of the proposed approach.
PET: A new Dataset for Process Extraction from Natural Language Text

Although there is a long tradition of work in NLP on extracting entities and relations from text, to date there exists little work on the acquisition of business processes from unstructured data such as textual corpora of process descriptions. With this work we aim at ﬁlling this gap and establishing the ﬁrst steps towards bridging data-driven information extraction methodologies from Natural Language Processing and the model-based formalization that is aimed from Business Process Management. For this, we develop the ﬁrst corpus of business process descriptions annotated with activities, gateways, actors and ﬂow information. We present our new resource, including a detailed overview of the annotation schema and guidelines, as well as a variety of baselines to benchmark the diﬃculty and challenges of business process extraction from text.
Error-based Knockoffs Inference for Controlled Feature Selection

Recently, the scheme of model-X knockoffs was proposed as a promising solution to address controlled feature selection under high-dimensional finite-sample settings. However, the procedure of model-X knockoffs depends heavily on the coefficient-based feature importance and only concerns the control of false discovery rate (FDR). To further improve its adaptivity and flexibility, in this paper, we propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together. The proposed inference procedure does not require specifying a regression model and can handle feature selection with theoretical guarantees on controlling false discovery proportion (FDP), FDR, or k-familywise error rate (k-FWER). Empirical evaluations demonstrate the competitive performance of our approach on both simulated and real data.
Collective Variable for Metadynamics Derived From AlphaFold Output

AlphaFold is a neural network–based tool for the prediction of 3D structures of proteins. In CASP14, a blind structure prediction challenge, it performed significantly better than other competitors, making it the best available structure prediction tool. One of the outputs of AlphaFold is the probability profile of residue–residue distances. This makes it possible to score any conformation of the studied protein to express its compliance with the AlphaFold model. Here, we show how this score can be used to drive protein folding simulation by metadynamics and parallel tempering metadynamics. Using parallel tempering metadynamics, we simulated the folding of a mini-protein Trp-cage and β hairpin and predicted their folding equilibria. We observe the potential of the AlphaFold-based collective variable in applications beyond structure prediction, such as in structure refinement or prediction of the outcome of a mutation.
Multi-Scale Adaptive Network for Single Image Denoising

Multi-scale architectures have shown effectiveness in a variety of tasks thanks to appealing cross-scale complementarity. However, existing architectures treat different scale features equally without considering the scale-specific characteristics, \textit{i.e.}, the within-scale characteristics are ignored in the architecture design. In this paper, we reveal this missing piece for multi-scale architecture design and accordingly propose a novel Multi-Scale Adaptive Network (MSANet) for single image denoising. Specifically, MSANet simultaneously embraces the within-scale characteristics and the cross-scale complementarity thanks to three novel neural blocks, \textit{i.e.}, adaptive feature block (AFeB), adaptive multi-scale block (AMB), and adaptive fusion block (AFuB). In brief, AFeB is designed to adaptively preserve image details and filter noises, which is highly expected for the features with mixed details and noises. AMB could enlarge the receptive field and aggregate the multi-scale information, which meets the need of contextually informative features. AFuB devotes to adaptively sampling and transferring the features from one scale to another scale, which fuses the multi-scale features with varying characteristics from coarse to fine. Extensive experiments on both three real and six synthetic noisy image datasets show the superiority of MSANet compared with 12 methods. The code could be accessed from https://github.com/XLearning-SCU/2022-NeurIPS-MSANet.
Spectral Compressive Imaging Reconstruction Using Convolution and Spectral Contextual Transformer

Spectral compressive imaging (SCI) is able to encode the high-dimensional hyperspectral image to a 2D measurement, and then uses algorithms to reconstruct the spatio-spectral data-cube. At present, the main bottleneck of SCI is the reconstruction algorithm, and the state-of-the-art (SOTA) reconstruction methods generally face the problem of long reconstruction time and/or poor detail recovery. In this paper, we propose a novel hybrid network module, namely CCoT (Convolution and Contextual Transformer) block, which can acquire the inductive bias ability of convolution and the powerful modeling ability of transformer simultaneously,and is conducive to improving the quality of reconstruction to restore fine details. We integrate the proposed CCoT block into deep unfolding framework based on the generalized alternating projection algorithm, and further propose the GAP-CCoT network. Through the experiments of extensive synthetic and real data, our proposed model achieves higher reconstruction quality ($>$2dB in PSNR on simulated benchmark datasets) and shorter running time than existing SOTA algorithms by a large margin. The code and models are publicly available at https://github.com/ucaswangls/GAP-CCoT.
Sound-to-Imagination: An Exploratory Study on Unsupervised Crossmodal Translation Using Diverse Audiovisual Data

The motivation of our research is to explore the possibilities of automatic sound-to-image (S2I) translation for enabling a human receiver to visually infer the occurrence of sound related events. We expect the computer to 'imagine' the scene from the captured sound, generating original images that picture the sound emitting source. Previous studies on similar topics opted for simplified approaches using data with low content diversity and/or sound class supervision. Differently, we propose to perform unsupervised S2I translation using thousands of distinct and unknown scenes, with slightly pre-cleaned data, just enough to guarantee aural-visual semantic coherence. To that end, we employ conditional generative adversarial networks (GANs) with a deep densely connected generator. Additionally, we present a solution using informativity classifiers to perform quantitative evaluation of the generated images. This enabled us to analyze the influence of network bottleneck variation over the translation, observing a potential trade-off between informativity and pixel space convergence. Despite the complexity of the specified S2I translation task, we were able to generalize the model enough to obtain more than 14%, in average, of interpretable and semantically coherent images translated from unknown sounds.
How to reduce the search space of Entity Resolution: with Joins, Blocking or Nearest Neighbor search?

Entity Resolution is the task of identifying pairs of entity profiles that represent the same real-world object. To avoid checking a quadratic number of entity pairs, various filtering techniques have been proposed that fall into three main categories: (i) blocking workflows group together entity profiles with identical or similar signatures, (ii) string similarity joins identify entity pairs that exceed a user-defined similarity threshold, and (iii) nearest-neighbor methods convert all entity profiles into vectors and identify the closest ones to every query entity. Unfortunately, the main techniques from these different categories have rarely been compared in the literature and, thus, their relative performance is unknown. We perform the first systematic experimental study that investigates the relative performance of the main representatives per category over 10 real-world datasets. Comparing techniques from different categories turns out to be a non-trivial task due to the various configuration parameters that are hard to fine-tune, but have a significant impact on performance. We consider a plethora of parameter configurations, optimizing each technique with respect to recall and precision targets. Both schema-agnostic and schema-based settings are evaluated. The experimental results provide novel insights into the effectiveness and time efficiency of the considered techniques, which are condensed in a systematic overview
SEMsens: An R Package for Sensitivity Analysis of Structural Equation Models With the Ant Colony Optimization Algorithm

The SEMsens package (Leite & Shen, 2021) for the R statistical software (R Core Team, 2021) implements the framework proposed by Leite et al. (in press) to conduct sensitivity analysis for structural equation modeling (SEM) against a potential missing confounder using the ant colony optimization (ACO) algorithm. A potential missing confounder is specified as a phantom variable, a latent variable without manifest indicators, such that the sensitivity of SEM can be assessed through comparing results from models with and without the phantom variable (Harring et al., 2017). The current SEMsens package can provide results and summary tables of sensitivity analysis for SEM with respect to the objective functions pre-coded in the package, such as objective functions based on p-values, parameter estimates, and model fit, or customized by users. The automation method works by sampling different phantom variables with probabilities according to the rank of solutions to maximize the objective function with the use of the ant colony optimization algorithm for continuous domains (Socha & Dorigo, 2008). The SEMsens package investigates problems for external model misspecification (Kaplan, 1990) by allowing researchers to understand the extent that each path coefficient in a model is vulnerable to a potential omitted confounder. If the magnitude or p-value of a path coefficient is found to be likely to change substantially if a weak or moderate confounder is included, researchers can consult theory to review the possibility of such a confounder and possibly expand the model in future studies. If only strong confounders would change the conclusions for the model, that provides additional evidence for the validity of the model. Currently, the package is designed to work with SEMmodels with raw data or sample variance covariance matrices, estimating SEM models through the lavaan package in R (Rosseel, 2012).
Nanocolloid simulators of luminescent solar concentrator photovoltaic windows

Abstract Transparent luminescent solar concentrator (LSC) windows with edge-attached photovoltaic (PV) cells have the potential for improving building efficiency without compromising aesthetics and comfort. Optimization of such windows requires an inexpensive simulator for experimenting with various designs. We report, for the first time to the best of our knowledge, the simulator of a transparent LSC window in the form of a plastic container filled with a colloid of photoluminescent nanoparticles (NPs) in an organic solvent (1-propanol). The exemplary NPs were produced by ball milling of the powder of rare earth (RE)-doped phosphor NaYF4:Yb3+,Er3+ synthesized by the wet method. The NPs converted the ultraviolet (UV) solar spectrum into visible/near infrared (NIR) via spectral down-shifting and down-conversion (quantum cutting). With a photoluminescence quantum yield (PLQY) of the phosphor <0.4%, the LSC at a nanocolloid concentration of ∼0.1 g solids per 100 mL liquids demonstrated a power conversion efficiency of 0.34% and a power concentration ratio of ∼0.022 comparable to the LSCs with RE-doped NPs with 200 times greater PLQY. At the same time, the 3 cm thick LSC window simulator had ∼90% transmittance to the sunlight. The content and concentration of the nanocolloid could be easily modified to optimize the LSC window performance without a costly window making process.
A quantitative man-machine model for cyber security efficiency analysis

The analysis of security defense processes is of utmost importance in the management of various cyber-security attacks, which are increasing in scope and rapidity. Organizations need to optimize their resources based on a sound understanding of the level of their security defense processes' efficiency and the impact of their investment. Modeling and characterization of the dynamics of cyber security management are essential to risk prediction, damage assessment, and resource allocations. This dissertation addresses the interactions between human factors and information systems. On the basis of the spiral life cycle model of software development processes, we develop a realistic, holistic security attack-defense model---Man-Machine Model (M3), which combines human factors and information systems' (i.e., machine) states under an integrated analytical framework. M 3 incorporates man and machine components. The man component is comprised of several variables such as Skill & Knowledge (SKKN) and Teamwork Quality (TWQ). The machine component is composed of variables such as traffic volume and the amount of downtime. M3 enables the analysis of intrusion detection and incident response process efficiency, i.e., security defense team performance. With data analysis, we formulate and test four major research hypotheses based on the data collected during security experiments. Through hypothesis testing, we evaluate regression models to estimate the security defense team performance (i.e. efficiency) at different levels of human intelligence (e.g., skill and knowledge) and teamwork (e.g., teamwork quality). We assess the fitness and significance of the regression models, and verify their assumptions. Based on these results, organizations can hire those who have an appropriate level of skill and knowledge when it concerns investments to increase the level of skill and knowledge of security personnel. They also can attempt to increase the level of skill and knowledge of security personnel.