Miracle-dz/UniFAR-FTUs huggingface dataset:
Training Neural Audio Classifiers with Few Data

We investigate supervised learning strategies that improve the training of neural network audio classifiers on small annotated collections. In particular, we study whether (i) a naive regularization of the solution space, (ii) prototypical networks, (iii) transfer learning, or (iv) their combination, can foster deep learning models to better leverage a small amount of training examples. To this end, we evaluate (i–iv) for the tasks of acoustic event recognition and acoustic scene classification, considering from 1 to 100 labeled examples per class. Results indicate that transfer learning is a powerful strategy in such scenarios, but prototypical networks show promising results when one does not count with external or validation data.
A Hybrid Flow for Multiple Sequence Alignment with a BLASTn Based Pairwise Alignment Processor

In this paper, we design a special-purpose processor for pairwise alignment and propose to integrate this design into a Multiple Sequence Alignment (MSA) flow. The processor is based on a modified algorithm of Banded Two-Hit Basic Local Alignment Search Tool for Nucleotides (MB2-BLASTn). In the new hybrid MSA flow, our MB2-BLASTn processor is used to find pairwise alignment first. Then the results are sent to CPUs for tree-building and progressive alignment. Since the pairwise alignment takes about 90% of total computing time in the original software flow, 100X speedup achieved by MB2-BLASTn can greatly improve the speed of MSA.
Gamification of education and learning: A review of empirical literature

: Gamification has become one of the most notable technological developments for human engagement. Therefore, it is not surprising that gamification has especially been addressed and implemented in the realm of education where supporting and retaining engagement is a constant challenge. However, while the volume of research on the topic has increased, synthesizing the consequent knowledge has remained modest and narrow. Therefore, in this literature review we catalogue 128 empirical research papers in the field of gamification of education and learning. The results indicate that gamification in education and learning most commonly utilizes affordances signaling achievement and progression, while social and immersion-oriented affordances are much less common; the outcomes examined in the studies are mainly focused on quantifiable performance metrics; and the results reported in the reviewed studies are strongly positively oriented. The findings imply that future research on gamification in education should increasingly put emphasis on varying the affordances in the implementations and the pursued goals of the gamification solutions. We encourage also increased attention on contextual factors of the solutions as well as on study designs in future research endeavors.
Proposal, analysis and demonstration of Analog/Digital-mixed Neural Networks based on memristive device arrays

Artificial neural network (NN) circuits are proposed and analyzed from the viewpoints of flexibility and robustness based on memristive devices. The typical 3-layered fully-connected NN is a primitive unit of Deep Learning (DL) and plays a key role in determining DL performance. We propose three types of NN structure at a circuit level — fully-digital, analog/digital mixed, fully-analog — focusing on digital or analog calculations such as a multiplier accumulator, and examine their figures of merit. One key property of the analog/digital-mixed structure is demonstrated on reconfigurable analog ICs. Important aspects regarding neuromorphic computing are highlighted in our experiments on spike-timing-dependent plasticity on memristive devices and non-linear characteristics.
Enabling Resilient Voltage-Controlled MeRAM Using Write Assist Techniques

Reliability concerns arise in nonvolatile magnetoelectric random access memory (MeRAM) due to continuously nanotechnology scaling down and CMOS-magnetic hybrid integration. The primary objective of this work is to investigate failure mitigation in voltage-controlled magnetic anisotropy-magnetic tunnel junction (VCMA-MTJ) based 1T-1MTJ MeRAM bit-cell, by using MTJ compact model and 28nm fully depleted silicon on insulator (FD-SOI) process design-kit. A comprehensive reliability study is performed considering process variation and aging degradations, including hot carrier injection (HCI), bias temperature instability (BTI), soft breakdown (SBD) and radiation effect. Write assist techniques are proposed to ensure failure resilient MeRAM design. Bit line (BL) boost and negative source line (SL) methods show high efficiency in writing latency improvement and failure mitigation.
Urbanization, environment and pharmaceuticals: advancing comparative physiology, pharmacology and toxicology

Pharmaceuticals are indicators of a globally urbanizing water cycle. Unique physicochemical properties and biological activities of medicines diverge from traditional environmental contaminants, and thus challenge historical assessment approaches. Studies to understand environmental risks of pharmaceuticals benefit from mammalian pharmacology and toxicology data, and reciprocally advance basic and applied comparative efforts.
A Novel Convolution Computing Paradigm Based on NOR Flash Array with High Computing Speed and Energy Efficient

A novel convolution computing paradigm based on the NOR Flash Array is proposed. Significant improvements both in computing speed and energy consumption are achieved compared to CMOS-based logic computing paradigms. Regarding to the feature extraction task from a 256×256 image, the computing speed of 3.9×104 frame per second (fps) and the energy consumption of 0.057nJ/pixel are achieved using the proposed computing paradigm.
Phase II Trial

A lifetime of steady schooling alphabet-rote, number-recital, history date, geography location, shoetie, jumprope, bikeride, slowdance, how we are born, grow, age, die hasn't readied me for the cold synopsis of your Consent Form. A lifetime of educated modesty bedroom door closing, body covering, segregating girls from boys, separate amenities for men and women, learned euphemisms for my body's functions hasn't readied me for your free inspection of my daily portapotti. A lifetime of careless risk-taking climbing oaktrees, diving rockpools, homeruns, hardballs, hookshots, headlocks, driving dragsters, shooting the tube, rash rollercoaster thrillseeking hasn't readied me for the chill reality of this life-challenge. A lifetime of practicing precaution sheltering from harm, not overdoing, running in the middle of the pack, getting by with least amount of effort, okay grades without distinction hasn't readied me for your accolades for this my hero's role.
Dynamic salience processing in paraventricular thalamus gates associative learning

A close view of the paraventricular thalamus The paraventricular thalamus is a relay station connecting brainstem and hypothalamic signals that represent internal states with the limbic forebrain that performs associative functions in emotional contexts. Zhu et al. found that paraventricular thalamic neurons represent multiple salient features of sensory stimuli, including reward, aversiveness, novelty, and surprise. The nucleus thus provides context-dependent salience encoding. The thalamus gates sensory information and contributes to the sleep-wake cycle through its interactions with the cerebral cortex. Ren et al. recorded from neurons in the paraventricular thalamus and observed that both population and single-neuron activity were tightly coupled with wakefulness. Science, this issue p. 423, p. 429 A brain region tracks context-dependent changes of the behavioral importance of a stimulus and controls associative learning. The salience of behaviorally relevant stimuli is dynamic and influenced by internal state and external environment. Monitoring such changes is critical for effective learning and flexible behavior, but the neuronal substrate for tracking the dynamics of stimulus salience is obscure. We found that neurons in the paraventricular thalamus (PVT) are robustly activated by a variety of behaviorally relevant events, including novel (“unfamiliar”) stimuli, reinforcing stimuli and their predicting cues, as well as omission of the expected reward. PVT responses are scaled with stimulus intensity and modulated by changes in homeostatic state or behavioral context. Inhibition of the PVT responses suppresses appetitive or aversive associative learning and reward extinction. Our findings demonstrate that the PVT gates associative learning by providing a dynamic representation of stimulus salience.
Effects of a linked data-based annotation approach on students’ learning achievement and cognitive load

ABSTRACT In this study, a linked data-based annotation approach is proposed. A learning system has been developed based on the approach by providing an annotating function, a linked data enrichment function, a sharing function and faceted search function. To evaluate the effectiveness of this innovative approach, an experiment was carried out in which two classes of students participated. The first class served as the experimental group, in which the students learned with the proposed approach, and the second class served as the control group, in which the students learned with the conventional annotation approach. The experiment results show that the experimental group significantly outperformed the control group. Moreover, the cognitive load of the students in the experimental group was significantly lower than the ones in the control group. This implies that the linked data-based annotation approach not only reduced the students’ cognitive load, but also improved their learning achievement.
Risk-Averse Two-Stage Stochastic Program with Distributional Ambiguity

In this paper, we develop a risk-averse two-stage stochastic program (RTSP) that explicitly incorporates the distributional ambiguity covering both discrete and continuous distributions. We formulate RTSP from the perspective of distributional robustness by hedging against the worst-case distribution within an ambiguity set and considering the corresponding expected total cost. In particular, we derive an equivalent reformulation for RTSP that indicates that each worst-case expectation over an L1-norm-based ambiguity set reduces to a convex combination of a conditional value-at-risk and an essential supremum. Our reformulation explicitly shows how additional data can help reduce the conservatism of the problem from the traditional two-stage robust optimization to the traditional risk-neutral two-stage stochastic program (TSP). Accordingly, we develop solution algorithms for the reformulations of RTSP based on the sample average approximation method. We also extend the studies to ambiguity sets based on L∞-.
Defocus and Motion Blur Detection with Deep Contextual Features

We propose a novel approach for detecting two kinds of partial blur, defocus and motion blur, by training a deep convolutional neural network. Existing blur detection methods concentrate on designing low‐level features, but those features have difficulty in detecting blur in homogeneous regions without enough textures or edges. To handle such regions, we propose a deep encoder‐decoder network with long residual skip‐connections and multi‐scale reconstruction loss functions to exploit high‐level contextual features as well as low‐level structural features. Another difficulty in partial blur detection is that there are no available datasets with images having both defocus and motion blur together, as most existing approaches concentrate only on either defocus or motion blur. To resolve this issue, we construct a synthetic dataset that consists of complex scenes with both types of blur. Experimental results show that our approach effectively detects and classifies blur, outperforming other state‐of‐the‐art methods. Our method can be used for various applications, such as photo editing, blur magnification, and deblurring.
Evaluation of the marginal and internal fit of a single crown fabricated based on a three-dimensional printed model

PURPOSE To evaluate the fit of a crown produced based on a 3D printed model and to investigate its clinical applicability. MATERIALS AND METHODS A master die was fabricated with epoxy. Stone dies were fabricated from conventional impressions (Conventional stone die group: CS, n=10). Digital virtual dies were fabricated by making digital impressions (Digital Virtual die group: VD, n=10). 3D data obtained from the digital impression was used to fabricate 3D printed models (DLP die group: DD, n=10, PolyJet die group: PD, n=10). A total of 40 crowns were fabricated with a milling machine, based on CS, VD, DD and PD. The inner surface of all crowns was superimposed with the master die files by the “Best-fit alignment” method using the analysis software. One-way and 2-way ANOVA were performed to identify significant differences among the groups and areas and their interactive effects (α=.05). Tukey's HSD was used for post-hoc analysis. RESULTS One-way ANOVA results revealed a significantly higher RMS value in the 3D printed models (DD and PD) than in the CS and DV. The RMS values of PD were the largest among the four groups. Statistically significant differences among groups (P<.001) and between areas (P<.001) were further revealed by 2-way ANOVA. CONCLUSION Although the fit of crowns fabricated based on the 3D printed models (DD and PD) was inferior to that of crowns prepared with CS and DV, the values of all four groups were within the clinically acceptable range (<120 µm).
Controlling Stroke Size in Fast Style Transfer with Recurrent Convolutional Neural Network

Controlling stroke size in Fast Style Transfer remains a difficult task. So far, only a few attempts have been made towards it, and they still exhibit several deficiencies regarding efficiency, flexibility, and diversity. In this paper, we aim to tackle these problems and propose a recurrent convolutional neural subnetwork, which we call recurrent stroke‐pyramid, to control the stroke size in Fast Style Transfer. Compared to the state‐of‐the‐art methods, our method not only achieves competitive results with much fewer parameters but provides more flexibility and efficiency for generalizing to unseen larger stroke size and being able to produce a wide range of stroke sizes with only one residual unit. We further embed the recurrent stroke‐pyramid into the Multi‐Styles and the Arbitrary‐Style models, achieving both style and stroke‐size control in an entirely feed‐forward manner with two novel run‐time control strategies.
Reactive caching for composed services: polling at the speed of push

Sometimes, service clients repeat requests in a polling loop in order to refresh their view. However, such polling may be slow to pick up changes, or may increase the load unacceptably, in particular for composed services that disperse over many components. We present an alternative reactive polling API and reactive caching algorithm that combines the conceptual simplicity of polling with the efficiency of push-based change propagation. A reactive cache contains a summary of a distributed read-only operation and maintains a connection to its dependencies so changes can be propagated automatically. We first formalize the setting using an abstract calculus for composed services. Then we present a fault-tolerant distributed algorithm for reactive caching that guarantees eventual consistency. Finally, we implement and evaluate our solution by extending the Orleans actor framework, and perform experiments on two benchmarks in a distributed cloud deployment. The results show that our solution provides superior performance compared to polling, at a latency that comes close to hand-written change notifications.
Treatment of Refractory Thrombotic Thrombocytopenic Purpura

What is the problem and what is known about it so far? Thrombotic thrombocytopenic purpura (TTP) is an uncommon but serious disease that can be fatal. Patients with TTP have low numbers of platelets (thrombocytopenia), abnormal red cells, and a tendency to develop tiny clots (thrombi) in small blood vessels. In some adults, TTP is caused by an immune reaction. Their bodies make an antibody that inactivates an important protein (ADAMTS13) whose function is to prevent platelets from sticking together and plugging blood vessels. Patients with TTP may have easy bruising, rashes with many little dots or large purple patches (purpura), fever, belly pain, thrombi in the brain or kidneys, or bleeding due to few platelets. Treatments include plasma exchanges to help rid the body of antibodies, removal of the spleen to reduce excess destruction of platelets, and drugs that suppress autoimmune processes. However, some patients have refractory TTP despite standard treatments. Whether intensive immunotherapy aimed at markedly suppressing antibodies against ADAMTS13 will help them is not known. Why did the researchers do this particular study? To see whether intensive immunotherapy works for refractory TTP. Who was studied? A 42-year-old woman with chronic relapsing TTP despite many treatments. How was the study done? The patient was treated with traditional therapies, including plasma exchange, removal of the spleen, and some immunotherapy (prednisone, vincristine, cyclosporine) for 19 months. She had many relapses despite treatment. Over the following year, the researchers twice treated relapses with powerful intensive immunotherapy administered through the patient's veins (rituximab or rituximab plus cyclophosphamide). The researchers repeatedly measured levels of ADAMTS13 and tested for inhibitors of ADAMTS13. What did the researchers find? After the second course of intensive immunotherapy, the patient had no symptoms of TTP and had normal numbers of platelets. Levels of ADAMTS13, which had been low to nonexistent before intensive immunotherapy, normalized. An IgG inhibitor, which had been present before intensive immunotherapy, was no longer detectable. The patient has not needed any additional therapy for 6 months. What were the limitations of the study? The study involved one patient and did not directly compare benefits of intensive immunotherapy with standard therapy. What are the implications of the study? Intensive immunotherapy may be a promising therapy for adults with refractory TTP. However, we need observations in many more patients and results from controlled studies that directly compare it with other therapies before we routinely recommend it.
Generative Adversarial Image Super‐Resolution Through Deep Dense Skip Connections

Recently, image super‐resolution works based on Convolutional Neural Networks (CNNs) and Generative Adversarial Nets (GANs) have shown promising performance. However, these methods tend to generate blurry and over‐smoothed super‐resolved (SR) images, due to the incomplete loss function and powerless architectures of networks. In this paper, a novel generative adversarial image super‐resolution through deep dense skip connections (GSR‐DDNet), is proposed to solve the above‐mentioned problems. It aims to take advantage of GAN's ability of modeling data distributions, so that GSR‐DDNet can select informative feature representation and model the mapping across the low‐quality and high‐quality images in an adversarial way. The pipeline of the proposed method consists of three main components: 1) The generator of a novel dense skip connection network with the deep structure for learning robust mapping function is proposed to generate SR images from low‐resolution images; 2) The feature extraction network based on VGG‐19 is adopted to capture high frequency feature maps for content loss; and 3) The discriminator with Wasserstein distance is adopted to identify the overall style of SR and ground‐truth images. Experiments conducted on four publicly available datasets demonstrate the superiority against the state‐of‐the‐art methods.
Epidemiological and imaging features that can affect the detection of ureterolithiasis on ultrasound

Objective To identify, in patients with clinical suspicion of ureterolithiasis, epidemiological and imaging features that affect calculus detection on ultrasound, as well as to compare ultrasound with multidetector computed tomography (MDCT). Materials and Methods We searched our database for patients who underwent ultrasound, followed by MDCT (if the ultrasound was negative), for suspected ureterolithiasis in an emergency setting. Patients were divided into three groups: positive ultrasound (US+); negative ultrasound/positive MDCT (US−/MDCT+); and negative ultrasound/negative MDCT (US−/MDCT−). We evaluated age, gender, ureterolithiasis laterality, location of the calculus within the ureter, body mass index, calculus diameter, and calculus attenuation on MDCT. Results Of a total of 292 cases of suspected ureterolithiasis, 155 (53.1%) were in the US+ group, 46 (15.7%) were in the US−/MDCT+ group, and 91 (31.2%) were in the US−/MDCT− group. There were no significant differences among the groups in terms of age, gender, ureterolithiasis laterality, and mean MDCT attenuation values. Distal ureterolithiasis was most common in the US+ group, and calculi at other ureteral locations were more common in the US−/MDCT+ group. The mean body mass index was significantly higher in the US−/MDCT+ group than in the US+ group, and the mean calculus diameter was significantly greater in the US+ group than in the US−/MDCT+ group. Conclusion A high body mass index, large calculus diameter, and calculus location in the distal third of the ureter are the major factors favoring ureterolithiasis detection on ultrasound.
Systematic Reviews: Synthesis of Best Evidence for Health Care Decisions

Systematic Reviews: Synthesis of Best Evidence for Health Care Decisions; Mulrow C, Cook D; eds. 117 pages. Philadelphia: American Coll of Physicians; 1998. $27.00. ISBN 0943126665. Order phone 800-523-1546, ext. 2600. Field of medicine: Any field that involves patient care, clinical decisions, or policymaking. Format: Softcover book. Audience: Health care providers, teachers, researchers, and policymakers. Although some of its content is introductory, the book will be useful to the seasoned clinician and teacher. Those new to clinical decision making and those in training will find it useful as a thorough primer. Purpose: To introduce and expand on the concept of the modern review of overwhelming amounts of statistical information and to systematize an approach to clinical decision making based on efficient and balanced reviews of the current literature. Content: The book is divided into 10 chapters, all of which address the central theme of integrating vast amounts of available research information into decision making. The reader is led from the initial definition of the systematic review and the use of computerized databases to a statistical primer that focuses on a practical approach to interpreting and using search results. The middle sections discuss the application of the systematic review to clinical education, policymaking, and construction of practice guidelines. The final chapters address more complex issues of qualitative appraisal of information, quantitative synthesis, and integration of heterogeneous evidence. Highlights: The book is readable and well-written. Each chapter is well referenced and suitably grounded in clinical vignettes that illustrate the use of the information and techniques addressed. In addition, each chapter contains a summary of key points and a glossary of important statistical terms. These features help make the book accessible to the novice while challenging the more sophisticated reader. Limitations: The book has no major limitations. Related reading: No other major works address this type of content in a similar fashion. Reviewer: David A. Horowitz, MD, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.