Miracle-dz/UniFAR-FTUs huggingface dataset:
High Dimensional Classification through $\ell_0$-Penalized Empirical Risk Minimization

We consider a high dimensional binary classification problem and construct a classification procedure by minimizing the empirical misclassification risk with a penalty on the number of selected features. We derive non-asymptotic probability bounds on the estimated sparsity as well as on the excess misclassification risk. In particular, we show that our method yields a sparse solution whose l0-norm can be arbitrarily close to true sparsity with high probability and obtain the rates of convergence for the excess misclassification risk. The proposed procedure is implemented via the method of mixed integer linear programming. Its numerical performance is illustrated in Monte Carlo experiments.
Selling Wind

We investigate the strategic behavior of wind producers in the presence of uncertain wind resource availability, where wind availability is correlated across firms. We study how the level of correlation between different firms’ wind resources impacts strategy and market outcomes. The main insight of our analysis is that increasing heterogeneity in resource availability improves social welfare, as a function of its effects both on improving diversification and on reducing withholding by firms. We show that this insight is robust for common assumptions regarding electricity demand. The model is also used to analyze the effect of wind resource heterogeneity on firm profits and opportunities for collusion. Finally, we analyze the impacts of improving public information and weather forecasting; enhanced public forecasting increases welfare, but it is not always in the best interests of strategic producers.
Visual Scene Representations: Sufficiency, Minimality, Invariance and Deep Approximations

An ideal visual representation would be a function of visual data that is a minimal sufficient statistics for the scene, and a maximal invariant to nuisance variability. We derive analytical expressions for such representations and show that, under certain assumptions underlying the Lambert-Ambient model, they are related to convolutional architectures. This link highlights the conditions under which they can be expected to perform well, and also suggests ways to improve and generalize them. This new interpretation draws connections to the classical theories of sampling, hypothesis testing and group invariance. We show that one layer of a convolutional architecture can approximate an optimal representation of one im- age, given sufficiently many receptive fields. We also show that stacking multiple layers, each of which is invariant to a small group transformation such as affine, achieves invariance to larger groups, all the way to planar diffeomorphisms, given sufficiently many layers and receptive fields.
Monte Carlo Confidence Sets for Identified Sets

In complicated/nonlinear parametric models, it is generally hard to know whether the model parameters are point identified. We provide computationally attractive procedures to construct confidence sets (CSs) for identified sets of full parameters and of subvectors in models defined through a likelihood or a vector of moment equalities or inequalities. These CSs are based on level sets of optimal sample criterion functions (such as likelihood or optimally-weighted or continuously-updated GMM criterions). The level sets are constructed using cutoffs that are computed via Monte Carlo (MC) simulations directly from the quasi-posterior distributions of the criterions. We establish new Bernstein-von Mises (or Bayesian Wilks) type theorems for the quasi-posterior distributions of the quasi-likelihood ratio (QLR) and profile QLR in partially-identified regular models and some non-regular models. These results imply that our MC CSs have exact asymptotic frequentist coverage for identified sets of full parameters and of subvectors in partially-identified regular models, and have valid but potentially conservative coverage in models with reduced-form parameters on the boundary. Our MC CSs for identified sets of subvectors are shown to have exact asymptotic coverage in models with singularities. We also provide results on uniform validity of our CSs over classes of DGPs that include point and partially identified models. We demonstrate good finite-sample coverage properties of our procedures in two simulation experiments. Finally, our procedures are applied to two non-trivial empirical examples: an airline entry game and a model of trade flows.
Metrological advantage at finite temperature for Gaussian phase estimation

In the context of phase estimation with Gaussian states, we introduce a quantifiable definition of metrological advantage that takes into account thermal noise in the preparation procedure. For a broad set of states—isotropic nonpure Gaussian states—we show that squeezing is not only necessary but sufficient to achieve metrological advantage. We interpret our results in the framework of resource theory and discuss possible sources of advantage other than squeezing. Our work is a step towards using phase estimation with pure and mixed states to define and quantify nonclassicality. This work is complementary with studies that defines nonclassicality using quadrature displacement estimation.
Should We Adjust for the Test for Pre-trends in Difference-in-Difference Designs?

The common practice in difference-in-difference (DiD) designs is to check for parallel trends prior to treatment assignment, yet typical estimation and inference does not account for the fact that this test has occurred. I analyze the properties of the traditional DiD estimator conditional on having passed (i.e. not rejected) the test for parallel pre-trends. When the DiD design is valid and the test for pre-trends confirms it, the typical DiD estimator is unbiased, but traditional standard errors are overly conservative. Additionally, there exists an alternative unbiased estimator that is more efficient than the traditional DiD estimator under parallel trends. However, when in population there is a non-zero pre-trend but we fail to reject the hypothesis of parallel pre-trends, the DiD estimator is generally biased relative to the population DiD coefficient. Moreover, if the trend is monotone, then under reasonable assumptions the bias from conditioning exacerbates the bias relative to the true treatment effect. I propose new estimation and inference procedures that account for the test for parallel trends, and compare their performance to that of the traditional estimator in a Monte Carlo simulation.
Inference on Estimators Defined by Mathematical Programming

We propose an inference procedure for estimators defined by mathematical programming problems, focusing on the important special cases of linear programming (LP) and quadratic programming (QP). In these settings, the coefficients in both the objective function and the constraints of the mathematical programming problem may be estimated from data and hence involve sampling error. Our inference approach exploits the characterization of the solutions to these programming problems by complementarity conditions; by doing so, we can transform the problem of doing inference on the solution of a constrained optimization problem (a non-standard inference problem) into one involving inference based on a set of inequalities with pre-estimated coefficients, which is much better understood. We evaluate the performance of our procedure in several Monte Carlo simulations and an empirical application to the classic portfolio selection problem in finance.
Laminar to fully turbulent flow in a pipe: scalar patches, structural duality of turbulent spots and transitional overshoot

We present findings on scalar flashes, turbulent spots and transition statistics in a direct numerical simulation of a 500 radius-long pipe flow with a radial-mode inlet disturbance. Transitional spots are found to contain two different types of eddies. The wall region consists primarily of ‘reverse hairpin vortices’. This unusual structure is related to the high-speed streaks arising from the prescribed inlet perturbation. The core region is populated by the normally observed ‘forward’ hairpin vortices. Number density of the reverse hairpins is quantified indirectly and conservatively by measuring the number density of negative skin-friction patches. During the late stages of transition, second-order statistics such as Reynolds stresses and the rate of dissipation of turbulent kinetic energy exhibit substantial overshoot. This is associated with mid-to-high frequency content in the energy spectra that exceeds the corresponding levels in fully developed turbulence. Flow visualizations reveal bursts of small-scale vortex motions, including the reverse hairpins, that probably account for the enhanced mid-to-high frequency spectral content. A passive scalar is injected at the centreline of the inlet plane, mimicking laboratory injection of dye through a needle, to investigate the mysterious phenomenon of turbulent scalar patches residing in fully developed turbulent pipe flow. At several hundred radii downstream of transition where the velocity field is genuine fully developed turbulence, the scalar patches retain persistent memory of events far upstream. Comparing the present flow with a similar pipe flow disturbed by a significantly different inlet condition suggests that the foregoing observations are insensitive to the form of the disturbances.
Hierarchical Attention Generative Adversarial Networks for Cross-domain Sentiment Classification

Cross-domain sentiment classification (CDSC) is an importance task in domain adaptation and sentiment classification. Due to the domain discrepancy, a sentiment classifier trained on source domain data may not works well on target domain data. In recent years, many researchers have used deep neural network models for cross-domain sentiment classification task, many of which use Gradient Reversal Layer (GRL) to design an adversarial network structure to train a domain-shared sentiment classifier. Different from those methods, we proposed Hierarchical Attention Generative Adversarial Networks (HAGAN) which alternately trains a generator and a discriminator in order to produce a document representation which is sentiment-distinguishable but domain-indistinguishable. Besides, the HAGAN model applies Bidirectional Gated Recurrent Unit (Bi-GRU) to encode the contextual information of a word and a sentence into the document representation. In addition, the HAGAN model use hierarchical attention mechanism to optimize the document representation and automatically capture the pivots and non-pivots. The experiments on Amazon review dataset show the effectiveness of HAGAN.
Deformable kernel networks for guided depth map upsampling

We address the problem of upsampling a low-resolution (LR) depth map using a registered high-resolution (HR) color image of the same scene. Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estimate structural details from LR depth and HR color images, and regress upsampling results directly from the networks. In this paper, we revisit the weighted averaging process that has been widely used to transfer structural details from hand-crafted visual features to LR depth maps. We instead learn explicitly sparse and spatially-variant kernels for this task. To this end, we propose a CNN architecture and its efficient implementation, called the deformable kernel network (DKN), that outputs sparse sets of neighbors and the corresponding weights adaptively for each pixel. We also propose a fast version of DKN (FDKN) that runs about 17 times faster (0.01 seconds for a HR image of size 640 x 480). Experimental results on standard benchmarks demonstrate the effectiveness of our approach. In particular, we show that the weighted averaging process with 3 x 3 kernels (i.e., aggregating 9 samples sparsely chosen) outperforms the state of the art by a significant margin.
Disclosing the diagnosis of schizophrenia: A pilot study of the ‘Coming Out Proud’ intervention

Background: Schizophrenia is one of the most stigmatized psychiatric disorders, and disclosing it is often a source of stress to individuals with the disorder. The Coming Out Proud (COP) group intervention is designed to reduce the stigma’s negative impact and help participants decide if they want to disclose their disorder. Aims: To assess the effect of the COP intervention in individuals with the diagnosis of schizophrenia. Methods: A pilot study of 3 2-hour group lessons (6–12 participants) per week. Individuals were selected from three specialized outpatient services in São Paulo, Brazil; 46 people were willing to participate, 11 dropped out during the intervention and 4 were excluded due to low intelligence quotient (IQ), resulting in a final sample of 31 participants. Outcomes were assessed before (T0/baseline) and after (T1/directly) after the COP intervention, and at 3-week follow-up (T2/3 weeks after T1). We applied eight scales, of which four scales are analyzed in this article (Coming Out with Mental Illness Scale (COMIS), Cognitive Appraisal of Stigma as a Stressor (CogApp), Self-Stigma of Mental Illness Scale-Short Form (SSMIS) and Perceived Devaluation-Discrimination Questionnaire (PDDQ)). Results: People who completed the COP intervention showed a significant increase in the decision to disclose their diagnosis (22.5% in T0 vs 67.7% in T2). As to the perception of stigma as a stressor, mean values significantly increased after the intervention (T0 = 3.83, standard deviation (SD) = . 92 vs T2 = 4.44, SD = 1.05; p = . 006). Two results had marginal significance: self-stigma was reduced (T0 = 3.10, SD = 1.70 vs T2 = 2.73, SD = 1.87; p = .063), while perceived discrimination increased (T0 = 2.68, SD = .55 vs T2 = 2.93, SD = .75; p = .063). Conclusion: This study suggests that the COP group intervention facilitated participants’ disclosure decisions, and the increasing awareness of stigma as a stressor in life may have facilitated their decision to eventually disclose their disorder. The results raise questions that require further analysis, taking sociocultural factors into account, as stigma is experienced differently across cultures.
Combination of Rail Fence Cipher Algorithm and Least Significant Bit Technique to Secure the Image File

Image is a visual representation of an object The process of sending information, especially images, can be done anywhere and with any intermediary, but not all information is public. Sometimes someone needs privacy or security from data or information that will be sent or received. One way to secure image files is to use cryptography. Cryptography is the study of how to keep data or messages safe when sent, from the sender to the recipient, without experiencing interference from third parties. In this study cryptography used is Rail Fence Cipher that was combined with steganography, so that the secret message could not be seen in plain view. Steganography serves to hide secret messages in other messages, so the secret is a very hidden existence. There are several methods in Steganography, one of which is Least Significant Bit. The concept of Least Significant Bit works by replacing the last bit of RGB image, where the message is converted first into bits. Based on experiments conducted on the Rail Fence Cipher algorithm, the time of encryption and decryption needed when the resolution of the image gets larger is linearly proportional to the value. The image does not show significant changes before and after inserting a secret message.
Salvaging Falsified Instrumental Variable Models

What should researchers do when their baseline model is falsified? We recommend reporting the set of parameters that are consistent with minimally nonfalsified models. We call this the falsification adaptive set (FAS). This set generalizes the standard baseline estimand to account for possible falsification. Importantly, it does not require the researcher to select or calibrate sensitivity parameters. In the classical linear IV model with multiple instruments, we show that the FAS has a simple closed‐form expression that only depends on a few 2SLS coefficients. We apply our results to an empirical study of roads and trade. We show how the FAS complements traditional overidentification tests by summarizing the variation in estimates obtained from alternative nonfalsified models.
Caracterização Sociodemográfica dos Doentes Internados por Tentativas de Suicídio e Comportamentos Autolesivos em Psiquiatria

Introdução: Os comportamentos suicidas acarretam de forma inequívoca consequências tanto a nível da sociedade como no seio familiar. Estes comportamentos podem dividir‑se em atos suicidas (tentativas de suicídio e suicídio consumado) e comportamentos autolesivos, sendo estes importantes preditores de risco para o suicídio. O objetivo deste trabalho baseia‑se na caracterização sociodemográfica do doente internado por tentativas de suicídio e comportamentos autolesivos num período de seis anos (2008‑2013), na comparação destes comportamentos em função do sexo e na avaliação da evolução desses internamentos ao longo desses seis anos. Métodos: Utilizou‑se como fonte de dados os registos dos processos clínicos. Resultados: No período em estudo as tentativas de suicídio foram responsáveis por 13,1% de todos os internamentos enquanto os comportamentos autolesivos estiveram na origem de 0,9%. A maioria permanece internada por um período relativamente curto. Ambos predominam no sexo feminino, na faixa etária dos 31‑40 anos e no concelho de Viana do Castelo. Constata‑se que a maioria dos internamentos por tentativa de suicídio ocorre no mês de fevereiro e na Primavera, fruto de uma perturbação de adaptação. Os comportamentos autolesivos verificam‑se sobretudo em abril e outubro, no Verão, apresentando a maioria psicose afetiva. Em ambos os casos, a intoxicação é o método predominante, sendo os agentes psicotrópicos os mais usados. Em 2013 regista‑se o maior número de internamentos por tentativa de suicídio, não se registando nenhum internamento por comportamentos autolesivos. Conclusão: O presente estudo permite confirmar a importância das tentativas de suicídio e comportamentos autolesivos na saúde pública.
Probing Cosmic Acceleration Using Model-independent Parameterizations and Three Kinds of Supernova Statistics Techniques

In this work, we explore the evolution of the dark energy equation of state ω using Chevalliear–Polarski–Linder parameterization and binned parameterizations. For binned parameterizations, we adopt three methods to choose the redshift interval: (1) ensure that “△z = const,” where △z is the width of each bin; (2) ensure that “n△z = const,” where n is the number of SN Ia in each bin; and (3) treat redshift discontinuity points as model parameters, i.e., “free △z. ” For observational data, we adopt JLA SN Ia samples, SDSS DR12 data, and Planck 2015 distance priors. In particular, for JLA SN Ia samples, we consider three statistic techniques: magnitude statistics, which is the traditional method; flux statistics, which reduces the systematic uncertainties of SN Ia; and improved flux statistics, which can reduce the systematic uncertainties and give tighter constrains at the same time. The results are as follows. For all the cases, ω = −1 is always satisfied at the 1σ confidence regions; this means that ΛCDM is still favored by current observations. For magnitude statistics, the “free △z” model will give the smallest error bars. However, this conclusion does not hold true for flux statistics and improved flux statistic. The improved flux statistic yields the largest present fractional density of matter Ωm; in addition, this technique will give the largest current deceleration parameter q0 , which reveals the universe with the slowest cosmic acceleration.
Imaging-based internal body temperature measurements: The journal Temperature toolbox

ABSTRACT Noninvasive imaging methods of internal body temperature are in high demand in both clinical medicine and physiological research. Thermography and thermometry can be used to assess tissue temperature during thermal therapies: ablative and hyperthermia treatments to ensure adequate temperature rise in target tissues but also to avoid collateral damage by heating healthy tissues. In research use, measurement of internal body temperature enables us the production of thermal maps on muscles, internal organs, and other tissues of interest. The most used methods for noninvasive imaging of internal body temperature are based on different parameters acquired with magnetic resonance imaging, ultrasound, computed tomography, microwave radiometry, photoacoustic imaging, and near-infrared spectroscopy. In the current review, we examine the aforementioned imaging methods, their use in estimating internal body temperature in vivo with their advantages and disadvantages, and the physical phenomena the thermography or thermometry modalities are based on.
First Record of a Species of Mermithidae Braun, 1883 Infecting a Decapod, Palaemon paludosus (Palaemonidae)

ABSTRACT The hemocoel of 26 of 30 (86%) eastern grass shrimps, Palaemon paludosus (Gibbes, 1850) (Decapoda: Palaemonidae), captured during June 2017 from several freshwater lakes near Leesburg and Lake Kissimmee, Florida, was infected by juveniles of a nematode species (Mermithidae sp.). Some infected eastern grass shrimps were preserved whole for histopathology, whereas others were dissected to excise parasitic juvenile nematodes, and still others were kept alive in glass aquaria such that post-parasitic (emerged) juvenile nematodes could be opportunistically observed alive and ultimately preserved. Parasitic and post-parasitic juvenile nematodes had cuticle cross-fibers, cephalic papillae, cup-shaped amphids, a horn-shaped vagina, a trophosome, and a caudal appendage, which collectively diagnosed them as Mermithidae sp. They differed from those of nematomorphs (Nematomorpha) by lacking 4 giant cells anteriorly, tegumental bristles, scale or plate-like areoles, a bifurcate or trifurcate posterior end, and an anus. A phylogenetic analysis of the small subunit rDNA (18S) that included all of the available mermithid sequences corresponding to morphologically diagnosed specimens recovered Mermithidae sp. within the clade of mermithids and sister to Ovomermis sinensis Chen, Jian, and Ren, 1991. This is the first record of a mermithid infection in a decapod and first report of a mermithid infection in an aquatic crustacean from North America (another mermithid infects a terrestrial isopod there). The high prevalence of infection and the multiple geographic localities harboring infected eastern grass shrimps indicated that these infections were not spurious. Because no other decapod is confirmed as a mermithid host, we suspect that these specimens likely represent a new species with a life cycle worth studying, since none for a mermithid involving a decapod nor a crustacean has been elucidated to date. We also provide a table of all mermithid and nematomorph infections in crustaceans.
Reply to Mortazavi, “Acquired Antibiotic Resistance in Escherichia coli Exposed to Simulated Microgravity: Possible Role of Other Space Stressors and Adaptive Responses”

We thank Dr. Mortazavi for pointing out the role of radiation as an important factor under microgravity conditions (1) while referring to our paper (2). We agree with Dr. Mortazavi that radiation is an important component when it comes to assessment of bacterial response to the space environment. We have previously published papers on radiation-resistant spore-producing Bacillus strains isolated from spacecraft cleanroom facilities that are of planetary protection concern …