Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-selection inference via algorithmic stability

Modern approaches to data analysis make extensive use of data-driven model selection. The resulting dependencies between the selected model and data used for inference invalidate statistical guarantees derived from classical theories. The framework of post-selection inference (PoSI) has formalized this problem and proposed corrections which ensure valid inferences. Yet, obtaining general principles that enable computationally-efficient, powerful PoSI methodology with formal guarantees remains a challenge. With this goal in mind, we revisit the PoSI problem through the lens of algorithmic stability. Under an appropriate formulation of stability---one that captures closure under post-processing and compositionality properties---we show that stability parameters of a selection method alone suffice to provide non-trivial corrections to classical z-test and t-test intervals. Then, for several popular model selection methods, including the LASSO, we show how stability can be achieved through simple, computationally efficient randomization schemes. Our algorithms offer provable unconditional simultaneous coverage and are computationally efficient; in particular, they do not rely on MCMC sampling. Importantly, our proposal explicitly relates the magnitude of randomization to the resulting confidence interval width, allowing the analyst to tune interval width to the loss in utility due to randomizing selection.
Neural network approximation and estimation of classifiers with classification boundary in a Barron class

We prove bounds for the approximation and estimation of certain classification functions using ReLU neural networks. Our estimation bounds provide a priori performance guarantees for empirical risk minimization using networks of a suitable size, depending on the number of training samples available. The obtained approximation and estimation rates are independent of the dimension of the input, showing that the curse of dimension can be overcome in this setting; in fact, the input dimension only enters in the form of a polynomial factor. Regarding the regularity of the target classification function, we assume the interfaces between the different classes to be locally of Barron-type. We complement our results by studying the relations between various Barron-type spaces that have been proposed in the literature. These spaces differ substantially more from each other than the current literature suggests.
A Multi-class Approach - Building a Visual Classifier based on Textual Descriptions using Zero-Shot Learning

Machine Learning (ML) techniques for image classification routinely require many labelled images for training the model and while testing, we ought to use images belonging to the same domain as those used for training. In this paper, we overcome the two main hurdles of ML, i.e. scarcity of data and constrained prediction of the classification model. We do this by introducing a visual classifier which uses a concept of transfer learning, namely Zero-Shot Learning (ZSL), and standard Natural Language Processing techniques. We train a classifier by mapping labelled images to their textual description instead of training it for specific classes. Transfer learning involves transferring knowledge across domains that are similar. ZSL intelligently applies the knowledge learned while training for future recognition tasks. ZSL differentiates classes as two types: seen and unseen classes. Seen classes are the classes upon which we have trained our model and unseen classes are the classes upon which we test our model. The examples from unseen classes have not been encountered in the training phase. Earlier research in this domain focused on developing a binary classifier but, in this paper, we present a multi-class classifier with a Zero-Shot Learning approach.
Regional homogeneity in first-episode patients with mild-to-moderate depressive disorder before and after cognitive-behavior therapy: a resting-state fMRI study

Objective To explore the effect of cognitive-behavior therapy(CBT) for the first-episode patients with mild to moderate depression using functional magnetic resonance imaging (fMRI) in resting-state. Methods 13 first-episode treatment-naive patients who suffered from major depressive disorder (MDD) from a mild to moderate degree and 13 matched healthy control subjects underwent fMRI scan. Afterwards, patients were scanned again after the 6-week CBT. The whole brain ReHo maps of subjects were calculated by DPARSF software. Two-sample t-test for comparison between groups and paired t-test for comparisons between pre-and post-treatment were conducted by SPM8. Results Compared with the controls, at baseline, patients exhibited decreased ReHo in right supramarginal gyrus(t=-3.18, P<0.05) and right opercular part of inferior frontal gyrus(t=-2.99, P<0.05) which tended to normalize after treatment. After treatment, patients exhibited increased ReHo in left middle frontal gyrus(t=3.24, P<0.05) and right middle frontal gyrus(t=3.06, P<0.05). From pre-to post-treatment, patients showed that ReHo increased in hippocampus(t=-3.92, P<0.05). Conclusion CBT may have an effect on MDD via activating both right inferior frontal gyrus and dorsolateral prefrontal cortex in cognitive inhibition system. Key words: Major depressive disorder; Cognitive-behavior therapy; Functional magnetic resonance; Regional homogeneity; Resting-state
Quantifying the mechanics of locomotion of the schistosome pathogen with respect to changes in its physical environment

Schistosomiasis is a chronic and morbid disease of poverty affecting approximately 200 million people worldwide. Mature schistosome flatworms wander in the host’s hepatic portal and mesenteric venous system where they encounter a range of blood flow conditions and geometrical confinement. However, the mechanisms that support schistosome locomotion and underlie the pathogen’s adaptation to its physical environment are largely unknown. By combining microfabrication and traction force microscopy, we developed various in vitro assays to quantify the mechanics of locomotion of adult male S. mansoni in different physiologically relevant conditions. We show that in unconfined settings, the parasite undergoes two-anchor marching mediated by the coordinated action of its oral and ventral suckers. This mode of locomotion is maintained when the worm faces an external flow, to which it responds by adjusting the strength of its suckers. In geometrically confined conditions, S. mansoni switches to a different crawling modality by generating retrograde peristaltic waves along its body, a mechanism shared with terrestrial and marine worms. But while the surface of most worms has backward-pointing bristles that rectify peristaltic waves and facilitate forward locomotion, S mansoni has isotropically oriented tubercles. This requires tight coordination between muscle contraction and substrate friction but confers S. mansoni the ability to reverse its direction of locomotion without turning its body, which is likely advantageous to maneuver in narrow bore vessels. We show that the parasite can also coordinate the action of its suckers with its peristaltic body contractions to increase crawling speed. Throughout this study, we report on a number of biomechanical parameters to quantify the motility of adult schistosomes (e.g. sucker grabbing strength, rate of detachment under flow, peristaltic wave properties and traction stresses). The new series of in vitro assays make it possible to quantify key phenotypical aspects of S. mansoni motility that could guide the discovery of new drugs to treat schistosomiasis.
A Modified Kwee–Van Woerden Method for Eclipse Minimum Timing with Reliable Error Estimates

The Kwee–van Woerden (KvW) method used for the determination of eclipse minimum times has been a staple in eclipsing binary research for decades, due its simplicity and the independence of external input parameters, which also makes it well-suited to obtaining timings of exoplanet transits. However, its estimates of the timing error have been known to have a low reliability. During the analysis of very precise photometry of CM Draconis eclipses from TESS space mission data, KvW’s original equation for the timing error estimate produced numerical errors, which evidenced a fundamental problem in this equation. This contribution introduces an improved approach for calculating the timing error with the KvW method. A code that implements this improved method, together with several further updates of the original method, are presented. An example of the application to CM Draconis light curves from TESS is given. The eclipse minimum times are derived with the KvW method’s three original light curve folds, but also with five and seven folds. The use of five or more folds produces minimum timings with a substantially better precision. The improved method of error calculation delivers consistent timing errors which are in excellent agreement with error estimates obtained by other means. In the case of TESS data from CM Draconis, minimum times with an average precision of 1.1 s are obtained. Reliable timing errors are also a valuable indicator for evaluating if a given scatter in an O-C diagram is caused by measurement errors or by a physical period variation.
CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement

We propose a new Generative Adversarial Network for Compressed Video quality Enhancement (CVEGAN). The CVEGAN generator benefits from the use of a novel Mul2Res block (with multiple levels of residual learning branches), an enhanced residual non-local block (ERNB) and an enhanced convolutional block attention module (ECBAM). The ERNB has also been employed in the discriminator to improve the representational capability. The training strategy has also been re-designed specifically for video compression applications, to employ a relativistic sphere GAN (ReSphereGAN) training methodology together with new perceptual loss functions. The proposed network has been fully evaluated in the context of two typical video compression enhancement tools: post-processing (PP) and spatial resolution adaptation (SRA). CVEGAN has been fully integrated into the MPEG HEVC video coding test model (HM16.20) and experimental results demonstrate significant coding gains (up to 28% for PP and 38% for SRA compared to the anchor) over existing state-of-the-art architectures for both coding tools across multiple datasets.
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks

Facial expressions are one of the most powerful ways to depict specific patterns in human behavior and describe the human emotional state. However, despite the impressive advances of affective computing over the last decade, automatic video-based systems for facial expression recognition still cannot correctly handle variations in facial expression among individuals as well as cross-cultural and demographic aspects. Nevertheless, recognizing facial expressions is a difficult task, even for humans. This paper investigates the suitability of state-of-the-art deep learning architectures based on convolutional neural networks (CNNs) to deal with long video sequences captured in the wild for continuous emotion recognition. For such an aim, several 2D CNN models that were designed to model spatial information are extended to allow spatiotemporal representation learning from videos, considering a complex and multi-dimensional emotion space, where continuous values of valence and arousal must be predicted. We have developed and evaluated convolutional recurrent neural networks, combining 2D CNNs and long short term-memory units and inflated 3D CNN models, which are built by inflating the weights of a pre-trained 2D CNN model during fine-tuning, using application-specific videos. Experimental results on the challenging SEWA-DB dataset have shown that these architectures can effectively be fine-tuned to encode spatiotemporal information from successive raw pixel images and achieve state-of-the-art results on such a dataset.
Looking for Obscured Young Star Clusters in NGC 1313

Using recently acquired Hubble Space Telescope NIR observations (J, Paβ, and H bands) of the nearby galaxy NGC 1313, we investigate the timescales required by a young star cluster to emerge from its natal cloud. We search for extincted star clusters, potentially embedded in their natal cloud as either (1) compact sources in regions with high Hα/Paβ extinctions or (2) compact H ii regions that appear as point-like sources in the Paβ emission map. The NUV–optical–NIR photometry of the candidate clusters is used to derive their ages, masses, and extinctions via a least-χ 2 spectral energy distribution broad- and narrowband fitting process. The 100 clusters in the final samples have masses in the range and moderate extinctions, E(B − V) ≲ 1.0 mag. Focusing on the young clusters (0–6 Myr), we derive a weak correlation between extinction and age of the clusters. Almost half of the clusters have low extinctions, E(B − V) < 0.25 mag, already at very young ages (≤3 Myr), suggesting that dust is quickly removed from clusters. A stronger correlation is found between the morphology of the nebular emission (compact, partial or absent, both in Hα and Paβ) and cluster age. Relative fractions of clusters associated with a specific nebular morphology are used to estimate the typical timescales for clearing the natal gas cloud, resulting in between 3 and 5 Myr, ∼1 Myr older than what was estimated from NUV–optical-based cluster studies. This difference hints at a bias for optical-only-based studies, which James Webb Space Telescope will address in the coming years.
A new transient ultraluminous X-ray source in NGC 7090

We report on the discovery of a new, transient ultraluminous X-ray source (ULX) in the galaxy NGC 7090. This new ULX, which we refer to as NGC 7090 ULX3, was discovered via monitoring with Swift during 2019–2020, and to date has exhibited a peak luminosity of LX ∼ 6 × 1039 erg s−1. Archival searches show that, prior to its recent transition into the ULX regime, ULX3 appeared to exhibit a fairly stable luminosity of LX ∼ 1038 erg s−1. Such strong long-time-scale variability may be reminiscent of the small population of known ULX pulsars, although deep follow-up observations with XMM–Newton and NuSTAR do not reveal any robust X-ray pulsation signals. Pulsations similar to those seen from known ULX pulsars cannot be completely excluded, however, as the limit on the pulsed fraction of any signal that remains undetected in these data is ≲20 per cent. The broad-band spectrum from these observations is well modelled with a simple thin disc model, consistent with sub-Eddington accretion, which may instead imply a moderately large black hole accretor (MBH ∼ 40 M⊙). Similarly, though, more complex models consistent with the super-Eddington spectra seen in other ULXs (and the known ULX pulsars) cannot be excluded given the limited signal-to-noise ratio of the available broad-band data. The nature of the accretor powering this new ULX therefore remains uncertain.
Resting shear elastic modulus as a marker of peripheral fatigue during maximal isometric contractions in humans

The aim of this study was to investigate whether the resting Vastus Lateralis (VL) muscle shear elastic modulus (µ), evaluated by shear wave elastography, represents peripheral fatigue during repetition of isometric maximal voluntary contractions (MVCs) of the knee extensor (KE) muscles. Eight healthy well-trained males repeated 60 isometric MVCs of the KE muscles (6 × 10 MVCs; 5 s on/5 s off). Single and double electrical stimulations were delivered to the femoral nerve every ten MVCs during contraction and at rest. The amplitude and properties of the potentiated torque following single (Twpot) double electrostimulation and the amplitude of the concomitant VL compound action potential were considered to be indicators of peripheral fatigue. The resting VLµ was measured during a 5-s rest period after each MVC and electrical stimulation series. The resting VLµ significantly decreased (-21.8 ± 3.9%; P < 0.001) by the end of the fatigue protocol, decreasing from the 10th MVC to the end of the exercise (60th MVC) for all participants, with the loss ranging from 18 to 29%. The potentiated doublet and single twitch torque (Twpot) decreased by 42.5 ± 10.8% and 55.7 ± 8.8%, respectively, by the end of exercise (P < 0.001 for both). The relative mechanical properties of Twpot, i.e. electromechanical delay (P <0 .001), contraction time (P = 0.004), and maximal rate of torque development/relaxation (P < 0.001) also changed significantly during exercise. This study shows that the kinetics of the resting VLµ is associated with changes in both voluntary and electrostimulated torque amplitudes and electromechanical properties of the single twitch during the repetition of maximal voluntary fatiguing exercise. Changes in the resting VLµ may reflect a decline in muscle function, e.g. impairment of excitation-contraction coupling, contractile processes, and/or elastic properties, throughout the increase in muscle compliance, directly affecting force transmission.
On the Feasibility and Enhancement of the Tuple Space Explosion Attack against Open vSwitch

Being a crucial part of networked systems, packet classification has to be highly efficient; however, software switches in cloud environments still face performance challenges. The recently proposed Tuple Space Explosion (TSE) attack exploits an algorithmic deficiency in Open vSwitch (OVS). In TSE, legitimate low-rate attack traffic makes the cardinal linear search algorithm in the Tuple Space Search (TSS) algorithm to spend an unaffordable time for classifying each packet resulting in a denial-of-service (DoS) for the rest of the users. In this paper, we investigate the feasibility of TSE from multiple perspectives. Besides showing that TSE is still efficient in the newer version of OVS, we show that when the kernel datapath is compiled from a different source, it can degrade its performance to ~1% of its baseline with less than 1 Mbps attack rate. Finally, we show that TSE is much less effective against OVS-DPDK with userspace datapath due to the enhanced ranking process in its TSS implementation. Therefore, we propose TSE 2.0 to defeat the ranking process and achieve a complete DoS against OVS-DPDK. Furthermore, we present TSE 2.1, which achieves the same goal against OVS-DPDK running on multiple cores without significantly increasing the attack rate.
Identity domains in complex behavior: Toward a biology of personality

Personality traits offer considerable insight into the biological basis of individual differences. However, existing approaches toward understanding personality across species rely on subjective criteria and limited sets of behavioral readouts, resulting in noisy and often inconsistent outcomes. Here, we introduce a mathematical framework for studying individual differences along dimensions with maximum consistency and discriminative power. We validate this framework in mice, using data from a system for high-throughput longitudinal monitoring of group-housed mice that yields a variety of readouts from all across an individual’s behavioral repertoire. We describe a set of stable traits that capture variability in behavior and gene expression in the brain, allowing for better informed mechanistic investigations into the biology of individual differences.
Use of Spirulina platensis in treatment of fish farming wastewater

Each year, the amount of fish produced around the world increases, which contributes to several environmental impacts such as the disposal of effluents without treatment in the environment. This scientific work had as main objective the development of Spirulina platensis in fish effluents, a low cost medium for the production of biomass, in order to reduce the levels of some inorganic nutrients to reach the allowed parameters by the Brazilian environmental standards for effluent disposal and to enable reuse of the water. Nile tilapia (Oreochromis niloticus) fingerlings were produced in fresh water. The effluent generated by the culture was transferred to a pool where seawater was added until the salinity of 10 ‰ was reached. A strain of cyanobacteria, Spirulina platensis, was inserted into the mixture in order to remove the nutrients dissolved on the fish culture effluent. The abiotic parameters analyzed were: absorbance, pH, dissolved oxygen, temperature, salinity and concentrations of ammonia, nitrite, nitrate and phosphate. The results revealed that the maximum cellular density of S. platensis resulted in the production of 0.22 g L-1 of dry biomass and maximum productivity of 0.03 g L-1 day-1. The concentration of ammonia, nitrite, nitrate and phosphate got lowered by more than 94.8%, maintaining the nutrient levels within the standards those required by Brazilian environmental standards. Hence, this effluent has become adequate for reuse in fish production or could be safely disposed in nature.
Sample preparation techniques in analytical chemistry

Contributors. Preface. Chapter 1. Sample Preparation: An Analytical Perspective (Somenath Mitra and Roman Brukh). SECTION A: EXTRACTION AND ENRICHMENT IN SAMPLE PREPARATION. Chapter 2. Principles of Extraction and the Extraction of Semivolatile Organics From Liquids (Martha J.M. Wells). Chapter 3. Extraction of Semivolatile Organic Compounds From Solid Matrices (Dawen Kou and Somenath Mitra). Chapter 4. Extraction of Volatile Organic Compounds From Solids and Liquids (Gregory C. Slack, et al.). Chapter 5. Preparation of Samples for Metals Analysis (Barbara B. Kebbekus). SECTION B: SAMPLE PREPARATION FOR NUCLEIC ACID ANALYSIS. Chapter 6. Sample Preparation in DNA Analysis (Satish Parimoo and Bhama Parimmo). Chapter 7. Sample Preparation in RNA Analysis (Bhama Parimoo and Satish Parimoo). Chapter 8. Techniques for the Extraction, Isolation and Purification of Nucleic Acids (Mahesh Karwa and Somenath Mitra). SECTION C: SAMPLE PREPARATION IN MICROSCOPY AND SPECTROSCOPY. Chapter 9. Sample Preparation for Microscope and Spectroscopic Characterization of Solid Surfaces and Films (Sharmila M. Mukhopadhyay). Chapter 10. Surface Enhancement by Sample and Substrate Preparation Techniques in Raman and Infrared Spectroscopy (Zafar Iqbal). Index.
Constrained, Global Optimization of Functions with Lipschitz Continuous Gradients.

We present two first-order, sequential optimization algorithms to solve constrained optimization problems. We consider a black-box setting with a priori unknown, non-convex objective and constraint functions that have Lipschitz continuous gradients. The proposed algorithms balance the exploration of the a priori unknown feasible space with the pursuit of global optimality within in a pre-specified finite number of first-order oracle calls. The first algorithm accommodates an infeasible start, and provides either a near-optimal global solution or establishes infeasibility. However, the algorithm may produce infeasible iterates during the search. For a strongly-convex constraint function and a feasible initial solution guess, the second algorithm returns a near-optimal global solution without any constraint violation. In contrast to existing methods, both of the algorithms also compute global suboptimality bounds at every iteration. We also show that the algorithms can satisfy user-specified tolerances in the computed solution with near-optimal complexity in oracle calls for a large class of optimization problems. We propose tractable implementations of the algorithms by exploiting the structure afforded by the Lipschitz continuous gradient property.