Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of an Atomic Mobility Game With Uncertainty Under Prospect Theory

In this paper, we present a study of a mobility game with uncertainty in the decision-making of travelers and incorporate prospect theory to model travel behavior. We formulate a mobility game that models how travelers distribute their traffic flows in a transportation network with splittable traffic, utilizing the Bureau of Public Roads function to establish the relationship between traffic flow and travel time cost. Given the inherent non-linearities and complexity introduced by the uncertainties, we propose a smooth approximation function to estimate the prospect-theoretic cost functions. As part of our analysis, we characterize the best-fit parameters and derive an upper bound for the error. We then show the existence of an equilibrium and its its best-possible approximation.
Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids

In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications. In the recent years, numerous smart grid applications, such as defect detection and load forecasting, have embraced data-driven methodologies. The purpose of this study is to investigate the challenges associated with the security of machine learning (ML) applications in the smart grid scenario. Indeed, the robustness and security of these data-driven algorithms have not been extensively studied in relation to all power grid applications. We demonstrate first that the deep neural network method used in the smart grid is susceptible to adversarial perturbation. Then, we highlight how studies on fault localization and type classification illustrate the weaknesses of present ML algorithms in smart grids to various adversarial attacks
DOA Estimation for UAV Positioning using Mixed-ADCs: Performance Loss and Energy Efﬁciency

—Due to the power consumption and high circuit cost in antenna arrays, the practical application of multiple-input multiple-output (MIMO) in the unmanned aerial vehicle (UAV) communications and positioning is still challenging. Employing low-resolution analog-to-digital converters (ADCs) and hybrid analog and digital (HAD) structure is two low-cost choice with acceptable performance loss. In this paper, the combination of the mixed-ADC architecture and HAD structure employed at receiver is proposed for direction of arrival (DOA) estimation, which will be applied to the beamforming tracking and alignment in UAV positioning. By adopting the additive quantization noise model, the exact closed-form expression of the Cram´er-Rao lower bound (CRLB) for the HAD architecture with mixed-ADCs is derived. Moreover, the closed-form expression of the performance loss factor is derived as a benchmark. In addition, to take power consumption into account, energy efﬁciency is also investigated in our paper. The numerical results reveal that the HAD structure with mixed-ADCs can signiﬁcantly reduce the power consumption and hardware cost. Furthermore, that architecture is able to achieve a better trade-off between the performance loss and the power consumption. Finally, adopting 2-4 bits of resolution may be a good choice in practical massive MIMO systems.
Safe Control of Partially-Observed Linear Time-Varying Systems with Minimal Worst-Case Dynamic Regret

We present safe control of partially-observed linear time-varying systems in the presence of unknown and unpredictable process and measurement noise. We introduce a control algorithm that minimizes dynamic regret, i.e., that minimizes the suboptimality against an optimal clairvoyant controller that knows the unpredictable future a priori. Specifically, our algorithm minimizes the worst-case dynamic regret among all possible noise realizations given a worst-case total noise magnitude. To this end, the control algorithm accounts for three key challenges: safety constraints; partially-observed time-varying systems; and unpredictable process and measurement noise. We are motivated by the future of autonomy where robots will autonomously perform complex tasks despite unknown and unpredictable disturbances leveraging their on-board control and sensing capabilities. To synthesize our minimal-regret controller, we formulate a constrained semi-definite program based on a System Level Synthesis approach for partially-observed time-varying systems. We validate our algorithm in simulated scenarios, including trajectory tracking scenarios of a hovering quadrotor collecting GPS and IMU measurements. Our algorithm is observed to have better performance than either or both the 'H2 and controllers, demonstrating a Best of Both Worlds performance.
NearPM: A Near-Data Processing System for Storage-Class Applications

Persistent Memory (PM) technologies enable both fast memory access and recovery in case of a failure. To ensure crash-consistent behavior, programs need to enforce persist ordering and employ mechanisms that introduce additional data movements such as logging, checkpointing, and shadow-paging. The emerging near-data processing (NDP) architectures can effectively reduce this overhead. In this work, we propose NearPM, a near-data processor that accelerates common, primitive operations that are crucial to crash consistency. Using these primitives, NearPM accelerates commonly-used crash-consistency mechanisms. NearPM further reduces the synchronization overheads between the NDP and the CPU by handling ordering near memory. We propose Partitioned Persist Ordering (PPO) that ensures a correct persist ordering between CPU and NDP devices, as well as among multiple NDP devices. We prototype NearPM on an FPGA platform. NearPM executes the data-intensive operations of crash-consistency mechanisms with correct ordering guarantees, while the rest of the program runs on the CPU. We evaluate nine PM workloads, each implemented in three crash consistency mechanisms: logging, checkpointing, and shadow paging. Overall, NearPM achieves 4.3 -- 9.8× speedup in the NDP-offloaded operations and 1.22 -- 1.35× speedup in the whole applications.
Bounded Simplex-Structured Matrix Factorization: Algorithms, Identifiability and Applications

In this article, we propose a new low-rank matrix factorization model dubbed bounded simplex-structured matrix factorization (BSSMF). Given an input matrix <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula> and a factorization rank <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula>, BSSMF looks for a matrix <inline-formula><tex-math notation="LaTeX">$W$</tex-math></inline-formula> with <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> columns and a matrix <inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> with <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> rows such that <inline-formula><tex-math notation="LaTeX">$X \approx WH$</tex-math></inline-formula> where the entries in each column of <inline-formula><tex-math notation="LaTeX">$W$</tex-math></inline-formula> are bounded, that is, they belong to given intervals, and the columns of <inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> belong to the probability simplex, that is, <inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> is column stochastic. BSSMF generalizes nonnegative matrix factorization (NMF), and simplex-structured matrix factorization (SSMF). BSSMF is particularly well suited when the entries of the input matrix <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula> belong to a given interval; for example when the rows of <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula> represent images, or <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula> is a rating matrix such as in the Netflix and MovieLens datasets where the entries of <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula> belong to the interval [1,5]. The simplex-structured matrix <inline-formula><tex-math notation="LaTeX">$H$</tex-math></inline-formula> not only leads to an easily understandable decomposition providing a soft clustering of the columns of <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula>, but implies that the entries of each column of <inline-formula><tex-math notation="LaTeX">$WH$</tex-math></inline-formula> belong to the same intervals as the columns of <inline-formula><tex-math notation="LaTeX">$W$</tex-math></inline-formula>. In this article, we first propose a fast algorithm for BSSMF, even in the presence of missing data in <inline-formula><tex-math notation="LaTeX">$X$</tex-math></inline-formula>. Then we provide identifiability conditions for BSSMF, that is, we provide conditions under which BSSMF admits a unique decomposition, up to trivial ambiguities. Finally, we illustrate the effectiveness of BSSMF on two applications: extraction of features in a set of images, and the matrix completion problem for recommender systems.
Intraspecific trait plasticity to N and P of the wetland invader, Alternanthera philoxeroides under flooded conditions

Abstract Interactions between invaders and resource availability may explain variation in their success or management efficacy. For widespread invaders, regional variation in plant response to nutrients can reflect phenotypic plasticity of the invader, genetic structure of invading populations, or a combination of the two. The wetland weed Alternanthera philoxeroides (alligatorweed) is established throughout the southeastern United States and California and has high genetic diversity despite primarily spreading clonally. Despite its history in the United States, the role of genetic variation for invasion and management success is only now being uncovered. To better understand how nutrients and genotype may influence A. philoxeroides invasion, we measured the response of plants from 26 A. philoxeroides populations (three cp haplotypes) to combinations of nitrogen (4 or 200 mg/L N) and phosphorus (0.4 or 40 mg/L P). We measured productivity (biomass accumulation and allocation), plant architecture (stem diameter and thickness, branching intensity), and foliar traits (toughness, dry matter content, percent N, and percent P). A short‐term developmental assay was also conducted by feeding a subset of plants from the nutrient experiment to the biological control agent Agasicles hygrophila, to determine whether increased availability of N or P to its host influenced agent performance, as has been previously suggested. Alternanthera philoxeroides haplotype Ap1 was more plastic than other haplotypes in response to nutrient amendments, producing more than double the biomass from low to high N and 50%–68% higher shoot: root ratio than other haplotypes in the high N treatment. Alternanthera philoxeroides haplotypes differed in seven of 10 variables in response to increased N. We found no differences in short‐term A. hygrophila development between haplotypes but mass was 23% greater in high than low N treatments. This study is the first to explore the interplay between nutrient availability, genetic variation, and phenotypic plasticity in invasive characteristics of the global invader, A. philoxeroides.
Boolean network sketches: a unifying framework for logical model inference

Abstract Motivation The problem of model inference is of fundamental importance to systems biology. Logical models (e.g. Boolean networks; BNs) represent a computationally attractive approach capable of handling large biological networks. The models are typically inferred from experimental data. However, even with a substantial amount of experimental data supported by some prior knowledge, existing inference methods often focus on a small sample of admissible candidate models only. Results We propose Boolean network sketches as a new formal instrument for the inference of Boolean networks. A sketch integrates (typically partial) knowledge about the network’s topology and the update logic (obtained through, e.g. a biological knowledge base or a literature search), as well as further assumptions about the properties of the network’s transitions (e.g. the form of its attractor landscape), and additional restrictions on the model dynamics given by the measured experimental data. Our new BNs inference algorithm starts with an ‘initial’ sketch, which is extended by adding restrictions representing experimental data to a ‘data-informed’ sketch and subsequently computes all BNs consistent with the data-informed sketch. Our algorithm is based on a symbolic representation and coloured model-checking. Our approach is unique in its ability to cover a broad spectrum of knowledge and efficiently produce a compact representation of all inferred BNs. We evaluate the method on a non-trivial collection of real-world and simulated data. Availability and implementation All software and data are freely available as a reproducible artefact at https://doi.org/10.5281/zenodo.7688740.
Adaptive deconvolution and identification of nonminimum phase FIR systems based on cumulants

A novel adaptive deconvolution and system identification scheme is introduced for a linear, non-minimum-phase finite-impulse-response (FIR) system driven by non-Gaussian white noise. The adaptive scheme is based on approximating the FIR system by noncausal autoregressive (AR) models and using higher order cumulants of the system output. As such, it is a blind equalization (deconvolution) scheme. The set of updated AR parameters is obtained by using a gradient-type algorithm and by using higher order cumulants instead of time samples of the output signal. It is demonstrated by means of extensive simulations that the adaptive scheme works well for both stationary and nonstationary cases. As expected, it outperforms the autocorrelation-based gradient method for nonminimum-phase system identification and deconvolution. Performance comparisons to existing methods are given, using as figures of merit the probability of errors in the restored input sequence, computational complexity, and convergence rate. >
Three-dimensional simulation of the electromagnetic ion/ion beam instability: cross field diffusion

Abstract. In a system with at least one ignorable spatial dimension charged particles moving in fluctuating fields are tied to the magnetic field lines. Thus, in one-and two-dimensional simulations cross-field diffusion is inhibited and important physics may be lost. We have investigated cross-field diffusion in self-consistent 3-D magnetic turbulence by fully 3-dimensional hybrid simulation (macro-particle ions, massless electron fluid). The turbulence is generated by the electromagnetic ion/ion beam instability. A cold, low density, ion beam with a high velocity stream relative to the background plasma excites the right-hand resonant instability. Such ion beams may be important in the region of the Earth's foreshock. The field turbulence scatters the beam ions parallel as well as perpendicular to the magnetic field. We have determined the parallel and perpendicular diffusion coefficient for the beam ions in the turbulent wave field. The result compares favourably well (within a factor 2) with hard-sphere scattering theory for the cross-field diffusion coefficient. The cross-field diffusion coefficient is larger than that obtained in a static field with a Kolmogorov type spectrum and similar total fluctuation power. This is attributed to the resonant behaviour of the particles in the fluctuating field.
Effects of Pre-Purchase Search Motivation on User Attitudes toward Online Social Network Advertising: A Case of University Students

Since last few years, social media have profoundly changed the ways of social and business communication. Particularly, social network sites (SNSs) have rapidly grown in popularity and number of users globally. They have become the main place for social interaction, discussion and communication. Today, businesses of various types use SNSs for commercial communication. Banner advertising is one of the common methods of commercial communication on SNSs. Advertising is a key source of revenue for many SNSs firms such as Facebook. In fact, the ex- istence of many SNSs owners and advertisers is contingent upon the success of social network advertising (SNA). Users demand free SNS services which makes SNA crucial for SNSs firms. SNA can be effective only if it is aligned with user motivations. Marketing literature identifies pre-purchase search as a primary consumer motivation for using media. The current study aims to identify the effects of pre-purchase search motivation (PSM) on user attitudes toward SNA. It also assesses the association between the attitudes toward SNA and users' banner ad-clicking behavior on SNSs. Data was gathered from 200 university students in Islamabad using offline survey. Results show positive effects of PSM on user attitudes toward SNA. They also show posi- tive association between user attitudes toward SNA and their SNS banner ad-clicking behavior. The firms which promote their products through SNSs to the young South Asian consumers may benefit from the findings of the current study.
Sin3a Associated Protein 130kDa, sap130, plays an evolutionary conserved role in zebrafish heart development

Hypoplastic left heart syndrome (HLHS) is a congenital heart disease where the left ventricle is reduced in size. A forward genetic screen in mice identified SIN3A associated protein 130kDa (Sap130), a protein in the chromatin modifying SIN3A/HDAC1 complex, as a gene contributing to the digenic etiology of HLHS. Here, we report the role of zebrafish sap130 genes in heart development. Loss of sap130a, one of two Sap130 orthologs, resulted in smaller ventricle size, a phenotype reminiscent to the hypoplastic left ventricle in mice. While cardiac progenitors were normal during somitogenesis, diminution of the ventricle size suggest the Second Heart Field (SHF) was the source of the defect. To explore the role of sap130a in gene regulation, transcriptome profiling was performed after the heart tube formation to identify candidate pathways and genes responsible for the small ventricle phenotype. Genes involved in cardiac differentiation and cell communication were dysregulated in sap130a, but not in sap130b mutants. Confocal light sheet analysis measured deficits in cardiac output in MZsap130a supporting the notion that cardiomyocyte maturation was disrupted. Lineage tracing experiments revealed a significant reduction of SHF cells in the ventricle that resulted in increased outflow tract size. These data suggest that sap130a is involved in cardiogenesis via regulating the accretion of SHF cells to the growing ventricle and in their subsequent maturation for cardiac function. Further, genetic studies revealed an interaction between hdac1 and sap130a, in the incidence of small ventricles. These studies highlight the conserved role of Sap130a and Hdac1 in zebrafish cardiogenesis.
Space–time calibration of radar rainfall data

Motivated by a specific problem concerning the relationship between radar reflectance and rainfall intensity, the paper develops a space–time model for use in environmental monitoring applications. The model is cast as a high dimensional multivariate state space time series model, in which the cross‐covariance structure is derived from the spatial context of the component series, in such a way that its interpretation is essentially independent of the particular set of spatial locations at which the data are recorded. We develop algorithms for estimating the parameters of the model by maximum likelihood, and for making spatial predictions of the radar calibration parameters by using realtime computations. We apply the model to data from a weather radar station in Lancashire, England, and demonstrate through empirical validation the predictive performance of the model.
Two Novel Techniques for Graph Optimization - Cycle Based Formulation and Change of Optimal Values

by Fang Bai, Ph.D. University of Technology Sydney Jan 2020 Graph optimization (GO) is an essential enabling technique widely used in the simultaneous localization and mapping (SLAM), sensor fusion, Lidar based or visual-inertial based navigation systems (VINS). As its name suggests, the graph optimization lies at the intersection of probabilistic inference, sparse linear algebra, and graph theory. In its explicit form, it is a sparse least squares derived from a maximum likelihood estimation (MLE). This thesis contains two fundamental contributions regarding this topic. A GO can be conventionally represented as a graph with vertices being (unobserved) latent variables, and edges being observed measurements. This vertex-edge paradigm has dominated the GO literature, which in essence solves the problem in the cut space of the graph. In this thesis, we firstly investigate a special GO instance, i.e., pose-graph optimization (PGO), and propose an orthogonal complementary formulation that solves PGO in the cycle space. For sparse graphs, which is typically the case for PGO, the cycle based formulation has a lower dimension of state variables, and takes a form of minimum norm optimization. By exploiting the sparsity by a minimum cycle basis (MCB), the cycle based PGO yields a superior convergence property against its vertex-based counterpart while being cheaper to compute. The second contribution is the theory on how to forecast the change of optimal values (COOV) in incrementally constructed GO instances. In specific, this thesis develops analytical equations to calculate COOV in case of least squares optimization, and minimum norm optimization. The equation is exactly proved under linear cases, and extends to nonlinear cases via linearizations. We show that COOV bears the same computational complexity as the mutual information (MI) in incremental scenarios, while both COOV and MI well complement one another. As a final contribution, we design several derived applications based on the proposed COOV metric, that demonstrates its effectiveness in outlier detection, cost forecasting, and enhancing the overall robustness in incremental settings. It can be foreseen that numerous applications can be generated based on the two cornerstones in this thesis, and the author would like to leave this part as a future research direction, and open to the community.
Deterministic and Probabilistic Q-Ball Tractography: from Diffusion to Sharp Fiber Distribution

We propose a deterministic and a probabilistic extension of classical diffusion tensor imaging (DTI) tractography algorithms based on a sharp fiber orientation distribution function (ODF) reconstruction from Q-Ball Imaging (QBI). An important contribution of the paper is the integration of some of the latest state-of-the-art high angular resolution diffusion imaging (HARDI) data processing methods to obtain accurate and convincing results of complex fiber bundles with crossing, fanning and branching configurations. First, we develop a new deconvolution \emph{sharpening} transformation from diffusion ODF (dODF) to fiber ODF (fODF). We show that this sharpening transformation improves angular resolution and fiber detection of QBI and thus greatly improves tractography results. The angular resolution of QBI is in fact improved by approximately 20$^\circ$ and the fODF is shown to behave very similarly of the fiber orientation density (FOD) estimated from the spherical deconvolution method of Tournier et al. Another major contribution of the paper is the extensive comparison study on human brain datasets of our new deterministic and probabilistic tracking algorithms. As an application, we show how the reconstruction of transcallosal fiber connections intersecting with the corona radiata and the superior longitudinal fasciculus can be improved with the fODF in a group of 8 subjects. Current DTI based methods neglect these fibers, which might lead to wrong interpretations of the brain functions.