Miracle-dz/UniFAR-FTUs huggingface dataset:
Three Mechanisms of Weight Decay Regularization

Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of $L_2$ regularization. Literal weight decay has been shown to outperform $L_2$ regularization for optimizers for which they differ. We empirically investigate weight decay for three optimization algorithms (SGD, Adam, and K-FAC) and a variety of network architectures. We identify three distinct mechanisms by which weight decay exerts a regularization effect, depending on the particular optimization algorithm and architecture: (1) increasing the effective learning rate, (2) approximately regularizing the input-output Jacobian norm, and (3) reducing the effective damping coefficient for second-order optimization. Our results provide insight into how to improve the regularization of neural networks.
Long-acting Insulin in Diabetes Therapy: In Silico Clinical Trials with the UVA/Padova Type 1 Diabetes Simulator*

The University of Virginia /Padova Type 1 Diabetes (TID) simulator has been widely used for testing artificial pancreas controllers, and, recently, novel insulin formulations and glucose sensors. However, a module describing the pharmacokinetics of the new long-acting insulin analogues is not available. The aim of this contribution is to reproduce multiple daily insulin injection (MDI) therapy, with insulin glargine 100 U/mL (Gla-100) as basal insulin, using the TID simulator. This was achieved by developing a model of Gla-100 and by incorporating it into the simulator. The methodology described here can be extended to other insulins, allowing an extensive in silico testing of different long-acting insulin analogues under various settings before starting human trials.
Single-hop Packet Delivery Rate in V2V Safety Communications for Worst-case Highway Scenarios

Ensuring maximum data packet delivery rate is crucial in the vehicle-to-vehicle (V2V) safety (broadcast) communication. The highly dynamic topology of vehicles on the road makes it difficult to appropriately quantify different quality of service parameters e.g. packet delivery rate. High packet delivery rate is difficult to achieve in periodic safety communications with broadcast as the default communication mechanism. A handful of transmission power control and beacon generation control methods have been proposed by researchers to enhance packet delivery in V2V communications. These methods however were lacking in the focus on realistic worst-case scenarios. In this paper, optimal combinations of transmission range and beacon generation interval are presented in the context of different packet delivery rates for worst-case scenarios. As such, the effectiveness of this approach was measured through extensive simulations based on the realistic worst-case scenarios on a highway.
Dynamic Thresholding Mechanisms for IR-Based Filtering in Efficient Source Code Plagiarism Detection

To solve time inefficiency issue, only potential pairs are compared in string-matching-based source code plagiarism detection; wherein potentiality is defined through a fast-yet-order-insensitive similarity measurement (adapted from Information Retrieval) and only pairs which similarity degrees are higher or equal to a particular threshold is selected. Defining such threshold is not a trivial task considering the threshold should lead to high efficiency improvement and low effectiveness reduction (if it is unavoidable). This paper proposes two three holding mechanisms-namely range-based and pair-count-based mechanism-that dynamically tune the threshold based on the distribution of resulted similarity degrees. According to our evaluation, both mechanisms are more practical to be used than manual threshold assignment since they are more proportional to efficiency improvement and effectiveness reduction.
Comparison of in vitro Neuronal Differentiation Capacity Between Mouse Epiblast Stem Cells Derived From Nuclear Transfer and Naturally Fertilized Embryos

Somatic cell nuclear transfer (SCNT) can give rise to fertile adults, but the successful perinatal and postnatal developmental rates are inefficient, including delayed developmental behaviors, and respiratory failure. However, the molecular and cellular mechanisms remain elusive. Mouse epiblast stem cells (mEpiSCs) from E5.5-6.5 epiblasts share defining features with human embryonic stem cells (hESCs), providing a new opportunity to study early mammalian development in vitro. In this study, mEpiSCs were established from naturally fertilized mouse embryos (F-mEpiSCs) and SCNT mouse embryos (NT-mEpiSCs). Also, the in vitro neuronal differentiation capacity of F-mEpiSCs and NT-mEpiSCs was compared. Morphology analysis showed less and smaller neurospheres formation and lower percentage of early neurons generation in NT-mEpiSCs. The immunocytochemical analysis and altered mRNA expression levels of the neuronal markers in differentiated cells further confirmed that neurogenesis was slower in NT-mEpiSCs than in F-mEpiSCs. Moreover, neuronal differentiation capacity was correlated with the basal expression levels of Atox1 and Vinculin but not Brachyury and Otx2, emphasizing that developmental aberrations in neurogenesis were associated with the NT technique but not random variations between clones. This study provided an important in vitro platform using mEpiSCs to study early epigenetic and developmental processes associated with neurogenesis.
Frontostriatal functional connectivity correlates with repetitive behaviour across autism spectrum disorder and obsessive–compulsive disorder

Abstract Background Autism spectrum disorder (ASD) and obsessive–compulsive disorder (OCD) are neurodevelopmental disorders with considerable overlap in terms of their defining symptoms of compulsivity/repetitive behaviour. Little is known about the extent to which ASD and OCD have common versus distinct neural correlates of compulsivity. Previous research points to potentially common dysfunction in frontostriatal connectivity, but direct comparisons in one study are lacking. Here, we assessed frontostriatal resting-state functional connectivity in youth with ASD or OCD, and healthy controls. In addition, we applied a cross-disorder approach to examine whether repetitive behaviour across ASD and OCD has common neural substrates. Methods A sample of 78 children and adolescents aged 8–16 years was used (ASD n = 24; OCD n = 25; healthy controls n = 29), originating from the multicentre study COMPULS. We tested whether diagnostic group, repetitive behaviour (measured with the Repetitive Behavior Scale-Revised) or their interaction was associated with resting-state functional connectivity of striatal seed regions. Results No diagnosis-specific differences were detected. The cross-disorder analysis, on the other hand, showed that increased functional connectivity between the left nucleus accumbens (NAcc) and a cluster in the right premotor cortex/middle frontal gyrus was related to more severe symptoms of repetitive behaviour. Conclusions We demonstrate the fruitfulness of applying a cross-disorder approach to investigate the neural underpinnings of compulsivity/repetitive behaviour, by revealing a shared alteration in functional connectivity in ASD and OCD. We argue that this alteration might reflect aberrant reward or motivational processing of the NAcc with excessive connectivity to the premotor cortex implementing learned action patterns.
An On-Chip Transformer-Based Self-Startup Hybrid SIDITO Converter for Thermoelectric Energy Harvesting

A hybrid single-inductor dual-input-triple-output (SIDITO) converter with an on-chip transformer-based starter for thermoelectric energy harvesting is presented. The on-chip transformer-based LC starter makes the system capable of self-startup at an open-circuit-voltage of 160 mV. With the proposed hybrid SIDITO architecture, additional inductor-sharing power switches are eliminated. An adaptive-on-time control scheme is also proposed to improve the efficiency at low input voltage levels. The chip is fabricated in a 0.18-<inline-formula> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> CMOS process. The measurement results show that the converter can provide an output current range from <inline-formula> <tex-math notation="LaTeX">$1~{\mu }\text{A}$ </tex-math></inline-formula> to 10 mA with a peak efficiency of 83.3%.
Dental pulp stem cells senescence and regenerative potential relationship

Uncomplicated treatments for pulpitis and periodontitis continues to be challenging and regenerative approaches could meet this contingency. Dental pulp stem cells (DPSCs) represent a good candidate for oral recovering therapies. Here, we investigated changes in morphology, proliferation, and in vitro differentiation toward mesenchymal and neuronal phenotypes of human DPSCs harvested from differently aged donors. Aging is a physiologic phenomenon occurring with time that hamper body’s capability to maintain homeostasis also affecting the functional reserve. Cytofluorimetric, immunohistochemical, quantitative reverse‐transcription polymerase chain reaction (qRT‐PCR), and western blot analyses were performed to gain insight for successful regenerative strategies in elderly. We observed a decline in DPSCs proliferation and differentiation potential with age. Interestingly, these cells behaved differently under osteogenic or odontogenic stimuli, showing different age‐related mineralization capabilities. Similarly, neurogenic differentiation decreased with age. In conclusion, our observations represent a valid tool for the development of tailored regenerative strategies in an aging society.
Estimation of compressive tibiofemoral force using over resistance of ankle-foot orthosis on gait

The purpose of this study was to investigate the effect of changing the plantar flexion resistance (PFR) of an ankle-foot orthosis (AFO) on the compressive tibiofemoral force, knee muscle forces, and knee joint angle. We measured and estimated knee flexion angle, knee muscle force, and the compressive tibiofemoral force in healthy adult males. The results showed that the first peak compressive tibiofemoral force, peak knee flexion angle, and peak quadriceps muscle force increased in the strong PFR condition compared with the no-AFO condition. These results suggest that over-PFR caused various knee troubles.
A Meta-Analysis of Malingering Detection Measures for Attention-Deficit/Hyperactivity Disorder

This meta-analysis compares stand-alone and embedded performance and symptom validity tests (PVTs and SVTs) for attention-deficit/hyperactivity disorder (ADHD) malingering detection in college students. Simulation design studies utilizing college student samples were included (k = 11). Analyses consisted of measures designed or previously used for malingering detection. Random-effects models were constructed to provide aggregated weighted effect sizes (Hedges’ g), indicating the difference between genuine ADHD and simulation groups. Overall PVTs (stand-alone and embedded) produced a large effect size (g = 0.84, 95% confidence interval [CI; 0.72, 1.13], p < .001), whereas overall SVTs (stand-alone and embedded) produced a medium-effect size (g = 0.54, 95% CI [0.44, 0.65], p < .001). Stand-alone PVTs (g = 0.98, 95% CI [0.84, 1.12], p < .001) outperformed embedded PVTs (g = 0.66, 95% CI [0.51, 0.80], p < .001). The stand-alone SVT (g = 0.66) and embedded SVTs (g = 0.54, 95% CI [0.43, 0.65], p < .001) produced medium-effect sizes. These findings support stand-alone PVTs and suggest that performance-based measures should be included in ADHD evaluation batteries, which may consist solely of symptom self-report measures.
Network Vulnerability Analysis on Brain Signal/Image Databases Using Nmap and Wireshark Tools

Brain signal processing is important for not only the physiologist doing analysis investigation, but also for the clinician inspecting patients, biomedical engineer who is responsible for collecting, processing, and interpreting the electroencephalogram signals by modeling systems and algorithms for their manipulations. The abundant materials on the subject of brain signal/image processing are scattered in different scientific, technological and physiological journals, international conference proceedings, and also in various databases. Therefore, it is altogether a difficult, too time-consuming, and much tiresome work, exclusively to the newcomers in this field. Therefore, this paper focuses on providing the list of popular databases available belonging to the neurological signals, brain signal/image collections, and so on. The count and the kinds of attacks across the networked computer systems have hiked the significance of computer network security. At present, network administrators use to inspect, examine, scrutinize, review, and analyze the network traffic to figure out what is going on and to set up a prompt response in the event of an identified attack. This paper analyzes the different sweep techniques such as Ping sweep, TCP sweep, and Null sweep on the popular databases about the brain signal/image collections. The results of the Ping sweep support status, TCP sweep times, and Null scan times on different servers are discussed finally.
Real-Time Hand Motion Recognition Using sEMG Patterns Classification

Increasing performance while decreasing the cost of sEMG prostheses is an important milestone in rehabilitation engineering. The different types of prosthetic hands that are currently available to patients worldwide can benefit from more effective and intuitive control. This paper presents a real-time approach to classify finger motions based on surface electromyography (sEMG) signals. A multichannel signal acquisition platform implemented using components off the shelf is used to record forearm sEMG signals from 7 channels. sEMG pattern classification is performed in real time, using a Linear Discriminant Analysis approach. Thirteen hand motions can be successfully identified with an accuracy of up to 95.8% and of 92.7% on average for 8 participants, with an updated prediction every 192 ms.
Leaf Segmentation on Dense Plant Point Clouds with Facet Region Growing

Leaves account for the largest proportion of all organ areas for most kinds of plants, and are comprise the main part of the photosynthetically active material in a plant. Observation of individual leaves can help to recognize their growth status and measure complex phenotypic traits. Current image-based leaf segmentation methods have problems due to highly restricted species and vulnerability toward canopy occlusion. In this work, we propose an individual leaf segmentation approach for dense plant point clouds using facet over-segmentation and facet region growing. The approach can be divided into three steps: (1) point cloud pre-processing, (2) facet over-segmentation, and (3) facet region growing for individual leaf segmentation. The experimental results show that the proposed method is effective and efficient in segmenting individual leaves from 3D point clouds of greenhouse ornamentals such as Epipremnum aureum, Monstera deliciosa, and Calathea makoyana, and the average precision and recall are both above 90%. The results also reveal the wide applicability of the proposed methodology for point clouds scanned from different kinds of 3D imaging systems, such as stereo vision and Kinect v2. Moreover, our method is potentially applicable in a broad range of applications that aim at segmenting regular surfaces and objects from a point cloud.
Effective Routing Algorithm Based on Software Defined Networking for Big Data Applications in Data Centre Network

in this paper, we discuss and identify the main traffic challenges of Hadoop jobs in data centre network (DCN). High volume of traffic is generated during the shuffle phase when the output data of mapper nodes is transferred to reducer nodes. This traffic requires efficient network resources to accelerate the shuffling phase of Hadoop jobs. Equal cost multipath algorithm (ECMP) is used in DCN to perform the routing process of flows to achieve high bandwidth utilization. However, the scheduling process of this algorithm is non-dynamic and lacks a global view of the entire network. Consequently, an effective scheduling and routing algorithm based on Software defined networking (SDN) is proposed to obtain efficient bandwidth utilization for each shuffling flow in DCN. By comparison with ECMP and TRILL (Transparent Interconnection of Lots of Links), the experimental results showed that our proposed algorithm can increase the bandwidth utilization of leaf-spine topology in DCN and provides dynamic scheduling and routing. It also can speed up the execution time of shuffling phase, which leads to improve the performance of Hadoop jobs.
Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy

Convolutional neural networks have been widely used for identifying diabetic retinopathy on color fundus images. For such application, we proposed a novel framework for the convolutional neural network architecture by embedding a preprocessing layer followed by the first convolutional layer to increase the performance of the convolutional neural network classifier. Two image enhancement techniques i.e. 1- Contrast Enhancement 2- Contrast-limited adaptive histogram equalization were separately embedded in the proposed layer and the results were compared. For identification of exudates, hemorrhages and microaneurysms, the proposed framework achieved the total accuracy of 87.6%, and 83.9% for the contrast enhancement and contrast-limited adaptive histogram equalization layers, respectively. However, the total accuracy of the convolutional neural network alone without the prreprocessing layer was found to be 81.4%. Consequently, the new convolutional neural network architecture with the proposed preprocessing layer improved the performance of convolutional neural network.
Clinical and Economic Impact of an Integrated Care Team Model on Targeted, High-Risk Medicare Patients With Type 2 Diabetes

IN BRIEF “Quality Improvement Success Stories” are published by the American Diabetes Association in collaboration with the American College of Physicians, Inc., and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes a referral-based program for managing high-risk patients with type 2 diabetes in California.
A Low-Overhead Multicast Bufferless Router with Reconfigurable Banyan Network

In modern Multi-Processors System-on-Chip (MPSoC), it is highly desirable to provide hardware support for efficient multicast traffic. Recently, bufferless router has become a promising solution for NoC due to its simplicity and low overhead. However, existing multicast bufferless routers utilize the serialized switch allocator to allocate both unicast and multicast packets based on the packet priority one by one, which makes the router have a long critical path and lowers the frequency of the router. In this paper, we propose a low-overhead multicast bufferless router with a reconfigurable Banyan network (called Banyan_PR, PR is short for packets replication). The Banyan switch of the router can be configured as four modes (straight, exchange, U-multicast and L-multicast) according to the type of the incoming packets. For the U-multicast and L-multicast configurations, the multicast packet can be replicated adaptively to reduce the multicast latency. Using a 4 × 4 Banyan network instead of the serialized switch allocator, the Banyan_PR router has shorter critical path length and less area overhead. Synthesis results under a 28nm technology show that the Banyan_PR router can achieve the frequency of 1GHz and save 65% less area and 89% less power consumption than the existing deflection-routing-based multicast bufferless router (called DRM PR all) with the serialized switch allocator. Simulation results illustrate that the Banyan_PR router achieves 25%, 28% and 19% less latency on average than that of the router without packets replication (called Banyan_noPR) and 39%, 42% and 35% less latency on average than that of the DRM_PR_all router under three synthetic traffic patterns respectively.
Fetal MRI Synthesis via Balanced Auto-Encoder Based Generative Adversarial Networks

Machine learning approaches for image analysis require large amounts of training imaging data. As an alternative, the use of realistic synthetic data reduces the high cost associated to medical image acquisition, as well as avoiding confidentiality and privacy issues, and consequently allows the creation of public data repositories for scientific purposes. Within the context of fetal imaging, we adopt an auto-encoder based Generative Adversarial Network for synthetic fetal MRI generation. The proposed architecture features a balanced power of the discriminator against the generator during training, provides an approximate convergence measure, and enables fast and robust training to generate high-quality fetal MRI in axial, sagittal and coronal planes. We demonstrate the feasibility of the proposed approach quantitatively and qualitatively by segmenting relevant fetal structures to assess the anatomical fidelity of the simulation, and performing a clinical verisimilitude study distinguishing the simulated data from the real images. The results obtained so far are promising, which makes further investigation on this new topic worthwhile.
Anxiety and Drinking: Are Personal Beliefs Regarding Control and Unpredictability Relevant?

Abstract Anxiety and drinking problems are fairly common, and it is important to know what factors affect these issues. Previous studies document that control beliefs are associated with anxiety and drinking. Unpredictability beliefs correlate with anxiety, yet whether they relate to drinking is unknown. The present study explored these relationships in a sample of adults (N = 150; 40.0% female; M = 34.4 years old) and in a sample of college students (N = 182; 74.7% female; M = 18.9 years old). Among adults, unpredictability beliefs correlated with anxiety and control beliefs, but not drinking. Similarly, control beliefs correlated with anxiety but not drinking. Furthermore, anxiety and drinking were uncorrelated. Among undergraduates, unpredictability and control beliefs predicted anxiety, whereas unpredictability (pertaining to the self and others) and internality predicted frequency of drinking. Personal unpredictability beliefs emerged as having the strongest association with anxiety across both samples. Future research and treatment should take into account unpredictability beliefs when addressing anxiety problems.