Miracle-dz/UniFAR-FTUs huggingface dataset:
Noisy Natural Gradient as Variational Inference

Variational Bayesian neural nets combine the flexibility of deep learning with Bayesian uncertainty estimation. Unfortunately, there is a tradeoff between cheap but simple variational families (e.g.~fully factorized) or expensive and complicated inference procedures. We show that natural gradient ascent with adaptive weight noise implicitly fits a variational posterior to maximize the evidence lower bound (ELBO). This insight allows us to train full-covariance, fully factorized, or matrix-variate Gaussian variational posteriors using noisy versions of natural gradient, Adam, and K-FAC, respectively, making it possible to scale up to modern-size ConvNets. On standard regression benchmarks, our noisy K-FAC algorithm makes better predictions and matches Hamiltonian Monte Carlo's predictive variances better than existing methods. Its improved uncertainty estimates lead to more efficient exploration in active learning, and intrinsic motivation for reinforcement learning.
Improving Recall of In Situ Sequencing by Self-Learned Features and a Graphical Model

Image-based sequencing of mRNA makes it possible to see where in a tissue sample a given gene is active, and thus discern large numbers of different cell types in parallel. This is crucial for gaining a better understanding of tissue development and disease such as cancer. Signals are collected over multiple staining and imaging cycles, and signal density together with noise makes signal decoding challenging. Previous approaches have led to low signal recall in efforts to maintain high sensitivity. We propose an approach where signal candidates are generously included, and true-signal probability at the cycle level is self-learned using a convolutional neural network. Signal candidates and probability predictions are thereafter fed into a graphical model searching for signal candidates across sequencing cycles. The graphical model combines intensity, probability and spatial distance to find optimal paths representing decoded signal sequences. We evaluate our approach in relation to state-of-the-art, and show that we increase recall by $27\%$ at maintained sensitivity. Furthermore, visual examination shows that most of the now correctly resolved signals were previously lost due to high signal density. Thus, the proposed approach has the potential to significantly improve further analysis of spatial statistics in in situ sequencing experiments.
Provenance and Probabilities in Relational Databases

We review the basics of data provenance in relational databases. We describe different provenance formalisms, from Boolean provenance to provenance semirings and beyond, that can be used for a wide variety of purposes, to obtain additional information on the output of a query. We discuss representation systems for data provenance, circuits in particular, with a focus on practical implementation. Finally, we explain how provenance is practically used for probabilistic query evaluation in probabilistic databases.
The effect of thought importance on stress responses: a test of the metacognitive model

Abstract Negative metacognitive beliefs are central determinants of distress in the metacognitive model of psychological vulnerability to stress. The current study tested this assertion in 75 undergraduate students assigned to either experimental (metacognitive belief manipulation) or control (no metacognitive belief manipulation) condition. All participants underwent a fake EEG, where they were told that the EEG would detect negative thoughts. The experimental subjects were informed that if they had a negative thought they may be exposed to a contingent burst of loud noise, while the control condition was told that they may be exposed to a burst of loud noise at random. Participants also underwent the Trier Social Stress Test. The results showed that on physiological measures (skin conductance level) there were no significant differences between groups. However, on self-report measures (positive and negative affect) participants in the experimental condition reported greater levels of negative affect and lower levels of positive affect in response to stress and maintained low positive affect at recovery. The results are consistent with the metacognitive model and suggest that metacognitive beliefs impact on positive and negative affect in reaction to and recovery from stress exposure.
Selenoprotein W redox-regulated Ca2+ channels correlate with selenium deficiency-induced muscles Ca2+ leak

Selenium (Se) deficiency induces Ca2+ leak and calcification in mammal skeletal muscles; however, the exact mechanism is still unclear. In the present study, both Se-deficient chicken muscle models and selenoprotein W (SelW) gene knockdown myoblast and embryo models were used to study the mechanism. The results showed that Se deficiency-induced typical muscular injuries accompanied with Ca2+ leak and oxidative stress (P < 0.05) injured the ultrastructure of the sarcoplasmic reticulum (SR) and mitochondria; decreased the levels of the Ca2+ channels, SERCA, SLC8A, CACNA1S, ORAI1, STIM1, TRPC1, and TRPC3 (P < 0.05); and increased the levels of Ca2+ channel PMCA (P < 0.05). Similarly, SelW knockdown also induced Ca2+ leak from the SR and cytoplasm; increased mitochondrial Ca2+ levels and oxidative stress; injured SR and mitochondrial ultrastructure; decreased levels of SLC8A, CACNA1S, ORA1, TRPC1, and TRPC3; and caused abnormal activities of Ca2+ channels in response to inhibitors in myoblasts and chicken embryos. Thus, both Se deficiency and SelW knockdown induced Ca2+ leak, oxidative stress, and Ca2+ channel reduction. In addition, Ca2+ levels and the expression of the Ca2+ channels, RyR1, SERCA, CACNA1S, TRPC1, and TRPC3 were recovered to normal levels by N-acetyl-L-cysteine (NAC) treatment compared with SelW knockdown cells. Thus, with regard to the decreased Ca2+ channels, SelW knockdown closely correlated Se deficiency with Ca2+ leak in muscles. The redox regulation role of SelW is crucial in Se deficiency-induced Ca2+ leak in muscles.
Adjustment of metro train operation curve for efficiently using regenerative energy

The reuse of the regenerated braking energy has huge potential. This paper mainly focuses on the efficient usage of regenerative energy. When one train is braking, if the regenerative energy isn't absorbed by other trains, the traction supply network voltage will increase, it leads to the regenerative braking failure and the waste of regenerative braking energy. Based on the knowing and analysis of online running trains' operation conditions, the other trains' operation curve will be adjusted correspondingly, to achieve the synchronization of acceleration and braking condition. In order to analyze the adjustment principles and adjustment strategies, the model of traction supply network and the constraint conditions of running train are put forward, then simulation in the simple traction power supply system of an actual line are analyzed to verify the correctness and validity of the methods.
An integrated on-board battery charger with a nine-phase PM machine

A fully integrated on-board battery charger for electrical vehicles (EVs) has been developed recently using a nine-phase machine. All the components used for propulsion are employed in the charging process, no additional components are required, and there is no need for hardware reconfiguration between charging and propulsion modes of operation. The proposed solution can be connected directly to single-phase or three-phase grid to perform charging, so that the expensive off-board charger infrastructure is not needed. The only requirement is to use a nine-phase machine in combination with a nine-phase inverter in the powertrain of the EV. This however inevitably brings in further advantages in the propulsion mode, such as increased fault tolerance and the current subdivision into more phases. The benefits of the topology, originally developed for an induction machine, make it interesting for further investigation. Therefore, the performance of the charger is examined here using a permanent magnet synchronous machine (PMSM). The results show that the charger topology is applicable to other types of synchronous machines and is, even more importantly, independent of the angular spatial shift between the individual three-phase windings of the nine-phase machine's stator. The results are comparable with those obtained using an induction machine and confirm the viability of the solution in conjunction with the PMSM as a propulsion motor.
K-robots clustering of moving sensors using coresets

We present an approach to position k servers (e.g. mobile robots) to provide a service to n independently moving clients; for example, in mobile ad-hoc networking applications where inter-agent distances need to be minimized, connectivity constraints exist between servers, and no a priori knowledge of the clients' motion can be assumed. Our primary contribution is an algorithm to compute and maintain a small representative set, called a kinematic coreset, of the n moving clients.We prove that, in any given moment, the maximum distance between the clients and any set of k servers is approximated by the coreset up to a factor of (1 ± ε), where ε > 0 is an arbitrarily small constant. We prove that both the size of our coreset and its update time is polynomial in k log(n)/ε. Although our optimization problem is NP-hard (i.e., takes time exponential in the number of servers to solve), solving it on the small coreset instead of the original clients results in a tractable controller. The approach is validated in a small scale hardware experiment using robot servers and human clients, and in a large scale numerical simulation using thousands of clients.
Antibiotic exposure in early life and childhood overweight and obesity: A systematic review and meta‐analysis

We conducted a systematic review and meta‐analysis of observational studies investigating the association between antibiotic exposure in infancy and risk of childhood overweight and obesity. Thirteen studies, including a total of 527 504 children, were included in the systematic review and 8 were included in meta‐analyses. Exposure to antibiotics in infancy was associated with an increased odds ratio (OR) of childhood overweight and obesity (OR 1.11, 95% confidence interval [CI] 1.02‐1.20). Whereas exposure to 1 treatment only and exposure between 6 and 24 months were not associated with increased risk of childhood overweight and obesity, exposure to >1 treatment was associated with an OR of 1.24 (95% CI 1.09‐1.43) and exposure within the first 6 months of life was associated with an OR of 1.20 (95% CI 1.04‐1.37). In conclusion, antibiotic exposure in infancy was associated with a slightly increased risk of childhood overweight and obesity, mainly if children were exposed to repeated treatments or treatment within the first 6 months of life. It is unclear whether this association is mediated via direct effects of antibiotics on the gut microbiota.
Supporting the dynamic frequency response in microgrids by means of active loads

Ensuring the stability and security of autonomous microgrids (MG) supplied by renewable energy sources (RES) represents an issue of great importance, which can be tackled from several perspectives. Besides implementing various control techniques, mainly based on energy storage systems, improving the response of RES-based generators and increasing the controllability at the consumption level are among the most discussed techniques to achieve high levels of reliability and robustness for the future MG. On this topic, the current paper presents a control solution for active loads (AL), which can be developed based on non-critical loads, in order to provide dynamic support during fast frequency variations. The study shows that implementing a frequency controller based on the derivative of the estimated frequency provides an improvement of the MG transitory behaviour, but due to the difficulty of accurately measuring the MG frequency, the method can also worsen the AL input current distortion. Therefore, the paper presents an original harmonic compensation technique, which can mitigate the current distortions in certain limits. The presented results show that the dynamic frequency response is improved by the action of AL, the feature being optimally used along with the proposed harmonic compensation technique.
DeepMap+: Recognizing High-Level Indoor Semantics Using Virtual Features and Samples Based on a Multi-Length Window Framework

Existing indoor semantic recognition schemes are mostly capable of discovering patterns through smartphone sensing, but it is hard to recognize rich enough high-level indoor semantics for map enhancement. In this work we present DeepMap+, an automatical inference system for recognizing high-level indoor semantics using complex human activities with wrist-worn sensing. DeepMap+ is the first deep computation system using deep learning (DL) based on a multi-length window framework to enrich the data source. Furthermore, we propose novel methods of increasing virtual features and virtual samples for DeepMap+ to better discover hidden patterns of complex hand gestures. We have performed 23 high-level indoor semantics (including public facilities and functional zones) and collected wrist-worn data at a Wal-Mart supermarket. The experimental results show that our proposed methods can effectively improve the classification accuracy.
Modeling topic and community structure in social tagging: The TTR-LDA-Community model

The presence of social networks in complex systems has made networks and community structure a focal point of study in many domains. Previous studies have focused on the structural emergence and growth of communities and on the topics displayed within the network. However, few scholars have closely examined the relationship between the thematic and structural properties of networks. Therefore, this article proposes the Tagger Tag Resource-Latent Dirichlet Allocation-Community model (TTR-LDA-Community model), which combines the Latent Dirichlet Allocation (LDA) model with the Girvan-Newman community detection algorithm through an inference mechanism. Using social tagging data from Delicious, this article demonstrates the clustering of active taggers into communities, the topic distributions within communities, and the ranking of taggers, tags, and resources within these communities. The data analysis evaluates patterns in community structure and topical affiliations diachronically. The article evaluates the effectiveness of community detection and the inference mechanism embedded in the model and finds that the TTR-LDA-Community model outperforms other traditional models in tag prediction. This has implications for scholars in domains interested in community detection, profiling, and recommender systems. © 2011 Wiley Periodicals, Inc.
How to Square Floats Accurately and Efficiently on the ST231 Integer Processor

We consider the problem of computing IEEE floating-point squares by means of integer arithmetic. We show how to exploit the specific properties of squaring in order to design and implement algorithms that have much lower latency than those for general multiplication, while still guaranteeing correct rounding. Our algorithms are parameterized by the floating-point format, aim at high instruction-level parallelism (ILP) exposure, and cover all rounding modes. We show further that their C implementation for the binary32 format yields efficient codes for targets like the ST231 VLIW integer processor from ST Microelectronics, with a latency at least 1.75x smaller than that of general multiplication in the same context.
TFII-I and AP2α Co-Occupy the Promoters of Key Regulatory Genes Associated with Craniofacial Development

Objectives: The aim of this study is to define the candidate target genes for TFII-I and AP2α regulation in neural crest progenitor cells. Design: The GTF2I and GTF2IRD1 genes encoding the TFII-I family of transcription factors are prime candidates for the Williams-Beuren syndrome, a complex multisystem disorder characterized by craniofacial, skeletal, and neurocognitive deficiencies. AP2α, a product of the TFAP2A gene, is a master regulator of neural crest cell lineage. Mutations in TFAP2A cause branchio-oculo-facial syndrome characterized by dysmorphic facial features and orofacial clefts. In this study, we examined the genome-wide promoter occupancy of TFII-I and AP2α in neural crest progenitor cells derived from in vitro-differentiated human embryonic stem cells. Results: Our study revealed that TFII-I and AP2α co-occupy a selective set of genes that control the specification of neural crest cells. Conclusions: The data suggest that TFII-I and AP2α may coordinately control the expression of genes encoding chromatin-modifying proteins, epigenetic enzymes, transcription factors, and signaling proteins.
PBGen: Partial Binarization of Deconvolution-Based Generators for Edge Intelligence

This work explores the binarization of the deconvolution-based generator in a GAN for memory saving and speedup of image construction. Our study suggests that different from convolutional neural networks (including the discriminator) where all layers can be binarized, only some of the layers in the generator can be binarized without significant performance loss. Supported by theoretical analysis and verified by experiments, a direct metric based on the dimension of deconvolution operations is established, which can be used to quickly decide which layers in the generator can be binarized. Our results also indicate that both the generator and the discriminator should be binarized simultaneously for balanced competition and better performance. Experimental results based on CelebA suggest that directly applying state-of-the-art binarization techniques to all the layers of the generator will lead to 2.83$\times$ performance loss measured by sliced Wasserstein distance compared with the original generator, while applying them to selected layers only can yield up to 25.81$\times$ saving in memory consumption, and 1.96$\times$ and 1.32$\times$ speedup in inference and training respectively with little performance loss.
Jasmonoyl-L-Tryptophan Disrupts IAA Activity through the AUX1 Auxin Permease

Amide-linked conjugates between tryptophan (Trp) and jasmonic (JA) or indole-3-acetic (IAA) acids interfered with gravitropism and other auxin-dependent activities in Arabidopsis, but the mechanism was unclear. To identify structural features necessary for activity several additional Trp conjugates were synthesized. The phenylacetic acid (PAA) conjugate was active, while several others were not. Common features of active conjugates is that they have ring structures that are linked to Trp through an acetic acid side chain, while longer or shorter linkages are inactive or less active. A dominant mutant, called tryptophan conjugate response1-D that is insensitive to JA-Trp, but still sensitive to other active conjugates, was identified and the defect was found to be a substitution of Asn for Asp456 in the C-terminal domain of the IAA cellular permease AUX1. Mutant seedling primary root growth in the absence of added conjugate was 15% less than WT, but otherwise plant phenotype appeared normal. These results suggest that JA-Trp disrupts AUX1 activity, but that endogenous JA-Trp has only a minor role in regulating plant growth. In contrast with IAA- and JA-Trp, which are present at <2 pmole g-1 FW, PAA-Trp was found at about 30 pmole g-1 FW. The latter, or other undiscovered Trp conjugates, may still have important endogenous roles, possibly helping to coordinate other pathways with auxin response.
A conditional estimating equation approach for recurrent event data with additional longitudinal information

Recurrent event data are quite common in biomedical and epidemiological studies. A significant portion of these data also contain additional longitudinal information on surrogate markers. Previous studies have shown that popular methods using a Cox model with longitudinal outcomes as time‐dependent covariates may lead to biased results, especially when longitudinal outcomes are measured with error. Hence, it is important to incorporate longitudinal information into the analysis properly. To achieve this, we model the correlation between longitudinal and recurrent event processes using latent random effect terms. We then propose a two‐stage conditional estimating equation approach to model the rate function of recurrent event process conditioned on the observed longitudinal information. The performance of our proposed approach is evaluated through simulation. We also apply the approach to analyze cocaine addiction data collected by the University of Connecticut Health Center. The data include recurrent event information on cocaine relapse and longitudinal cocaine craving scores. Copyright © 2016 John Wiley & Sons, Ltd.
Hematological and liver toxicity of anti-tuberculosis drugs

Introduction Tuberculosis (TB) is a major global health problem, and anti-tuberculosis drugs can cause severe adverse reactions. The aim of this study was to determine hematological and biochemical changes and associated risk factors in smear positive pulmonary tuberculosis patients undergoing treatment with standard protocols. Methods In a descriptive study, a total of 40 tuberculosis patients aged between 15–60 years were collected from hospitals in Khuzestan Province (Iran) from March 2013 to March 2014. The patients were treated with drugs (isoniazid, rifampicin, ethambutol, and pyrazinamide) during the initial two months, followed by isoniazid and rifampicin for the next four to six months. Activities of liver enzymes (ALT, AST, and ALP) and hematological parameters were recorded before and after treatment. Data were analyzed using paired samples t-test and Wilcoxon test by SPSS 16. Results After using drug treatments, hematological parameters (RBC, Hb, HCT, MCV, MCH, and MCHC), except platelet count, were changed significantly (p ≤ 0.001). Liver enzyme activities (ALT, AST, and ALP) were decreased significantly (p ≤ 0.001) after treatment. Conclusion In this study, changes of hematological and biochemical parameters have been observed in patients with pulmonary tuberculosis. It can be concluded that the anti-tuberculosis treatment is associated with changes of hematological parameters and liver enzymes.
Experimental Analysis of Deep Echo State Networks for Ambient Assisted Living

The Reservoir Computing (RC) paradigm represents a stateof-the-art methodology for efficient building of recurrent neural networks, which in the last years has proved effective in learning real-world temporal tasks from streams of sensorial data in the Ambient Assisted Living (AAL) domain. Recently, the study of RC networks has been extended to the case of deep architectures, with the introduction of the deep Echo State Network (DeepESN) model. Featured by a layered composition of recurrent units, DeepESNs are inherently able to develop a hierarchically structured representation of temporal information, at the same time preserving the RC characterization of training efficiency. In this paper, we discuss the introduction of the DeepESN approach in the field of AAL. To this aim, we perform a comparative experimental analysis on two real-world benchmark datasets related to inferring the user’s behavior from data streams gathered from the nodes of a wireless sensor network. Results show that DeepESNs outperform standard RC networks with shallow architecture, suggesting a multiple-time scales nature of the involved temporal data and pointing out the great potentiality of the proposed approach in the AAL field.