Miracle-dz/UniFAR-FTUs huggingface dataset:
A 4.28 pJ/access high-density average-8T sub-threshold SRAM with reverse narrow-width effect (RNWE)-aware sizing

This paper presents a sizing technique to strengthen the write and access transistors based on reverse narrow-width effect for sub-threshold SRAM in advanced CMOS technology. The technique is verified by a 16 kb SRAM chip in 65 nm technology. The measurement results show the chip consumes only 4.28 pJ/access in the best case with supply voltage as low as 0.27 V. Based on the average of measurements from 20 chips, the chip works from 30.8 kHz at 0.3 V while consuming 246 nW up to 2.42 MHz at 0.6 V while consuming 11.6 μW.
Cluster-based polynomial-fit histogram equalization (CPHEQ) for robust speech recognition

Noise robustness is one of the primary challenges facing most automatic speech recognition (ASR) systems. A vast amount of research efforts on preventing the degradation of ASR performance under various noisy environments have been made during the past several years. In this paper, we consider the use of histogram equalization (HEQ) for robust ASR. In contrast to conventional methods, a novel data fitting method based on polynomial regression was presented to efficiently approximate the inverse of the cumulative density functions of speech feature vectors for HEQ. Moreover, a more elaborate attempt of using such polynomial regression models to directly characterizing the relationship between the speech feature vectors and their corresponding probability distributions, under various noise conditions, was proposed as well. All experiments were carried out on the Aurora-2 database and task. The performance of the presented methods were extensively tested and verified by comparison with the other methods. Experimental results shown that for cleancondition training, our method achieved a considerable word error rate reduction over the baseline system, and also significantly outperformed the other methods. Index Terms: noise robustness, speech recognition, histogram equalization, polynomial regression model
Testing-Based Watermarking Techniques for Intellectual-Property Identification in SOC Design

The author proposes a novel testing-based watermarking scheme for intellectual-property (IP) identification in this paper. The principles are established for the development of new watermarking IP-identification procedures that depend on current IP-based design flow. The core concept is embedding a watermark-generating circuit (WGC) and a test circuit into the IP core at the behavior design level. Therefore, this scheme can also successfully survive synthesis, placement, and routing and can identify the IP core at various design levels. This method adopts current main system-on-a-chip (SOC) design-for-test (DFT) strategies. The identity of the IP is proven during the general test process without implementing any extra extraction flow. After the chip has been manufactured and packaged, it is still easy to detect the identification of the IP provider without the need to examine the microphotograph. On real designs, our approaches entail low hardware overhead, tracking costs, and processing-time costs. The proposed method solves the IP-identification problem.
RESTful API in Life Science Research Systems and Data Integration Challenges: Linking Metabolic Pathway, Metabolic Network, Gene and Publication

Translational science research development requires life sciences researchers to update their concept on systems integration and data integration. The RESTful API has proven to be very useful in integrating the massive amount of data and tools. This work presents an approach for biological systems analysis based on the semantic web and RESTful API. The integration of this approach in the BioExtract Server engine as a WMS (workflow management system) allows us to apply our work to the linking of several biological levels (metabolic pathway, metabolic network model, genes and publications). This work shows how HPC infrastructure and RESTful API can be an opportunity in Systems Biology and life science research challenges. We present a use case in BioExtract server as an integrated workflow management system. The use case shows how we can extract data to build a mathematical model for a specific biological system. The two workflow examples provided in this work enhance the flexibility in the workflow for reusability. Then any other researcher can customize the same workflow for different gene name or biological process.
A Reception Based Node Selection Protocol for Multi-hop Routing in Vehicular Ad-hoc Networks

Vehicular Ad hoc networks (VANETs) are large complex networks consisting of vehicles with high speeds and highly dynamic network structure. Multi-hop routing in VANETs is one of the important issues due to frequent topology changes and high mobility of network nodes. Most of the current routing protocols propose broadcast based hop selection for packet transfer where requests are broadcasted until the destination is found in the network. Due to this issue, the channel availability reduces and network congestion and packet delay increases. This paper proposes a reception based node selection (RBNS) technique without broadcasting the request messages to discover the destination node. Neighbouring vehicles keep records of their packet reception rate for nodes within their transmission range and the vehicle with highest reception rate is selected as forwarding hop and assigned as the source in the subsequent region to prevent vehicles from the previous transmission regions from broadcasting the request messages. The algorithm performed best in terms of throughput, packet drop and collision when compared to other algorithm from the literature.
Palmitoleic Acid (N-7) Attenuates the Immunometabolic Disturbances Caused by a High-Fat Diet Independently of PPARα

Palmitoleic acid (PMA) has anti-inflammatory and antidiabetic activities. Here we tested whether these effects of PMA on glucose homeostasis and liver inflammation, in mice fed with high-fat diet (HFD), are PPAR-α dependent. C57BL6 wild-type (WT) and PPAR-α-knockout (KO) mice fed with a standard diet (SD) or HFD for 12 weeks were treated after the 10th week with oleic acid (OLA, 300 mg/kg of b.w.) or PMA 300 mg/kg of b.w. Steatosis induced by HFD was associated with liver inflammation only in the KO mice, as shown by the increased hepatic levels of IL1-beta, IL-12, and TNF-α; however, the HFD increased the expression of TLR4 and decreased the expression of IL1-Ra in both genotypes. Treatment with palmitoleate markedly attenuated the insulin resistance induced by the HFD, increased glucose uptake and incorporation into muscle in vitro, reduced the serum levels of AST in WT mice, decreased the hepatic levels of IL1-beta and IL-12 in KO mice, reduced the expression of TLR-4 and increased the expression of IL-1Ra in WT mice, and reduced the phosphorylation of NF 𝜅B (p65) in the livers of KO mice. We conclude that palmitoleate attenuates diet-induced insulin resistance, liver inflammation, and damage through mechanisms that do not depend on PPAR-α.
Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text

We present the time-dependent topic-cluster model, a hierarchical approach for combining Latent Dirichlet Allocation and clustering via the Recurrent Chinese Restaurant Process. It inherits the advantages of both of its constituents, namely interpretability and concise representation. We show how it can be applied to streaming collections of objects such as real world feeds in a news portal. We provide details of a parallel Sequential Monte Carlo algorithm to perform inference in the resulting graphical model which scales to hundred of thousands of documents.
Localization Using Extended Kalman Filters in Wireless Sensor Networks

Localization arises repeatedly in many location-aware applications such as navigation, autonomous robotic movement, and asset tracking. Analytical localization methods include triangulation and trilateration. Triangulation uses angles, distances, and trigonometric relationships to locate an object. Trilateration, on the other hand, uses only distance measurements to identify the position of the target. Figure 1A, describes a simple example of trilateration. Using three reference points S1, S2, and S3 with known locations and distances d1, d2, and d3 to the target object, the object can be located at the intersecting point of the three circles. However, in a dynamic system where distance measurements are noisy and fluctuate, the task of localizing becomes difficult. This can be seen in Figure 1B, where with fluctuating distances, regions within the circles become possible locations for the tracked object. In this case, rather than the object being located at a single point at the intersection of the circles as in Figure 1A, the object can be located anywhere in the dark overlapped region in Figure 1B.
Towards clinically relevant automatic assessment of upper-limb motor function impairment

This paper is to develop an automated assessment system of upper-limb motor function impairment for clinical environment. Although we had proposed the system in our previous work, there are some rooms to be improved. Using glove sensor was difficult due to stroke patient's hand contracture. Moreover, it was based on machine learning, and thus required huge effort to collect reference data to increase classification accuracy. To address those issues, three tests of Fugl-Meyer Assessment which were closely related the issues were chosen as target tests. Since Kinect v2 and force-sensing resister can provide hand-related information, the tests were automated without glove sensor. Fuzzy-logic classification table that is based on traditional FMA guidelines was implemented to rate the FMA score without machine learning. With a healthy subject, simple experiments were conducted to evaluate the proposed system with novel classification scheme. The results show a feasibility for more convenient automated assessment of upper-limb motor function impairment.
Sleep disturbance in patients with rheumatoid arthritis: evaluation by medical outcomes study and visual analog sleep scales.

OBJECTIVE Except for some polysomnography studies, there have been no large quantitative studies of sleep disturbance (SD) in rheumatoid arthritis (RA). SD has taken on new importance with the observation that etanercept and infliximab reduce daytime sleepiness, and patient groups indicate that sleep is an important issue. METHODS We evaluated 8676 patients with RA and a comparison group of 1364 subjects with non-fibromyalgia, noninflammatory disorders (NID) using the Medical Outcome Study (MOS) sleep questionnaire, including 2 MOS sleep problem indexes (SPI-I, SPI-II) and the MOS SD scale. In addition, patients completed a visual analog scale (VAS) sleep disturbance scale (SDS). RESULTS The scales had similar mean values: SPI-I 35.4 (19.4), SPI-II 36.0 (19.1), SDS 35.0 (24.7), and VAS sleep 36.1 (29.7), and the values for the MOS scales exceeded population norms by 25% (VAS by 42%). In multivariable analyses SD was primarily determined by pain and mood. Patients receiving anti-tumor necrosis factor (TNF) did not have less abnormal sleep scores. SD was comparable in RA and NID. The VAS scale was more strongly associated with RA clinical variables than the MOS scales; however, the distributional characteristics of the scales differed, with the VAS scales capturing more extreme values. The standard error of the measurement (SEM), which is related to minimal (important) change, was SPI-I 9.0, SPI-II 7.3, SDS 9.6, and VAS sleep 10.4. CONCLUSION SD is increased in RA, and 25% to 42% of SD can be attributed to RA. SD is linked to pain, mood, and disease activity. SD is slightly greater in women and is less with increasing age. All scales appear to be valid in RA, with minimal differences in SEM.
Computation offloading decision algorithm for energy saving in 5G/HetNets C-RAN

Cloud Radio Access Network (C-RAN) is a novel mobile network architecture that brings baseband processing units (BBUs) of a set of base stations into a central server keeping only remote radio heads (RRHs) at the cell sites. Because of their limited resources, running sophisticated applications on mobile handsets could result in poor performances and shortened battery lifetime. One promising solution is computation offloading to the cloud. It permits to enhance both QoS and battery life of mobile terminals. In this paper we present a novel offloading decision algorithm that decides about offloading mobile computation to Cloud-RRH, i.e RRH with additional computational and storage resources located in the cloud. The proposed offloading algorithm incorporates a multitude of parameters in the offloading decision process while diminishing the mobile handset energy consumption and keeping a good user quality of experience by reducing application's response time. Simulation shows that the proposed algorithm is able to assure the computation of all the applications while respecting latency constraints and to prolong the mobile handset battery life.
PET Imaging of Estrogen Receptors as a Diagnostic Tool for Breast Cancer Patients Presenting with a Clinical Dilemma

16α-18F-fluoro-17β-estradiol (18F-FES) is an estrogen receptor (ER)–specific PET tracer with various potential interesting applications. The precise contribution of this technique in current clinical practice, however, has yet to be determined. Therefore, the aim of this study was to evaluate the value of 18F-FES PET in breast cancer patients presenting with a clinical dilemma. Methods: 18F-FES PET examination could be requested by referring physicians for patients with a history of ER-positive breast cancer and the presence of a clinical dilemma despite complete standard work-up. All requests for 18F-FES PET required a positive arbitration by a dedicated medical oncologist and nuclear medicine physician. The referring physician was asked to fill in validated questionnaires before, shortly after, and at more than 3 mo after 18F-FES PET to determine indication, diagnostic value, and therapeutic consequences of 18F-FES PET. To further validate 18F-FES PET findings, 18F-FES PET lesions were quantified and compared with centrally reviewed conventional imaging. Results: Thirty-three patients underwent 18F-FES PET between December 2008 and October 2010. 18F-FES PET was requested to evaluate equivocal lesions on conventional work-up (n = 21), ER status in metastatic patients (n = 10), and the origin of metastases (n = 2). 18F-FES–positive lesions were observed in 22 patients. 18F-FES PET was especially sensitive for bone metastases, detecting 341 bone lesions, compared with 246 by conventional imaging. The sensitivity for liver metastases was poor, and quantification of 18F-FES uptake in liver lesions was hampered by high physiologic background. 18F-FES uptake was highly variable between all metastases (range of standardized uptake value, 1.20–18.81), and 45% of the patients with a positive 18F-FES PET finding had both 18F-FES–positive and 18F-FES–negative metastases. 18F-FES PET improved diagnostic understanding in 88% of the patients and led to therapy change in 48% of the patients. Conclusion: With the exception of liver metastases, whole-body imaging of ER expression with 18F-FES PET can be a valuable additional diagnostic tool when standard work-up is inconclusive. 18F-FES PET supported therapy decisions by improving diagnostic understanding and providing information on ER status of tumor lesions.
Mathematical Model of the Rapidly Activating Delayed Rectifier Potassium Current IKr in Rabbit Sinoatrial Node

Role of IKr in Rabbit Sinoatrial Node. Introduction: A rapidly activating delayed rectifier potassium current (IKr) is known to have an important role in determining the properties of spontaneous pacing in enzymatically isolated rabbit sinoatrial node (SAN) cells. The functional characteristics of IKr are conferred by its dependence on time, voltage, and external potassium. The aim of this study was to develop a rigorous mathematical representation for IKr based on experimental findings and to investigate the role of IKr in the automaticity and intercellular communication of SAN cells.
The efficacy of a subhypnotic dose of propofol in preventing laryngospasm following tonsillectomy and adenoidectomy in children

Background: Laryngospasm is a well‐known problem typically occurring immediately following tracheal extubation. Propofol is known to inhibit airway reflexes. In this study, we sought to assess whether the empiric use of a subhypnotic dose of propofol prior to emergence will decrease the occurrence of laryngospasm following extubation in children.
Minimal public communication for maximum rate secret key generation

Secret key generation is considered for a pair of terminals that observe correlated sources and communicate interactively over a public channel. It is argued that optimum rate secret key generation is linked inherently to the Wyner's notion of common information between two dependent random variables. The minimum rate of interactive public communication required to generate an optimum rate secret key is characterized in terms of a variant of this notion of common information.
Providing service differentiation for video streaming in hybrid P2P overlay network

In Peer-to-Peer (P2P) networks, a great number of peers are reluctant to contribute to the system. An incentive mechanism thus becomes a necessary component to motivate the cooperation of peers. Moreover, due to the continuous nature of data transmission, the P2P media streaming requires different incentive strategies from the P2P file-sharing. In this paper, we propose a new service differentiation mechanism to provide a redistribution incentive for P2P streaming in a hybrid overlay network. The contribution amount of a peer can be measured from the number of video sub-streams it transmits, instead of the quantity of transferred data. By sending one request message, the number of sub-streams that each peer can retrieve is varied by its contribution amount. An altruistic peer thus has to send less request messages and experiences smoother video quality than a selfish peer. Through simulations, we demonstrate that the proposed mechanism can provide service differentiation among peers according to their contribution amount.
Efficient Cloth Pattern Recognition Using Random Ferns

SUMMARY This paper proposes a novel image classiﬁcation scheme for cloth pattern recognition. The rotation and scale invariant delta-HOG (DHOG)-based descriptor and the entire recognition process using random ferns with this descriptor are proposed independent from pose and scale changes. These methods consider maximun orientation and various radii of a circular patch window for fast and e ﬃ cient classiﬁcation even when cloth patches are rotated and the scale is changed. It exhibits good performance in cloth pattern recognition experiments. It found a greater number of similar cloth patches than dense-SIFT in 20 tests out of a total of 36 query tests. In addition, the proposed method is much faster than dense-SIFT in both training and testing; its time consumption is decreased by 57.7% in training and 41.4% in testing. The proposed method, therefore, is expected to contribute to real-time cloth searching service applications that update vast numbers of cloth images posted on the Internet.
Social Support, Body Image Perception and Depressive Symptoms, Among University Students in Nigeria, by Gender and Ethnicity

Background: The health determinant model indicates that certain socio-cultural, socio-demographic, environmental, and lifestyle factors influence health status and wellbeing of any population group in any given nation, [1]. Previous studies have suggested the need for regional and interregional comparison of health inequalities due to the interaction of these factors. AIMs: This study investigated social support, body image perception and depressive symptoms by sex and ethnicity among university students in Nigeria. Method: The study was a cross sectional survey. Full time university students were recruited from six universities within the major three ethnic groups in Nigeria.2500 anonymous questionnaire was sent to students during a class lecture, 1549 responses were valid, while 563 responses were rejected for missing data especially sex and ethnicity and 388 students did not return their questionnaire. The variables examined were, demographic, income status, social support, body image perception and depressive symptoms. Descriptive tests, chi-square tests and analysis of variance (ANOVA) tests were conducted. Results: The result showed that depression indicated significant differences by gender and ethnicity, with students from the Hausa ethnic group reporting depression more than other ethnic groups, and female students more than males. Body image indicated a sex*ethnicity interaction effect for theideal female body image. Further analysis of the data suggested that Igbo males and Yoruba females’ preferred bigger female body size. Irrespective of ethnicity, the study indicated that more female students preferred bigger female body sizes than males. With regard to social support and monthly income, the result indicated sex*ethnicity interaction effects, where female students from the Hausa ethnic group reported better social support and monthly income. Conclusion: The findings indicated that the mental health of female students in the sample were poorer than those of male students and female students from the Hausa ethnic group demonstrating the worst possible health outcome, despite a morefavorable social support and income status. This study made a major contribution to the understanding that people who live among regions with conflict and violence may report poor psychological health compared to those that live in a conflict free zones.
A hybrid HMM/traps model for robust voice activity detection

We present three voice activity detection (VAD) algorithms that are suitable for the off-line processing of noisy speech and compare their performance on SPINE-2 evaluation data using speech recognition error rate as the quality metric. One VAD system is a simple HMM-based segmenter that uses normalized log-energy and a degree of voicing measure as raw features. The other two VAD systems focus on frequency-localized temporal information in the speech signal using a TempoRAl PatternS (TRAPS) classifier. They differ only in the processing of the TRAPS output. One VAD system uses median filtering to generate segment hypotheses, while the other is a hybrid system that uses a Viterbi search identical to that used in the HMM segmenter. Recognition on the hybrid HMM/TRAPS segmentation is more accurate than recognition on the other two segmentations by 1% absolute. This difference is statistically significant at a 99% confidence level according to a matched pairs sentence-segment word error test.