Miracle-dz/UniFAR-FTUs huggingface dataset:
LaKe: An Energy Efficient, Low Latency, Accelerated Key-Value Store

Key-value store is a popular type of cloud computing applications. The performance of key-value store applications have been shown to be very sensitive to load within the data center, and in particular to latency. As load within data center increases, it is becoming hard to maintain key-value store applications' performance, without exceeding both the processing capacity of hosts and the power budgets of racks. In this paper, we present LaKe: a low latency, power efficient key-value store design for cloud applications. LaKe is a modular design, combining multiple cores and cache layering, both in hardware and software. LaKe achieves full line rate throughput, while maintaining a latency of 1.1us and better power efficiency than existing hardware based memcached designs. Using the modularity of our design, we study trade-offs in the use of on-chip memory, SRAM and DRAM in accelerated designs and provide insights for future architectures.
A Hysteretic Buck Converter With 92.1% Maximum Efficiency Designed for Ultra-Low Power and Fast Wake-Up SoC Applications

This paper presents a dc–dc converter for integration in the power management unit of an ultra-low power microcontroller. The converter is designed to significantly reduce the wake-up energy and startup delay of the supplied core. The use of a minimized output capacitor is the key factor to save the wake-up energy. The converter is buffered with only 56 nF and guarantees a stable output of 1.2 V with a voltage ripple smaller than 30 mV. The controller of the proposed dc–dc converter is based on a predictive peak current control that allows the system to control the energy transfer at extremely low power consumption. The proposed circuit is implemented in 130-nm CMOS technology with an area of only 0.14 mm2. It achieves a high conversion efficiency of 92.1% and a small quiescent current of 440 nA. It operates from 1.8 to 3.3 V with a maximum load of 2.65 mA.
Multilevel correlates of household anthropometric typologies in Colombian mothers and their infants

Background. The aim of this study was to establish the association of maternal, family, and contextual correlates of anthropometric typologies at the household level in Colombia using 2005 Demographic Health Survey (DHS/ENDS) data. Methods. Household-level information from mothers 18–49 years old and their children <5 years old was included. Stunting and overweight were assessed for each child. Mothers were classified according to their body mass index. Four anthropometric typologies at the household level were constructed: normal, underweight, overweight, and dual burden. Four three-level [households (n = 8598) nested within municipalities (n = 226), nested within states (n = 32)] hierarchical polytomous logistic models were developed. Household log-odds of belonging to one of the four anthropometric categories, holding ‘normal’ as the reference group, were obtained. Results. This study found that anthropometric typologies were associated with maternal and family characteristics of maternal age, parity, maternal education, and wealth index. Higher municipal living conditions index was associated with a lower likelihood of underweight typology and a higher likelihood of overweight typology. Higher population density was associated with a lower likelihood of overweight typology. Conclusion. Distal and proximal determinants of the various anthropometric typologies at the household level should be taken into account when framing policies and designing interventions to reduce malnutrition in Colombia.
The Two-Dimensional Magneto-optical Trap as a Source of Slow Atoms

We experimentally study the use of two-dimensional magneto-optical trapping ~2D-MOT! for the generation of slow beams of cold atoms out of a vapor cell. A particularly high flux of 9310 rubidium atoms/s at a mean velocity of 8 m/s is obtained using a combination of magneto-optical trapping in two dimensions and Doppler cooling in the third dimension (2D-MOT! The resulting width of the velocity distribution is 3.3 m/s @full width at half maximum ~FWHM! # with a beam divergence of 43 mrad ~FWHM! We investigate the total flux as a function of vapor cell pressure and determine the velocity distribution of our slow atom sources. For comparison, we also realized a low-velocity intense source ~LVIS! , first reported by Lu et al. @Phys. Rev. Lett. 77, 3331 ~1996! #. We find that the 2D-MOT yields a significantly higher flux than the LVIS, even when used with an order of magnitude less laser power. @S1050-2947~98! 04311-X#
Lymphatic Endothelial Cell in Endemic Bancroftian Filariasis: A Focus on the Lymphatics of the Tunica Vaginalis Testis

Background In endemic areas, lymphangiectasia is the fundamental alteration to live Wuchereria bancrofti adult worms which, in adult males, are usually found in the lymphatic vessels of the spermatic cord; accordingly, hydrocele/filaricele is the most common clinical manifestation of bancroftian filariasis. The pathogenic role of the lymphatic endothelial cells (LECs) and the status of mesothelial cells (MCs) samples of the parietal layer (PL) of the tunica vaginalis testis were examined. Methods The PL of thirty-two patients, excised for different reasons, was examined by histology and immunohistochemistry using the D2-40 monoclonal antibody for identification of LECs and CK-7 antibody for recognition of mesothelial cells (MCs). Results The most important findings were (a) marked lymphangiectasia, especially in hydroceles with minor evolution time; (b) the first report of lymphatic stomata and submesothelial lacunae in filarial acute hydrocele; (c) the likely participation of LECs in filarial granuloma; (d) the potential phenotypic transition of LECs into myofibroblasts in severe chylocele; and (e) mesothelial reactive hyperplasia, a hallmark of filaricele, varying in intensity from mild to severe, sometimes mimicking a mesothelial neoplasia. Conclusion The data suggest that LECs have an active role in the pathogenesis of bancroftian hydrocele and, possibly, in other clinical forms of lymphatic filariasis.
Reactions with Aminobenzoic Acids via Diazonium Salts Open New Routes to Bio-Derived Aromatics

Aromatics have a broad application in our everyday life ranging from plastics, coatings and fibres, to food and pharmaceuticals. To date the bulk of these aromatics is derived from naphtha-based pet-rochemistry. However, recent progress in the fermentative production of metabolites using renew-able resources and engineered microbes has enabled the production of bio-precursors, such as 4-amino benzoic acid (pABA) and 2-amino benzoic acid (oABA). In this work we explored the feasibility of Sandmeyer reactions for the conversion of pABA to terephthalic and oABA salicylic acid, providing two very important platform chemicals for the chemical and pharmaceutical industries. We could demonstrate that both acids can be obtained from the amino benzoic acids derived from the shikimate pathway in microbes and plants. Good conversions could be achieved using Sandmeyer reactions at mild conditions with biodegradable reagents and without organic solvents.
A Time-Ordered Aggregation Model-Based Centrality Metric for Mobile Social Networks

How to measure the centrality of nodes is a significant problem in mobile social networks (MSNs). Current studies in MSNs mainly focus on measuring the centrality of nodes in a certain time interval based on the static graph that do not change over time. However, the network topology of MSNs is changing very rapidly, which is the main characteristic of MSNs. Therefore, it will not be accurate to measure the centrality of nodes in a certain time interval by using the static graph. To solve this problem, this paper first introduces a new centrality metric named cumulative neighboring relationship (CNR) for MSNs. Then, a time-ordered aggregation model is proposed to reduce a dynamic network to a series of time-ordered networks. Based on the time-ordered aggregation model, this paper proposes three particular time-ordered aggregation methods and combines with the proposed centrality metric CNR to measure the importance of nodes in a certain time interval. Finally, extensive trace-driven simulations are conducted to evaluate the performance of our proposed time-ordered aggregation model-based centrality metric time-ordered cumulative neighboring relationship (TCNR). The results show that the exponential time-ordered aggregation method can measure TCNR centrality in a certain time interval more accurately than other aggregation methods, and our proposed time-ordered aggregation model-based centrality metric TCNR outperforms other existing temporal centrality metrics.
Heavy-Traffic Limit of theGI/GI/1 Stationary Departure Process and Its Variance Function

Heavy-traffic limits are established for the stationary departure process from a GI/GI/1 queue and its variance function. The limit process is a function of the Brownian motion limits of the arrival and service processes plus the stationary reflected Brownian motion (RBM) limit of the queue-length process. An explicit expression is given for the variance function, which depends only on the first two moments of the interarrival times and service times plus the previously determined correlation function of canonical (drift −1, diffusion coefficient 1) RBM. The limit for the variance function here is used to show that the approximation for the index of dispersion for counts of the departure process used in our new robust queueing network analyzer is asymptotically correct in the heavy-traffic limit.
Understanding the geographical burden of stunting in India: A regression‐decomposition analysis of district‐level data from 2015–16

Abstract India accounts for approximately one third of the world's total population of stunted preschoolers. Addressing global undernutrition, therefore, requires an understanding of the determinants of stunting across India's diverse states and districts. We created a district‐level aggregate data set from the recently released 2015–2016 National and Family Health Survey, which covered 601,509 households in 640 districts. We used mapping and descriptive analyses to understand spatial differences in distribution of stunting. We then used population‐weighted regressions to identify stunting determinants and regression‐based decompositions to explain differences between high‐ and low‐stunting districts across India. Stunting prevalence is high (38.4%) and varies considerably across districts (range: 12.4% to 65.1%), with 239 of the 640 districts have stunting levels above 40% and 202 have prevalence of 30–40%. High‐stunting districts are heavily clustered in the north and centre of the country. Differences in stunting prevalence between low and high burden districts were explained by differences in women's low body mass index (19% of the difference), education (12%), children's adequate diet (9%), assets (7%), open defecation (7%), age at marriage (7%), antenatal care (6%), and household size (5%). The decomposition models explained 71% of the observed difference in stunting prevalence. Our findings emphasize the variability in stunting across India, reinforce the multifactorial determinants of stunting, and highlight that interdistrict differences in stunting are strongly explained by a multitude of economic, health, hygiene, and demographic factors. A nationwide focus for stunting prevention is required, while addressing critical determinants district‐by‐district to reduce inequalities and prevalence of childhood stunting.
Mobile Health (mHealth) Versus Clinic-Based Group Intervention for People With Serious Mental Illness: A Randomized Controlled Trial.

OBJECTIVE mHealth approaches that use mobile phones to deliver interventions can help improve access to care for people with serious mental illness. The goal was to evaluate how mHealth performs against more traditional treatment. METHODS A three-month randomized controlled trial was conducted of a smartphone-delivered intervention (FOCUS) versus a clinic-based group intervention (Wellness Recovery Action Plan [WRAP]). Participants were 163 clients, mostly from racial minority groups and with long-term, serious mental illness (schizophrenia or schizoaffective disorder, 49%; bipolar disorder, 28%; and major depressive disorder, 23%). Outcomes were engagement throughout the intervention; satisfaction posttreatment (three months); and improvement in clinical symptoms, recovery, and quality of life (assessed at baseline, posttreatment, and six months). RESULTS Participants assigned to FOCUS were more likely than those assigned to WRAP to commence treatment (90% versus 58%) and remain fully engaged in eight weeks of care (56% versus 40%). Satisfaction ratings were comparably high for both interventions. Participants in both groups improved significantly and did not differ in clinical outcomes, including general psychopathology and depression. Significant improvements in recovery were seen for the WRAP group posttreatment, and significant improvements in recovery and quality of life were seen for the FOCUS group at six months. CONCLUSIONS Both interventions produced significant gains among clients with serious and persistent mental illnesses who were mostly from racial minority groups. The mHealth intervention showed superior patient engagement and produced patient satisfaction and clinical and recovery outcomes that were comparable to those from a widely used clinic-based group intervention for illness management.
Fast K-Means Clustering with Anderson Acceleration

We propose a novel method to accelerate Lloyd's algorithm for K-Means clustering. Unlike previous acceleration approaches that reduce computational cost per iterations or improve initialization, our approach is focused on reducing the number of iterations required for convergence. This is achieved by treating the assignment step and the update step of Lloyd's algorithm as a fixed-point iteration, and applying Anderson acceleration, a well-established technique for accelerating fixed-point solvers. Classical Anderson acceleration utilizes m previous iterates to find an accelerated iterate, and its performance on K-Means clustering can be sensitive to choice of m and the distribution of samples. We propose a new strategy to dynamically adjust the value of m, which achieves robust and consistent speedups across different problem instances. Our method complements existing acceleration techniques, and can be combined with them to achieve state-of-the-art performance. We perform extensive experiments to evaluate the performance of the proposed method, where it outperforms other algorithms in 106 out of 120 test cases, and the mean decrease ratio of computational time is more than 33%.
Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts

Existing keyphrase extraction methods suffer from data sparsity problem when they are conducted on short and informal texts, especially microblog messages. Enriching context is one way to alleviate this problem. Considering that conversations are formed by reposting and replying messages, they provide useful clues for recognizing essential content in target posts and are therefore helpful for keyphrase identification. In this paper, we present a neural keyphrase extraction framework for microblog posts that takes their conversation context into account, where four types of neural encoders, namely, averaged embedding, RNN, attention, and memory networks, are proposed to represent the conversation context. Experimental results on Twitter and Weibo datasets show that our framework with such encoders outperforms state-of-the-art approaches.
A Phase‐Based Approach for Animating Images Using Video Examples

We present a novel approach for animating static images that contain objects that move in a subtle, stochastic fashion (e.g. rippling water, swaying trees, or flickering candles). To do this, our algorithm leverages example videos of similar objects, supplied by the user. Unlike previous approaches which estimate motion fields in the example video to transfer motion into the image, a process which is brittle and produces artefacts, we propose an Eulerian phase‐based approach which uses the phase information from the sample video to animate the static image. As is well known, phase variations in a signal relate naturally to the displacement of the signal via the Fourier Shift Theorem. To enable local and spatially varying motion analysis, we analyse phase changes in a complex steerable pyramid of the example video. These phase changes are then transferred to the corresponding spatial sub‐bands of the input image to animate it. We demonstrate that this simple, phase‐based approach for transferring small motion is more effective at animating still images than methods which rely on optical flow.
Accuracy of Ebola Information in a Knowledge Exchange Social Website (KESW)

Background: Misinformation on interactive Knowledge Exchange Social Websites (KESWs) is concerning since it can influence Internet users’ health behaviors, especially during an infectious disease outbreak. Objective: The present study seeks to examine the accuracy and characteristics of health information posted to a Knowledge Exchange Social Website (KESW). Methods: A sample of 204 answers to Ebola questions were extracted and rated for accuracy. Multiple logistic regression modeling was used to examine whether answer characteristics (best answer, professional background, statistical information, source disclosed, link, and word count) predicted accuracy. Results: Overall, only 27.0% of the posted answers were rated as “accurate”. Accuracy varied across question topics with between 11.8% - 45.5% of answers being rated as accurate. When Yahoo Answers’ “best answers” were examined, the overall accuracy was substantially higher, with 80.0% of “best answers” being rated as accurate compared to 16.0% of all other answers. Conclusion: There is need for tools to help Internet users navigate health information posted on these dynamic user-generated knowledge exchange social websites.
OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models

We present OpenSeq2Seq – an open-source toolkit for training sequence-to-sequence models. The main goal of our toolkit is to allow researchers to most effectively explore different sequence-to-sequence architectures. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq provides building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future.
Astrocyte truncated tropomyosin receptor kinase B mediates brain‐derived neurotrophic factor anti‐apoptotic effect leading to neuroprotection

Astrocytes are glial cells that help maintain brain homeostasis and become reactive in neurodegenerative processes releasing both harmful and beneficial factors. We have demonstrated that brain‐derived neurotrophic factor (BDNF) expression is induced by melanocortins in astrocytes but BDNF actions in astrocytes are largely unknown. We hypothesize that BDNF may prevent astrocyte death resulting in neuroprotection. We found that BDNF increased astrocyte viability, preventing apoptosis induced by serum deprivation by decreasing active caspase 3 and p53 expression. The anti‐apoptotic action of BDNF was abolished by ANA‐12 (a specific TrkB antagonist) and by K252a (a general Trk antagonist). Astrocytes only express the BDNF receptor TrkB‐truncated isoform 1, TrkB‐T1. BDNF induced ERK, Akt, and Src (a non‐receptor tyrosine kinase) activation in astrocytes. Blocking ERK and Akt pathways abolished BDNF protection in serum deprivation‐induced cell death. Moreover, BDNF protected astrocytes from death by 3‐nitropropionic acid (3‐NP), an effect also blocked by ANA‐12, K252a, and inhibitors of ERK, calcium, and Src. BDNF reduced reactive oxygen species levels induced in astrocytes by 3‐NP and increased xCT expression and glutathione levels. Astrocyte‐conditioned medium (ACM) from untreated astrocytes partially protected PC12 neurons, whereas ACM from BDNF‐treated astrocytes completely protected PC12 neurons from 3‐NP‐induced apoptosis. Both ACM from control and BDNF‐treated astrocytes markedly reduced reactive oxygen species levels induced by 3‐NP in PC12 cells. Our results demonstrate that BDNF protects astrocytes from cell death through TrkB‐T1 signaling, exerts an antioxidant action, and induces release of neuroprotective factors from astrocytes.
PET-MR respiratory signal estimation using semi-supervised manifold alignment

In simultaneous PET-MR scanning, respiratory motion can lead to artefacts and blurring in both PET and MR images, negatively impacting research and clinical applications. This can be compensated for by estimating respiratory motion through a respiratory signal. Here, we propose a data-driven dimensionality-reduction-based technique which aligns manifolds formed from both PET and MR data to produce a robust signal even in situations where MR data are unavailable, as expected in realistic workflows. To handle the missing MR data, 3 methods for semi-supervised manifold alignment alignment were tested using a semi-synthetic dataset consisting of 500 0.64 s dynamic MR volumes and PET sinograms. It was found that implicit correspondences for unlabelled PET data were most effective on average for signal estimation, at 81 ± 4% mean correlation to a gold standard diaphragmatic navigator, compared to 89 ± 0.2% when using MR only with no missing data. Two explicit correspondence estimators, based on graph theory, performed poorly, with 1-to-1 and many-to-1 correspondences achieving 34 ±16% correlation and 31 ± 9% correlation, respectively.
Towards a typology of ASR errors via syntax-prosody mapping

This study explores automatic speech recognition (ASR) errors from a syntax-prosody mapping perspective. Our contribution is threefold: we propose (i) an ASR error study according to French syntactic structures, (ii) a quantitative evaluation of the syntax-prosody mapping in reference transcriptions (iii) a qualitative analysis of syntax-prosody mapping violations in ASR transcriptions. Results show that some morphosyntactic and syntactic components are particularly prone to transcription errors such as proper names or verbal nuclei. In addition, we found that transcription errors in the ASR hypothesis may violate the syntactic-prosodic mapping rules. Such conflicting patterns may be used as clues to automatically detect ASR errors.
Tree-climbing mangrove crabs: a case of convergent evolution

Several crab species of the families Sesarmidae and Grapsidae (Crustacea: Brachyura: Grapsoidea) are known to climb mangrove trees. They show different degrees of dependence on arboreal life, with only a few of them thriving in the tree canopies and feeding on fresh leaves. Some of the sesarmid tree-dwelling crabs share a number of morphological characters and therefore have been considered to be of monophyletic origin. A phylogeny derived from 1038 base pairs of the mitochondrial DNA encoding the small and large ribosomal subunits was used to examine the evolutionary origin of tree-climbing behaviour within the Grapsoidea, and to determine whether morphological and ecological similarities are based on convergence or common ancestry. The analysis included African, American and Asian arboreal crab species plus several representatives of ground-living forms. Our results suggest that the very specialized arboreal lifestyle evolved several times independently within grapsoid mangroves crabs, providing another striking example of the likelihood of convergence in evolutionary biology and the degree of phenetic and ecological potential to be found among marine organisms.