Miracle-dz/UniFAR-FTUs huggingface dataset:
Multivariate Trend Filtering for Lattice Data

We study a multivariate version of trend filtering, called Kronecker trend filtering or KTF, for the case in which the design points form a lattice in $d$ dimensions. KTF is a natural extension of univariate trend filtering (Steidl et al., 2006; Kim et al., 2009; Tibshirani, 2014), and is defined by minimizing a penalized least squares problem whose penalty term sums the absolute (higher-order) differences of the parameter to be estimated along each of the coordinate directions. The corresponding penalty operator can be written in terms of Kronecker products of univariate trend filtering penalty operators, hence the name Kronecker trend filtering. Equivalently, one can view KTF in terms of an $\ell_1$-penalized basis regression problem where the basis functions are tensor products of falling factorial functions, a piecewise polynomial (discrete spline) basis that underlies univariate trend filtering. This paper is a unification and extension of the results in Sadhanala et al. (2016, 2017). We develop a complete set of theoretical results that describe the behavior of $k^{\mathrm{th}}$ order Kronecker trend filtering in $d$ dimensions, for every $k \geq 0$ and $d \geq 1$. This reveals a number of interesting phenomena, including the dominance of KTF over linear smoothers in estimating heterogeneously smooth functions, and a phase transition at $d=2(k+1)$, a boundary past which (on the high dimension-to-smoothness side) linear smoothers fail to be consistent entirely. We also leverage recent results on discrete splines from Tibshirani (2020), in particular, discrete spline interpolation results that enable us to extend the KTF estimate to any off-lattice location in constant-time (independent of the size of the lattice $n$).
Logarithmic Voronoi polytopes for discrete linear models

We study logarithmic Voronoi cells for linear statistical models and partial linear models. The logarithmic Voronoi cells at points on such model are polytopes. To any $d$-dimensional linear model inside the probability simplex $\Delta_{n-1}$, we can associate an $n\times d$ matrix $B$. For interior points, we describe the vertices of these polytopes in terms of co-circuits of $B$. We also show that these polytopes are combinatorially isomorphic to the dual of a vector configuration with Gale diagram $B$. This means that logarithmic Voronoi cells at all interior points on a linear model have the same combinatorial type. We also describe logarithmic Voronoi cells at points on the boundary of the simplex. Finally, we study logarithmic Voronoi cells of partial linear models, where the points on the boundary of the model are especially of interest.
Fermionic integrable models and graded Borchers triples

We provide an operator-algebraic construction of integrable models of quantum field theory on 1+1 dimensional Minkowski space with fermionic scattering states. These are obtained by a grading of the wedge-local fields or, alternatively, of the underlying Borchers triple defining the theory. This leads to a net of graded-local field algebras, of which the even part can be considered observable, although it is lacking Haag duality. Importantly, the nuclearity condition implying nontriviality of the local field algebras is independent of the grading, so that existing results on this technical question can be utilized. Application of Haag-Ruelle scattering theory confirms that the asymptotic particles are indeed fermionic. We also discuss connections with the form factor programme.
A Survey on Gender Bias in Natural Language Processing

Language can be used as a means of reproducing and enforcing harmful stereotypes and biases and has been analysed as such in numerous research. In this paper, we present a survey of 304 papers on gender bias in natural language processing. We analyse definitions of gender and its categories within social sciences and connect them to formal definitions of gender bias in NLP research. We survey lexica and datasets applied in research on gender bias and then compare and contrast approaches to detecting and mitigating gender bias. We find that research on gender bias suffers from four core limitations. 1) Most research treats gender as a binary variable neglecting its fluidity and continuity. 2) Most of the work has been conducted in monolingual setups for English or other high-resource languages. 3) Despite a myriad of papers on gender bias in NLP methods, we find that most of the newly developed algorithms do not test their models for bias and disregard possible ethical considerations of their work. 4) Finally, methodologies developed in this line of research are fundamentally flawed covering very limited definitions of gender bias and lacking evaluation baselines and pipelines. We suggest recommendations towards overcoming these limitations as a guide for future research.
Crystalline equivalent boundary-bulk correspondence of two-dimensional topological phases

The boundary of topological phases of matter can manifest its topology nature, which leads to the so-called boundary-bulk correspondence (BBC) of topological phases. In this Letter, we construct a one-to-one correspondence between the boundary theories of fermionic SPT (fSPT) phases protected by crystalline symmetry and on-site symmetry in 2D fermionic systems, which follow the so-called crystalline equivalence principle. We dub such correspondence crystalline equivalent BBC. We illustrate this correspondence by two simple examples and as an application, we discover the topological boundary theory of 2D fSPT phase with spin-1/2 fermions, protected by a non-Abelian group Z4 ⋊ Z T 2 with A 4 = T 2 = Pf where A is the generator of Z4, from its crystalline equivalent partner—2D higher-order fSPT phase with spinless fermions, protected by D4 symmetry.
Can Social Ontological Knowledge Representations be Measured Using Machine Learning?

Personal Social Ontology (PSO), it is proposed, is how an individual perceives the ontological properties of terms. For example, an absolute fatalist would arguably use terms that remove any form of agency from a person. Such fatalism has the impact of ontologically defining acts such as ‘winning’, ‘victory’ and ‘success’, for example, in a manner that is contrary to how a non-fatalist would ontologically define them. While both a fatalist and non-fatalist would agree on the dictionary definition of these terms, they would differ on what and how they can be caused. This difference between the two individuals, it is argued, can be induced from the co-occurrence of terms used by each individual. That such co-occurrence carries an implied social ontology, one that is specific to that person. The use of principal social perceptions -as evidenced by the social psychology and social neuroscience literature, is put forward as a viable method to feature engineer such texts. With the natural language characterisation of these features, they are then usable in machine learning pipelines.
Reducing Minor Page Fault Overheads through Enhanced Page Walker

Application virtual memory footprints are growing rapidly in all systems from servers down to smartphones. To address this growing demand, system integrators are incorporating ever larger amounts of main memory, warranting rethinking of memory management. In current systems, applications produce page fault exceptions whenever they access virtual memory regions that are not backed by a physical page. As application memory footprints grow, they induce more and more minor page faults. Handling of each minor page fault can take a few thousands of CPU cycles and blocks the application till the OS kernel finds a free physical frame. These page faults can be detrimental to the performance when their frequency of occurrence is high and spread across application runtime. Specifically, lazy allocation-induced minor page faults are increasingly impacting application performance. Our evaluation of several workloads indicates an overhead due to minor page faults as high as 29% of execution time. In this article, we propose to mitigate this problem through a hardware, software co-design approach. Specifically, we first propose to parallelize portions of the kernel page allocation to run ahead of fault time in a separate thread. Then we propose the Minor Fault Offload Engine (MFOE), a per-core hardware accelerator for minor fault handling. MFOE is equipped with a pre-allocated page frame table that it uses to service a page fault. On a page fault, MFOE quickly picks a pre-allocated page frame from this table, makes an entry for it in the TLB, and updates the page table entry to satisfy the page fault. The pre-allocation frame tables are periodically refreshed by a background kernel thread, which also updates the data structures in the kernel to account for the handled page faults. We evaluate this system in the gem5 architectural simulator with a modified Linux kernel running on top of simulated hardware containing the MFOE accelerator. Our results show that MFOE improves the average critical path fault handling latency by 33× and tail critical path latency by 51×. Among the evaluated applications, we observed an improvement of runtime by an average of 6.6%.
On Uniform Ensemble Controllability of Diagonalizable Linear Ensemble Systems

In this paper, we study uniform ensemble controllability (UEC) of linear ensemble systems defined in an infinite-dimensional space through finite-dimensional settings. Specifically, with the help of the Stone-Weierstrass theorem for modules, we provide an algebraic framework for examining UEC of linear ensemble systems with diagonalizable drift vector fields through checking the controllability of finite-dimensional subsystems in the ensemble. The new framework renders a novel concept of ensemble controllability matrix, which rank-condition serves as a sufficient and necessary condition for UEC of linear ensembles. We provide several examples demonstrating that the proposed approach well-encompasses existing results and analyzes UEC of linear ensembles not addressed by literature.
A novel control to actively damp resonance in input LC filter of a three phase voltage source converter

The voltage source power converter (VSC) provides constant/controllable DC bus voltage, regenerating capability, controllable power factor and nearly sinusoidal input current. If an input LC filter is added, it prevents pollution of the utility with current ripple due to the PWM switching. A novel control strategy for actively damping high frequency resonances (above 1.5 kHz) in the input LC filter is presented. No additional sensors to the standard VSC are required. Theoretical results of the analysis and simulation are experimentally verified.
Iterative decision feedback equalisation of linearly precoded OFDM systems

An iterative extension of the decision feedback equaliser (DFE) receiver for linearly precoded OFDM is proposed, which improves the performance significantly, but requires a Cholesky factorisation for each iteration every time the channel changes. Next, a lower complexity form for Hadamard precoded systems is proposed, which requires just one Cholesky factorisation per OFDM symbol, with no loss in performance.
Invasive Right Ventricular Pressure-Volume Analysis: Basic Principles, Clinical Applications, and Practical Recommendations

Supplemental Digital Content is available in the text. Right ventricular pressure-volume (PV) analysis characterizes ventricular systolic and diastolic properties independent of loading conditions like volume status and afterload. While long-considered the gold-standard method for quantifying myocardial chamber performance, it was traditionally only performed in highly specialized research settings. With recent advances in catheter technology and more sophisticated approaches to analyze PV data, it is now more commonly used in a variety of clinical and research settings. Herein, we review the basic techniques for PV loop measurement, analysis, and interpretation with the aim of providing readers with a deeper understanding of the strengths and limitations of PV analysis. In the second half of the review, we detail key scenarios in which right ventricular PV analysis has influenced our understanding of clinically relevant topics and where the technique can be applied to resolve additional areas of uncertainty. All told, PV analysis has an important role in advancing our understanding of right ventricular physiology and its contribution to cardiovascular function in health and disease.
Current distribution on a receiving dipole antenna

The current distribution on a receiving dipole antenna is dependent on its feed-point load impedance. Calculations based on the modified King-Middleton approximation have been made of the distribution for antenna half lengths of 0.1, 0.2, 0.25, and 0.3 \lambda . Representative values of the antenna load impedance, resistive, capacitive, and inductive, are used to show their effects on the antenna current distribution.
A new technique for extracting partial discharge signals in on-line monitoring with wavelet analysis

This paper deals with the multiresolution analysis of wavelet transform for on-line partial discharge (PD) monitoring. One of the problems of PD on-line monitoring is how to suppress strong noises such as narrow-band radio frequency noise and random noise. In recent years, wavelet transform has become a powerful tool to analyze and process signals in various science and technology fields. In this paper, Mallat's algorithm of dyadic wavelet transform is adopted for the research of the characteristics of PD signals and noises, and a new denoising technique with wavelet analysis is proposed. Traditionally, noise is filtered by using low-pass, bandpass or high-pass filter. While the new denoising technique carries out filtering on the base of the different singularity of PD signals and noises. The numerical simulating signals and some experimental data obtained in a power plant were processed by using the new technique. The results show that the new technique not only can improve the Signal to Noise Ratio (SNR), but also possesses high time resolution of PD signals. Finally some factors influencing the efficiency of filtering are discussed in detail.
Calcium Modification Therapies in Contemporary Percutaneous Coronary Intervention

Coronary Artery Calcification (CAC) has been known to be associated with worse Percutaneous Coronary Intervention (PCI) short- and long-term outcomes. Nowadays, with the increased prevalence of the risk factors leading to CAC in the population and also more PCI procedures done in older patients and with the growing number of higher-risk cases of Chronic Total Occlusion (CTO) PCI and PCI after Coronary Artery Bypass Grafting (CABG), severe cases of CAC are now encountered on a daily basis in the catheterization lab and remain a big challenge to the interventional community, making it crucial to identify cases of severe CAC and plan a CAC PCI modification strategy upfront. Improved CAC detection with intravascular imaging helped identify more of these severe CAC cases and predict response to therapy and stent expansion based on CAC distribution in the vessel. Multiple available therapies for CAC modification have evolved over the years. Familiarity with the specifics and special considerations and limitations of each of these tools are essential in the choice and application of these therapies when used in severe CAC treatment. In this review, we discuss CAC pathophysiology, modes of detection, and different available therapies for CAC modification.
A quinoline alkaloid potentially modulates the amyloidogenic structural transitions of the biofilm scaffolding small basic protein

Abstract Bacterial biofilm formation by communities of opportunistic bacterial pathogens like Staphylococcus epidermidis is regarded as the primary virulence mechanism facilitating the spread of detrimental nosocomial and implant-associated infections. An 18-kDa small basic protein (Sbp) and its amyloid fibrils account for strengthening the biofilm architecture and scaffolding the S. epidermidis biofilm matrix. Our study reports systematic analysis of the amyloidogenic structural transitions of Sbp and predicts the amyloid core of the protein which may trigger misfolding and aggregation. Herein, we report the novel amyloid inhibitory potential of Camptothecin, a quinoline alkaloid which binds stably to Sbp monomers and redirects the formation of unstructured regions further destabilizing the protein. Molecular dynamics simulations reveal that Camptothecin averts β-sheet transitions, interrupts with electrostatic interactions and disrupts the intermolecular hydrophobic associations between the exposed hydrophobic amyloidogenic regions of Sbp. Collectively, our study puts forward the first report detailing the heteromolecular associations and amyloid modulatory effects of Camptothecin which may serve as a structural scaffold for the tailored designing of novel drugs targeting the S. epidermidis biofilm matrix. Communicated by Ramaswamy H. Sarma
Asbestos‐induced mesothelial injury and carcinogenesis: Involvement of iron and reactive oxygen species

Asbestos fibers have been used as an industrial and construction material worldwide due to their high durability and low production cost. Commercial usage of asbestos is currently prohibited in Japan; however, the risk of asbestos‐induced malignant mesothelioma (MM) remains. According to epidemiological data, the onset of MM is estimated to occur after a latent period of 30–40 years from initial exposure to asbestos fibers; thus, the continuous increase in MM is a concern. To explore the molecular mechanisms of MM using animal models, iron saccharate with iron chelator‐induced sarcomatoid mesothelioma (SM) revealed hallmarks of homozygous deletion of Cdkn2a/2b by aCGH and microRNA‐199/214 by expression microarray. Oral treatment of iron chelation by deferasirox decreased the rate of high‐grade SM. Moreover, phlebotomy delayed MM development in crocidolite‐induced MM in rats. In Divalent metal transporter 1 (Dmt1) transgenic mice, MM development was delayed because of low reactive oxygen species (ROS) production. These results indicate the importance of iron and ROS in mesothelial carcinogenesis. The aims of this review focus on the pathogenesis of elongated mineral particles (EMPs), including asbestos fibers and multiwalled carbon nanotubes (MWCNTs) that share similar rod‐like shapes in addition to the molecular mechanisms of MM development.
Representing plant diversity in land models: An evolutionary approach to make “Functional Types” more functional

Plants are critical mediators of terrestrial mass and energy fluxes, and their structural and functional traits have profound impacts on local and global climate, biogeochemistry, biodiversity, and hydrology. Yet, Earth System Models (ESMs), our most powerful tools for predicting the effects of humans on the coupled biosphere–atmosphere system, simplify the incredible diversity of land plants into a handful of coarse categories of “Plant Functional Types” (PFTs) that often fail to capture ecological dynamics such as biome distributions. The inclusion of more realistic functional diversity is a recognized goal for ESMs, yet there is currently no consistent, widely accepted way to add diversity to models, that is, to determine what new PFTs to add and with what data to constrain their parameters. We review approaches to representing plant diversity in ESMs and draw on recent ecological and evolutionary findings to present an evolution‐based functional type approach for further disaggregating functional diversity. Specifically, the prevalence of niche conservatism, or the tendency of closely related taxa to retain similar ecological and functional attributes through evolutionary time, reveals that evolutionary relatedness is a powerful framework for summarizing functional similarities and differences among plant types. We advocate that Plant Functional Types based on dominant evolutionary lineages (“Lineage Functional Types”) will provide an ecologically defensible, tractable, and scalable framework for representing plant diversity in next‐generation ESMs, with the potential to improve parameterization, process representation, and model benchmarking. We highlight how the importance of evolutionary history for plant function can unify the work of disparate fields to improve predictive modeling of the Earth system.
Automatic Statistic of Rock Mass Discontinuity Attitude Elements Based on K-Mean Clustering Analysis

The rock attitude elements play important role in the fields of both mechanical properties and stability analysis. The traditional statistical graphs such as rose diagram, isopycnic graph and angle histogram are displayed which can roughly estimate the dip direction and angle of rock mass. And k-mean clustering analysis is applied to rock mass discontinuity attitude automatic statistic which can determine the the dip direction of rock mass accurately and estimate the proportion of gathered data. The K-mean clustering analysis is a beneficial supplement of traditional statistical methods, which has a prospect of engineering application.
Cognitive functioning and cannabis use in first-episode psychosis

Abstract Aim Cannabis use is common in people with psychotic disorders. However, the effect of cannabis on cognition in psychosis remains unclear. Our study investigates relationships between the history of cannabis use and cognitive performance in patients with first-episode psychosis (FEP) during a one-year follow-up. Methods The present study included FEP (N = 91) and control (N = 61) groups. Cannabis use was evaluated with a self-report questionnaire, clinical assessment, and medical records during a lifetime and 12 months prior to the treatment onset (recent). Symptoms of psychosis and anxiety were evaluated on the brief psychiatric rating scale. Negative symptoms were assessed using the scale for the assessment of negative symptoms. Cognitive tests were used to evaluate neurocognition (summarized in the g factor) and social cognition. Crude regression analyses for the g factor included variables of cannabis use as independent variables. Full regression models were controlled for gender, education, and clinical symptoms. Results In the FEP group, men used cannabis more frequently than women. In the crude regression model for FEP patients, never having used cannabis was associated with a better neurocognitive profile at 12 months. In the full model, more severe anxiety symptoms were associated with better neurocognition at two months, and less severe negative symptoms were associated with better neurocognition at 12 months. Cannabis use was not associated with social cognition. No associations between cognitive performance and cannabis use emerged in the controls. Conclusion Negative and affective symptom severity in FEP was associated with cognitive performance to a greater degree than a lifetime history of cannabis use.