Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing Conditional Independence of Discrete Distributions

We study the problem of testing conditional independence for discrete distributions. Specifically, given samples from a discrete random variable (X, Y, Z) on domain $[\ell_{1}]\times[\ell_{2}]\times[n]$, we want to distinguish, with probability at least 2/3, between the case that $X$ and $Y$ are conditionally independent given $Z$ from the case that (X,\ Y,\ Z) is ɛ-far, in ℓ1-distance, from every distribution that has this property. Conditional independence is a concept of central importance in probability and statistics with a range of applications in various scientific domains. As such, the statistical task of testing conditional independence has been extensively studied in various forms within the statistics and econometrics communities for nearly a century. Perhaps surprisingly, this problem has not been previously considered in the framework of distribution property testing and in particular no tester with sublinear sample complexity is known, even for the important special case that the domains of X and Y are binary. The main algorithmic result of this work is the first conditional independence tester with sublinear sample complexity for discrete distributions over $[\ell_{1}]\times[ell_{2}]\times[n]$. To complement our upper bounds, we prove information-theoretic lower bounds establishing that the sample complexity of our algorithm is optimal, up to constant factors, for a number of settings. Specifically, for the prototypical setting when ℓ1,ℓ2=O(1), we show that the sample complexity of testing conditional independence (upper bound and matching lower bound) is \begin{equation*} \Theta(\max(n^{1/2}/\varepsilon^{2},\min(n^{7/8}/varepsilon,n^{6/7}/\varepsilon^{8/7}))) \end{equation*} To obtain our tester, we employ a variety of tools, including (1) a suitable weighted adaptation of the flattening technique [DK16], and (2) the design and analysis of an optimal (unbiased) estimator for the following statistical problem of independent interest: Given a degree -d polynomial Q:Rn→R and sample access to a distribution $p$ over [n], estimate Q(p_{1}, …, p_{n}) up to small additive error. Obtaining tight variance analyses for specific estimators of this form has been a major technical hurdle in distribution testing (see, e.g., [CDVV14]). As an important contribution of this work, we develop a general theory providing tight variance bounds for all such estimators. Our lower bounds, established using the mutual information method, rely on novel constructions of hard instances that may be useful in other settings.
Micro-facial movement detection using spatio-temporal features

Micro-facial expressions are fast, subtle movements of facial muscles that occur when someone is attempting to conceal their true emotion. Detecting these movements for a human is di�cult, as the movement could appear and disappear within half of a second. Recently, research into detecting micro-facial movements using computer vision and other techniques has emerged with the aim of outperforming a human. The motivation behind a lot of this research is the potential applications in security, healthcare and emotional-based training. The research has also introduced some ethical concerns on whether it is okay to detect micro-movements when people do not know they are showing them. The main aim of this thesis is to investigate and develop novel ways of detecting micro-facial movements using features based in the spatial and temporal domains. The contributions towards this aim are: an extended feature descriptor to describe micro-facial movement namely Local Binary Patterns on Three Orthogonal Planes (LBP-TOP) combined with Gaussian Derivatives (GD); a dataset of spontaneously induced micro-facial movements, namely Spontaneous Activity of Micro-Movements (SAMM); an individualised baseline method for micromovement detection that forms an Adaptive Baseline Threshold (ABT); Facial Action Coding System (FACS)-based regions are proposed to focus on the local movement of relevant facial areas. The LBP-TOP with GD feature was developed to improve on an established feature and use the GD to enhance the facial features. Using machine learning, the method performs well achieving an accuracy of 92.6%. Next a new dataset, SAMM, was introduced that improved on the limitations of previous sets, including a wider demographic, increased resolution and comprehensively FACS coded. An individualised baseline method was the introduced and tested using the new dataset. Using feature di�erence instead of machine learning, the performance increased with a recall of 0.8429 on the maximum thresholding and a further increase of the recall to 0.9125 when using the ABT. To increase the relevance of what is being processed on the face, FACS-based regions were created. By focusing on local regions and individualised baselines, this method outperformed similar state-of-the-art with an Area Under Curve (AUC) of 0.7513. The research into detecting micro-movements is still in it's infancy, and much more can be done to advance this �eld. While machine learning can �nd patterns in normal facial expressions, it is the feature di�erence methods that perform the best when detecting the subtle changes of the face. By using this and comparing the movement against a person's baseline, the micro-movements can �nally be accurately detected.
Evaluation of Particle Motion in Solid-Liquid Two-Phase Pipe Flow with Downward / Upward Flow Directions

Flow structure in solid-liquid two-phase turbulent flow has been measured by using a Particle Tracking Velocimetry (PTV). Both downward and upward flows in a pipe are tested to evaluate the effect of the flow direction on fluid and particle motions. The pipe diameter is 26 mm and the mean mainstream velocity is 0.75 m/s. The carrier fluid is used water. The spherical glass particles with 625 μm in mean diameter are used for the solid particles. The volume fraction of glass particle is set to 0.6 %. The PTV, which is capturing images by a high-speed video camera, is applied for measurement to obtain velocities in the single-phase flow, both particles and the fluid in the two-phase flow. From the instantaneous velocity data, the distributions of the mean velocity and the RMS velocity of both particles and the fluid are obtained. The mean velocity of particles has larger than that of the fluid in the downward flow, and it has smaller than that in the upward flow. The RMS velocities of both longitudinal and radial directions of particles have larger than that of the fluid in the downward flow. The water-phase RMS velocity in longitudinal direction is enhanced by particles. On the other hand, in the upward flow, the longitudinal RMS velocity of particles has a flat shape in whole region of the pipe. The particle number density distribution depends on the flow direction. The time history of the glass particle number is counted from PTV results. The frequency analysis for the particle number is also tested. When the particles form a cluster in the solid-liquid pipe flow, the frequency of the particle number is varied. FFT analysis results for the time history of the particle number shows that the particle cluster is hardly formed at near the wall in both the downward and the upward flow.
A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery

We propose a unified framework to solve general low-rank plus sparse matrix recovery problems based on matrix factorization, which covers a broad family of objective functions satisfying the restricted strong convexity and smoothness conditions. Based on projected gradient descent and the double thresholding operator, our proposed generic algorithm is guaranteed to converge to the unknown low-rank and sparse matrices at a locally linear rate, while matching the bestknown robustness guarantee (i.e., tolerance for sparsity). At the core of our theory is a novel structural Lipschitz gradient condition for low-rank plus sparse matrices, which is essential for proving the linear convergence rate of our algorithm, and we believe is of independent interest to prove fast rates for general superposition-structured models. We illustrate the application of our framework through two concrete examples: robust matrix sensing and robust PCA. Empirical experiments corroborate our theory.
HPTLC-Densitometric and RP-HPLC Method Development and Validation for Determination of Salbutamol Sulphate, Bromhexine Hydrochloride and Etofylline in Tablet Dosage Forms

Different HPTLC-densitometric and RP-HPLC methods are developed for the simultaneous determination of salbutamol sulphate, bromhexine hydrochloride and etofylline in pharmaceutical tablets. HPTLC method was based on separation of all three drugs followed by densitometric measurements of their spots at 275 nm using CAMAG, TLC scanner III. The separation was carried out on Merck HPTLC aluminium plates of silica gel 60 F254, using acetone: methanol: formic acid (9:3:0.01) as mobile phase at room temperature (25 ± 2°C). The HPLC separation was carried out using a mobile phase consisting of 0.02 M ammonium acetate buffer: organic phase (where organic phase MeOH: ACN in ratio of 70:30) adjusted to pH 4.5 with gradient elution. The column used was Waters Spherisorb®C18 bonded with 5 μm (4.6 x 250 mm) with a flow rate of 1 mL min-1 and UV detection at 275 nm simultaneously. The mean recovery ofboth HPTLC and HPLC methods were found to be within 99.44 to 99.85 % w/w. Both HPTLCdensitometric and RP-HPLC methods were statistically validated and can be used for analysis of combined dose tablet formulation containing salbutamol sulphate, bromhexine hydrochloride and etofylline.
Measuring Quality of Life: A Case Study of Agritourism in the Northeast

Evaluation of Extension programs is critical for accountability and improved program effectiveness. However, measuring outcomes remains a challenge for many types of programs, especially those that aim to improve the quality of life of participants. The study reported here examined changes in quality of life indicators as part of a measure of farm viability. Farmers participated in agritourism trainings in the Northeast, and impacts of the trainings were evaluated through an Internet survey 1 year later. The index developed to measure quality of life for the agritourism program may be adapted for evaluation of a wide variety of Extension programs.
High performance communication by people with paralysis using an intracortical brain-computer interface

Brain-computer interfaces (BCIs) have the potential to restore communication for people with tetraplegia and anarthria by translating neural activity into control signals for assistive communication devices. While previous pre-clinical and clinical studies have demonstrated promising proofs-of-concept (Serruya et al., 2002; Simeral et al., 2011; Bacher et al., 2015; Nuyujukian et al., 2015; Aflalo et al., 2015; Gilja et al., 2015; Jarosiewicz et al., 2015; Wolpaw et al., 1998; Hwang et al., 2012; Spüler et al., 2012; Leuthardt et al., 2004; Taylor et al., 2002; Schalk et al., 2008; Moran, 2010; Brunner et al., 2011; Wang et al., 2013; Townsend and Platsko, 2016; Vansteensel et al., 2016; Nuyujukian et al., 2016; Carmena et al., 2003; Musallam et al., 2004; Santhanam et al., 2006; Hochberg et al., 2006; Ganguly et al., 2011; O’Doherty et al., 2011; Gilja et al., 2012), the performance of human clinical BCI systems is not yet high enough to support widespread adoption by people with physical limitations of speech. Here we report a high-performance intracortical BCI (iBCI) for communication, which was tested by three clinical trial participants with paralysis. The system leveraged advances in decoder design developed in prior pre-clinical and clinical studies (Gilja et al., 2015; Kao et al., 2016; Gilja et al., 2012). For all three participants, performance exceeded previous iBCIs (Bacher et al., 2015; Jarosiewicz et al., 2015) as measured by typing rate (by a factor of 1.4–4.2) and information throughput (by a factor of 2.2–4.0). This high level of performance demonstrates the potential utility of iBCIs as powerful assistive communication devices for people with limited motor function. Clinical Trial No: NCT00912041 DOI: http://dx.doi. org/10.7554/eLife.18554.001
Identification of candidate effector genes of Pratylenchus penetrans.

Pratylenchus penetrans is one of the most important species of root lesion nematodes (RLNs) because of its detrimental and economic impact in a wide range of crops. Similar to other plant-parasitic nematodes (PPNs), P. penetrans harbours a significant number of secreted proteins that play key roles during parasitism. Here, we combined spatially and temporally resolved next-generation sequencing datasets of P. penetrans to select a list of candidate genes aimed at the identification of a panel of effector genes for this species. We determined the spatial expression of transcripts of 22 candidate effectors within the oesophageal glands of P. penetrans by in situ hybridization. These comprised homologues of known effectors of other PPNs with diverse putative functions, as well as novel pioneer effectors specific to RLNs. It is noteworthy that five of the pioneer effectors encode extremely proline-rich proteins. We then combined in situ localization of effectors with available genomic data to identify a non-coding motif enriched in promoter regions of a subset of P. penetrans effectors, and thus a putative hallmark of spatial expression. Expression profiling analyses of a subset of candidate effectors confirmed their expression during plant infection. Our current results provide the most comprehensive panel of effectors found for RLNs. Considering the damage caused by P. penetrans, this information provides valuable data to elucidate the mode of parasitism of this nematode and offers useful suggestions regarding the potential use of P. penetrans-specific target effector genes to control this important pathogen.
Novel 18F-Labeled κ-Opioid Receptor Antagonist as PET Radiotracer: Synthesis and In Vivo Evaluation of 18F-LY2459989 in Nonhuman Primates

The κ-opioid receptor (KOR) has been implicated in depression, addictions, and other central nervous system disorders and, thus, is an important target for drug development. We previously developed several 11C-labeled PET radiotracers for KOR imaging in humans. Here we report the synthesis and evaluation of 18F-LY2459989 as the first 18F-labeled KOR antagonist radiotracer in nonhuman primates and its comparison with 11C-LY2459989. Methods: The novel radioligand 18F-LY2459989 was synthesized by 18F displacement of a nitro group or an iodonium ylide. PET scans in rhesus monkeys were obtained on a small-animal scanner to assess the pharmacokinetic and in vivo binding properties of the ligand. Metabolite-corrected arterial activity curves were measured and used as input functions in the analysis of brain time–activity curves and the calculation of binding parameters. Results: With the iodonium ylide precursor, 18F-LY2459989 was prepared at high radiochemical yield (36% ± 7% [mean ± SD]), radiochemical purity (>99%), and mean molar activity (1,175 GBq/μmol; n = 6). In monkeys, 18F-LY2459989 was metabolized at a moderate rate, with a parent fraction of approximately 35% at 30 min after injection. Fast and reversible kinetics were observed, with a regional peak uptake time of less than 20 min. Pretreatment with the selective KOR antagonist LY2456302 (0.1 mg/kg) decreased the activity level in regions with high levels of binding to that in the cerebellum, thus demonstrating the binding specificity and selectivity of 18F-LY2459989 in vivo. Regional time–activity curves were well fitted by the multilinear analysis 1 kinetic model to derive reliable estimates of regional distribution volumes. With the cerebellum as the reference region, regional binding potentials were calculated and ranked as follows: cingulate cortex > insula > caudate/putamen > frontal cortex > temporal cortex > thalamus, consistent with the reported KOR distribution in the monkey brain. Conclusion: The evaluation of 18F-LY2459989 in nonhuman primates demonstrated many attractive imaging properties: fast tissue kinetics, specific and selective binding to the KOR, and high specific binding signals. A side-by-side comparison of 18F-LY2459989 and 11C-LY2459989 indicated similar kinetic and binding profiles for the 2 radiotracers. Taken together, the results indicated that 18F-LY2459989 appears to be an excellent PET radiotracer for the imaging and quantification of the KOR in vivo.
Strength and Durability assessment of binary blended Self-Compacting Concrete replacing partial sand with electronic plastic waste

107 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: D2881028419/19©BEIESP Abstract: This work is aimed to examine the strength and durability properties of Self-Compacting Concrete (SCC) replacing fine aggregate partially by volume with e-waste High impact polystyrene (HIPS) granules. In addition, Cement is replaced with fly ash in the optimized binder content of 497 kg/m3 using 0.36 water-to-binder ratio in all SCC mixtures. Compressive strength is studied at 28 and 90 days curing age. Durability tests of SCC such as water absorption and sorptivity are investigated at the age of 28 and 90 days with varying fine aggregate replacement up to 40% at an interval of 10%. The increase in HIPS up to 30% replacement shows a linear declination in the reported values of water absorption, and sorptivity tests at all curing periods. Reduction of compressive strength is minimal at all ages with an increment in the volume of HIPS replacement up to 30%. For SCC mixtures, all test values are within the permissible limits. Different particle sizes of aggregates in SCC achieved continuous gradation. In addition, fly ash contributed its effort acting as filler and holds good bonding at interfacial transition zone. Hence, the reduced porosity helps in improvement of durability properties. This investigation is aided in identifying the excellent durability properties with the both cement and sand replacement by fly ash and e-waste aggregate respectively. Replacing e-waste HIPS as fine aggregate in SCC serves as an eco-friendly multipurpose solution. It compensates the disposal problem, conserves natural resources, and reduces energy, emissions, dead load and cost of concrete production.
μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching

NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose performance and memory footprint may vary considerably, depending on the layer dimensions. When an algorithm is automatically selected by cuDNN, the decision is performed on a per-layer basis, and thus it often resorts to slower algorithms that fit the workspace size constraints. We present {\mu}-cuDNN, a transparent wrapper library for cuDNN, which divides layers' mini-batch computation into several micro-batches. Based on Dynamic Programming and Integer Linear Programming, {\mu}-cuDNN enables faster algorithms by decreasing the workspace requirements. At the same time, {\mu}-cuDNN keeps the computational semantics unchanged, so that it decouples statistical efficiency from the hardware efficiency safely. We demonstrate the effectiveness of {\mu}-cuDNN over two frameworks, Caffe and TensorFlow, achieving speedups of 1.63x for AlexNet and 1.21x for ResNet-18 on P100-SXM2 GPU. These results indicate that using micro-batches can seamlessly increase the performance of deep learning, while maintaining the same memory footprint.
Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution

In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important. However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains. This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI. Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic. Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses. First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network. Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. Third, the angular difference between representations are minimized in order to reduce the spectral distortion. We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.
Comparison of Four Pancreatic Islet Implantation Sites

Although the liver is the most common site for pancreatic islet transplantation, it is not optimal. We compared kidney, liver, muscle, and omentum as transplantation sites with regard to operative feasibility, and the efficiency of implantation and glycemic control. Islets from C57BL/6 mice were transplanted into diabetic syngeneic recipients. The mean operative time and mortality were measured to assess feasibility. To assess implantation efficiency, the marginal mass required to cure diabetes and the mean time taken to achieve normoglycemia were measured. A glucose tolerance test was performed to assess glycemic control efficiency. The data are listed in the order of the kidney, liver, muscle, and omentum, respectively. The mean mortality rate was 6.7, 20.0, 7.1, and 12.5%; the mean operative time was 10.2, 27.4, 11.2, and 19.8 min; the marginal islet mass was 100, 600, 600, and 200 islet equivalence units and the mean time to reach euglycemia was 3.0, 15.1, 26.6, and 13.9 days. The glucose kinetics of omental pouch islets was the most similar to controls. Thus, a strategic approach is required for deciding on the best transplantation recipient sites after considering donor sources and islet volume. Alternatives can be chosen based on safety or efficacy.
Vicarious PTG after Fireworks Trauma

Studies about fireworks-related trauma are scarce. Research on other traumas indicates not only the negative reactions and consequences, but also some important positive experiences. This falls within the remit of post-traumatic growth. More complicated may be the possibility of growth through trauma experienced by another person. Vicarious posttraumatic growth refers to positive changes from vicarious or secondary traumatic exposure. In this study, we looked at the trauma experiences by relatives of victims of fireworks’ explosions in Malta, and the potential growth that may have ensued. Method: By using Interpretative Phenomenological Analysis, we interviewed 8 individuals who met the inclusion criteria. Analysis of data was guided by the Smith method (1999). Results: Key themes highlighted in this study focused on participants’ appreciation of the present, acceptance of reality (others & events), and spiritual growth (sense of relatedness with a God and with others). These themes conform to Tedeschi and Calhoun theory of post-traumatic growth (2004).