Miracle-dz/UniFAR-FTUs huggingface dataset:
A Noise Reduced Hard Thresholding method for Noisy Sparse Reconstruction

The problem of noisy sparse signal recovery has been studied in this paper. The generalized expectation maximization (GEM) algorithm provides solutions over state of the art convex relaxation methods for sparse signal recovery by considering the noisy measurements as missing data. However, in the case of images, artifacts in the reconstructed signal renders it to have low quality, especially based on qualitative performance metrics and so are unsuitable for further application specific processing. The problem may be reduced significantly by minimizing the total variation in the signal considering the constraints created by the statistics of noise in the reconstructed image. A modified iterative GEM for low noise reconstruction is presented and analyzed. Even though time consuming, simulations and numerical analysis show significantly higher performance metrics, especially PSNR and Structural Similarity, which imply reconstructed images may be suitable for further processing.
The funnel pre‐compensator

We introduce the funnel pre‐compensator as a novel and simple adaptive pre‐compensator of “high‐gain type”. We show that this pre‐compensator is feasible for a large class of signal pairs, which satisfy a certain relationship. We show that the funnel pre‐compensator guarantees prescribed transient behavior of the compensator error, it is of low complexity and inherently robust since its design is model‐free. As an application in adaptive control of nonlinear systems, a cascade of funnel pre‐compensators is exploited to obtain an artificial output with explicitly known derivatives, which tracks the system output with prescribed transient behavior. In some important cases the interconnection of the system with the pre‐compensator cascade is shown to have input‐to‐state stable internal dynamics. This guarantees feasibility of a novel funnel controller that consists of a funnel pre‐compensator cascade in conjunction with a recently developed funnel controller for systems with arbitrary relative degree. We illustrate the application of this interconnection for some mechanical systems.
Enhanced reference-plane-based dual-frequency absolute phase retrieval for depth measurement

Digital fringe projection is one of the most widely adopted optical non-contact methods used for high-precision depth measurement in industry. Nevertheless, new approaches with less measurement complexity and higher accuracy are still emerging in the research field. In this paper, we first analyze the efficiency of the reference-plane-based dual-frequency (RDF) pixel-by-pixel absolute phase retrieval method. The RDF method is able to provide a high-precision phase measurement, but the constraint of measurement depth range limits its application. We therefore propose an enhanced reference-plane-based dual-frequency (ERDF) pixel-by-pixel absolute phase retrieval method. The proposed ERDF method overcomes the constraint of measurement depth range and preserves the merits of the RDF method. In the ERDF method, the lower-frequency phase map is completely unwrapped with reference-plane-based single-frequency method, and the higher-frequency phase map is completely unwrapped using the lower-frequency unwrapped phase. The ERDF method is suitable for measuring objects with step-height surface discontinuities. Experiments have been conducted to demonstrate the advantages of the proposed ERDF method as compared to the RDF method.
A shape optimization approach for simulating contact of elastic membranes with rigid obstacles

The obstacle problem consists in computing equilibrium shapes of elastic membranes in contact with rigid obstacles. In addition to the displacement u of the membrane, the interface Γ on the membrane demarcating the region in contact with the obstacle is also an unknown and plays the role of a free boundary. Numerical methods that simulate obstacle problems as variational inequalities share the unifying feature of first computing membrane displacements and then deducing the location of the free boundary a posteriori. We present a shape optimization‐based approach here that inverts this paradigm by considering the free boundary to be the primary unknown and compute it as the minimizer of a certain shape functional using a gradient descent algorithm. In a nutshell, we compute Γ then u, and not u then Γ. Our approach proffers clear algorithmic advantages. Unilateral contact constraints on displacements, which render traditional approaches into expensive quadratic programs, appear only as Dirichlet boundary conditions along the free boundary. Displacements of the membrane need to be approximated only over the noncoincidence set, thereby rendering smaller discrete problems to be resolved. The issue of suboptimal convergence of finite element solutions stemming from the reduced regularity of displacements across the free boundary is naturally circumvented. Most importantly perhaps, our numerical experiments reveal that the free boundary can be approximated to within distances that are two orders of magnitude smaller than the mesh size used for spatial discretization. The success of the proposed algorithm relies on a confluence of factors‐ choosing a suitable shape functional, representing free boundary iterates with smooth implicit functions, an ansatz for the velocity of the free boundary that helps realize a gradient descent scheme and triangulating evolving domains with universal meshes. We discuss these aspects in detail and present numerous examples examining the performance of the algorithm.
Causes and Explanations: A Structural-Model Approach: Part 1: Causes

We propose a new definition of actual causes, using structural equations to model counterfactuals.We show that the definitions yield a plausible and elegant account ofcausation that handles well examples which have caused problems forother definitions and resolves major difficulties in the traditionalaccount. In a companion paper, we show how the definition of causality can beused to give an elegant definition of (causal) explanation.
Optimal Learning

This paper studies the problem of learning an unknown function $f$ from given data about $f$. The learning problem is to give an approximation $\hat f$ to $f$ that predicts the values of $f$ away from the data. There are numerous settings for this learning problem depending on (i) what additional information we have about $f$ (known as a model class assumption), (ii) how we measure the accuracy of how well $\hat f$ predicts $f$, (iii) what is known about the data and data sites, (iv) whether the data observations are polluted by noise. A mathematical description of the optimal performance possible (the smallest possible error of recovery) is known in the presence of a model class assumption. Under standard model class assumptions, it is shown in this paper that a near optimal $\hat f$ can be found by solving a certain discrete over-parameterized optimization problem with a penalty term. Here, near optimal means that the error is bounded by a fixed constant times the optimal error. This explains the advantage of over-parameterization which is commonly used in modern machine learning. The main results of this paper prove that over-parameterized learning with an appropriate loss function gives a near optimal approximation $\hat f$ of the function $f$ from which the data is collected. Quantitative bounds are given for how much over-parameterization needs to be employed and how the penalization needs to be scaled in order to guarantee a near optimal recovery of $f$. An extension of these results to the case where the data is polluted by additive deterministic noise is also given.
Unequal Uncertainties and Uncertain Inequalities : An Axiomatic Approach 1

In this paper, we provide an axiomatic characterization of social welfare functions for uncertain incomes. Our most general result is that a small number of reasonable assumptions regarding welfare orderings under uncertainty rule out pure ex ante as well as pure ex post evaluations. Any social welfare function that satisfies these axioms should lie strictly between the ex ante and the ex post evaluations of income distributions. We also provide an axiomatic characterization of the weighted average of the minimum and the maximum of ex post and the ex ante evaluations.
OIL PRICE

Since the advent of oil production and export in late 1999, Sudan economy became more reliant on oil exports proceeds. This situation has exposed the economy to the negative effect of oil price fluctuations. In general, oil exporting countries exhibit positive impact on their economy to oil price increase, while oil importing economies suffer. Unlike developing economies, there is a paucity of research in developing countries with regards to the relationship between the macro-economy and oil price shocks. In this regard, Sudan has been neglected from serious studies related to oil price shocks. This research attempts to contribute towards filling this gap. In doing so, Vector Auto-Regression model is employed to investigate the impact of oil price shocks on the real GDP growth and unemployment rates over the period 2000 2014. The Granger causality test suggests that unemployment has statistically and significantly influenced real GDP growth. Results from the Impulse Response Functions and Forecast Error Variance Decomposition analysis suggest that increase in real oil price has greater influence on GDP growth. Interestingly, real oil price decrease has a significant positive impact on unemployment rate.
Robust Transceiver Design for MISO Interference Channel With Energy Harvesting

In this paper, we consider the multiuser multiple-input single-output (MISO) interference channel where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. The transmit beamforming vectors and receive power splitting (PS) ratios are jointly designed in order to minimize the total transmission power subject to both signal-to-interference-plus-noise ratio (SINR) and EH constraints. Most joint beamforming and power splitting (JBPS) designs assume that perfect channel state information (CSI) is available; however CSI errors are inevitable in practice. To overcome this limitation, we study the robust JBPS design problem assuming a norm-bounded error (NBE) model for the CSI. Three different solution approaches are proposed for the robust JBPS problem, each one leading to a different computational algorithm. Firstly, an efficient semidefinite relaxation (SDR)-based approach is presented to solve the highly non-convex JBPS problem, where the latter can be formulated as a semidefinite programming (SDP) problem. A rank-one recovery method is provided to recover a robust feasible solution to the original problem. Secondly, based on second order cone programming (SOCP) relaxation, we propose a low complexity approach with the aid of a closed-form robust solution recovery method. Thirdly, a new iterative method is also provided which can achieve near-optimal performance when the SDR-based algorithm results in a higher-rank solution. We prove that this iterative algorithm monotonically converges to a Karush-Kuhn-Tucker (KKT) solution of the robust JBPS problem. Finally, simulation results are presented to validate the robustness and efficiency of the proposed algorithms.
From proof theory to theories theory

When constructing a proof, we use rules that are specific to the objects the proof speaks about and others that are ontologically neutral. We call the rules of the first kind theoretical rules and those of the second logical rules. The position of the border delimiting the logic from the theories is not always clear and, for instance, it can be discussed if the notion of set belongs to the realm of logic or to that of a specific theory. It seems that the dominant point of view at the end of the XIXth and at the beginning of the XXth century — Frege, Whitehead and Russell, . — was that this notion of set, or of concept, was logical. But Russell’s paradox seems to have finally ruined this point of view: as we must give up full comprehension, and either restrict comprehension to specific propositions, as in Zermelo set theory, or add a type system as in the Principia Mathematica, there are too many ways to do so — with or without the replacement axiom, with or without transfinite types — hence the notion of set is theoretical. We may speculate that, even if full comprehension had not been contradictory, this idea that the notion of set is ontologically neutral would have been finally given up, as even with full comprehension, there still would have been choices to be made for the set existence axioms: first because comprehension depends on the choice of
High-Order FDTD with Exponential Time Differencing Algorithm for Modeling Wave Propagation in Debye Dispersive Materials

A high-order (HO) finite-difference time-domain (FDTD) method with exponential time differencing (ETD) algorithm is proposed to model electromagnetic wave propagation in Debye dispersive material in this paper. The proposed method introduces an auxiliary difference equation (ADE) technique which establishes the relationship between the electric displacement vector and electric field intensity with a differential equation in Debye dispersive media. The ETD algorithm is applied to the displacement vector and auxiliary difference variable in time domain, and the fourth-order centraldifference discretization is used in space domain. One example with plane wave propagation in a Debye dispersive media is calculated. Compared with the conventional ETD-FDTD method, the results from our proposed method show its accuracy and efficiency for Debye dispersive media simulation.
X Y V 1 ReLU W 1 Embedding Space L 2 Normalization L 2 Normalization W 2 V 2 ReLU

This paper proposes a method for learning joint embeddings of images and text using a two-branch neural network with multiple layers of linear projections followed by nonlinearities. The network is trained using a novel largemargin objective that combines cross-view ranking constraints with within-view neighborhood structure preservation constraints inspired by metric learning literature. Extensive experiments show that both contributions of our method, the nonlinear network structure and the structurepreserving objective function, achieve significant improvements in accuracy for image-to-text and text-to-image retrieval. Our method achieves new state-of-the-art results on the Flickr30K and MSCOCO image-sentence datasets and shows promise on the new task of phrase localization on the Flickr30K Entities dataset.
The effect of antagonizing rgD-binding integrin activity in papillary thyroid cancer cell lines

: Patients with papillary thyroid cancer (PTC) generally have good prognosis, but inoperable and radioactive iodine–refractory PTC still poses significant clinical challenges due to lack of effective treatment and higher mortality rates. Given the important role of integrins in multiple steps of tumor development, integrin-targeting therapy could be an effective strategy for PTC therapy. In this study, we investigated the antitumor effect of antagonizing Arg-Gly-Asp (RGD)-binding integrin activity in several PTC cell lines. Two RGD-binding integrin heterodimers α v β 3 and α v β 5 were first determined with fluorescence-activated cell sorting (FACS) and immunofluorescence assay. Cell proliferation and apoptosis were examined by Cell Counting Kit-8 assay and FACS, respectively. Cell migration and invasion were determined by transwell assays. All three PTC cell lines examined (BCPAP, K1, and TPC1) showed a moderate-to-high expression of α v β 3 and α v β 5 ( P , 0.05). Antagonizing the two heterodimers with the RGD-containing antagonist showed moderate inhibitory effect on cell viability of K1 and BCPAP cells, while the inhibitory effect was more significant in TPC1 cells. Similarly, the apoptotic effect induced by antagonizing α v β 3 and α v β 5 was much stronger in TPC1 cells than in BCPAP and K1 cells. Cell migration and invasion were significantly inhibited by α v β 3 and α v β 5 antagonism in all three PTC cell lines. Our results suggested that the demonstrated expression of RGD-binding integrin on PTC cells provides the possibility of integrin-targeting treatment in PTC. The strong apoptotic effect observed in TPC1 cells indicated that a subgroup of PTC patients may benefit from the cytotoxic effect of RGD-binding integrin antagonism, while the strong inhibitory effect on migration and invasion in all three PTC cells by antagonizing α v β 3 and α v β 5 showed there is an exciting possibility that targeting RGD-binding integrin may serve a potential therapeutic approach for metastatic PTC patients.
On the Use and Abuse of Spatial Instruments

Instruments based on realizations of the endogenous variable in other units—for instance, regional or global weighted averages—are commonly used in political science. Such spatial instruments have proved attractive: they are convenient to obtain, typically have power, and are plausibly exogenous. We argue that the assumptions underlying spatial instruments remain poorly understood and challenge whether spatial instruments can satisfy the conditions required for valid instruments. First, when cross-unit dependence exists in the endogenous predictor, other cross-unit relationships—spillovers and interdependence—likely exist as well and risk violations of the exclusion restriction. Second, spatial instruments produce simultaneity in the first-stage equation, as left-hand side outcomes are included as right-hand side predictors. Because the instrument and the endogenous variable are simultaneously determined, the exclusion restriction is, necessarily and by construction, violated. Taken together, these concerns lead us to conclude that spatial instruments are rarely, if ever, valid.
Semi-empirical functions describing the response of short-lived radicals to their driving forces in the WRF/Chem Model

Short-lived radicals play a key role in the chemistry of the lower atmosphere. They oxidize pollutants, e.g., carbon monoxide (CO) and methane (CH4), making them more soluble and therefore easier to remove by wet or dry deposition. Without these radicals, tropospheric pollution would accumulate to much higher levels and have a stronger negative effect on our climate and health. To understand short-lived radicals, i.e., hydroxyl radical (OH), we evaluated them against the environmental and chemical driving factors that, theoretically, should control them. We use a semi-empirical function (SEF) that is linear in log space, whose slopes represent the “normalized sensitivity coefficients”. These coefficients describe how sensitive a radical is with respect to changes in the individual driver, e.g., atmospheric water vapor (H2O), volatile organic compounds (VOCs), nitrogen oxides (NOx), and the ultraviolet photolysis coefficient for NO2 (J(NO2)),. Using the output from the WRF/Chem model and the sensitivity coefficients calculated from the NCAR Master Mechanism box model, we evaluated the chemical concentrations with respect to the SEF and found a linear relation with a reduction in scatter compared to the correlations with each individual driver. However, multiple correlation lines were identified, and their origins were traced to WRF/Chem‟s use of different chemical mechanisms over land, ocean, and near the domain boundaries. Therefore, these correlations may provide a powerful technique for comparing models with one another and with measurements, specifically with field campaign observations (like those of MIRAGE) for evaluating the representation of fast radical photochemistry in three-dimensional chemistrytransport models (such as WRF/Chem).
Trc150094 attenuates progression of nontraditional cardiovascular risk factors associated with obesity and type 2 diabetes in obese ZSF1 rats

: Chronic overnutrition and consequential visceral obesity is associated with a cluster of risk factors for cardiovascular disease and type 2 diabetes mellitus. Moreover, individuals who have a triad of hypertension, dysglycemia, and elevated triglycerides along with reduced high-density lipoprotein cholesterol have a greater residual cardiovascular risk even after factoring for the traditional risk factors such as age, smoking, diabetes, and elevated low-density lipoprotein cholesterol. In our previous study we demonstrated that TRC150094, when administered to rats receiving a high-fat diet, stimulated mitochondrial fatty acid oxidation (FAO) and reduced visceral adiposity, opening an interesting perspective for a possible clinical application. In the present study, oral administration of TRC150094 to obese Zucker spontaneously hypertensive fatty rats (obese ZSF1) improved glucose tolerance and glycemic profile as well as attenuated a rise in blood pressure. Obese ZSF1 rats treated with TRC150094 also showed reduced hepatic steatosis, reduced progression of nephropathy, and improved skeletal muscle function. At the cellular level, TRC150094 induced a significant increase in mitochondrial respiration as well as an increased FAO in liver and skeletal muscle, ultimately resulting in reduced hepatic as well as total body fat accumulation, as evaluated by magnetic resonance spectroscopy and magnetic resonance imaging, respectively. If reproduced in humans, these results could confirm that TRC150094 may represent an attractive therapeutic agent to counteract multiple residual cardiovascular risk components.
A Discrete Variational Derivation of Accelerated Methods in Optimization

Many of the new developments in machine learning are connected with gradient-based optimization methods. Recently, these methods have been studied using a variational perspective. This has opened up the possibility of introducing variational and symplectic integration methods using geometric integrators. In particular, in this paper, we introduce variational integrators which allow us to derive different methods for optimization. Using both, Hamilton's principle and Lagrange-d'Alembert's, we derive two families of optimization methods in one-to-one correspondence that generalize Polyak's heavy ball and the well known Nesterov accelerated gradient method, mimicking the behavior of the latter which reduces the oscillations of typical momentum methods. However, since the systems considered are explicitly time-dependent, the preservation of symplecticity of autonomous systems occurs here solely on the fibers. Several experiments exemplify the result.
Mathematical Analysis and an Exact Solution Combined with Preprocessing Method for Resynchronizing of Bus Timetable Problem

Bus timetabling is a subproblem of bus network planning, and it determines departure time of each trip of lines to make vehicles from different lines synchronously arrive at transfer stations. Due to the well-designed coordination of bus timetables, passengers can make a smooth transfer without waiting a long time for connecting buses. This paper addresses the planning level of resynchronizing of bus timetable problem allowing modifications to initial timetable. Timetable modifications consist of shifts in the departure times and headways. A single-objective mixed-integer programming model is proposed for this problem to maximize the number of total transferring passengers benefiting from smooth transfers. We analyze the mathematical properties of this model, and then a preprocessing method is designed to reduce the solution space of the proposed model. The numerical results show that the reduced model is effectively solved by branch and bound algorithm, and the preprocessing method has the potential to be applied for large-scale bus networks.