nils-herrmann/clean_scirepeval huggingface dataset:
Session based Recommendations with Recurrent Neural Networks
Venue: ICLR

We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real life recommender systems often face the problem of having to base recommendations only on short session based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix) In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item to item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNN based approach for session based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data sets show marked improvements over widely used approaches.
Improved Recurrent Neural Networks for Session based Recommendations
Venue: DLRS@RecSys

Recurrent neural networks (RNNs) were recently proposed for the session based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN based models for session based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.
Rapidly separating microneedles for transdermal drug delivery.
Venue: Acta biomaterialia

The applications of polymer microneedles (MNs) into human skin emerged as an alternative of the conventional hypodermic needles. However, dissolving MNs require many minutes to be dissolved in the skin and typically have difficulty being fully inserted into the skin, which may lead to the low drug delivery efficiency. To address these issues, we introduce rapidly separating MNs that can rapidly deliver drugs into the skin in a minimally invasive way. For the rapidly separating MNs, drug loaded dissolving MNs are mounted on the top of solid MNs, which are made of biodegradable polylactic acid which eliminate the biohazardous waste. These MNs have sufficient mechanical strength to be inserted into the skin with the drug loaded tips fully embedded for subsequent dissolution. Compared with the traditional MNs, rapidly separating MNs achieve over 90% of drug delivery efficiency in 30s while the traditional MNs needs 2min to achieve the same efficiency. With the in vivo test in mice, the micro holes caused by rapidly separating MNs can heal in 1h, indicating that the rapidly separating MNs are safe for future applications. These results indicate that the design of rapidly separating dissolvable MNs can offer a quick, high efficient, convenient, safe and potentially self administered method of drug delivery.  Polymer microneedles offer an attractive, painless and minimally invasive approach for transdermal drug delivery. However, dissolving microneedles require many minutes to be dissolved in the skin and typically have difficulty being fully inserted into the skin due to the skin deformation, which may lead to the low drug delivery efficiency. In this work we proposed rapidly separating microneedles which can deliver over 90% of drug into the skin in 30s. The in vitro and in vivo results indicate that the new design of these microneedles can offer a quick, high efficient, convenient and safe method for transdermal drug delivery.
The Impact of Country of Origin on Brand Positioning for Luxury Goods

Brand positioning for luxury goods requires creating unique and strong associations in consumers' minds regarding upscale benefits and high social status. Country of origin has numerous implications on brand image perception, being an important positioning element. This paper's purpose is to examine the impact of country of origin on brand positioning for luxury goods, for a more accurate understanding of how brand image can be affected by product provenance. We analyze the effects of the degree of fit between country image and the associated product category. Furthermore, through an exploratory research, based on investigation of secondary information as a data collection method, we emphasize the variation of favorable perceptions due to a brand's perceived country of origin. We also highlight the effects that country image has on brand prestige, influencing consumers' willingness to pay a premium price for the brand. The study explains how desired positioning can be achieved by leveraging country of origin characteristics to brand image. Main findings support previous research, showing how a favorable country image can reinforce brand positioning. Finally, we discuss the implications of brand origin for luxury goods in the era of global brands and offer recommendations for future research.
Clinically Significant Interactions with Benzodiazepines

Benzodiazepines are a class of lipophilic compounds used for a variety of indications including anxiety disorders, insomnia, epilepsy, musculoskeletal disorders, and as sedatives during surgery. The chemical nucleus of each benzodiazepine as well as their pharmacodynamic activity is identical. However, alterations to this basic structure lead to numerous benzodiazepines with different pharmacokinetic properties and lipid solubility resulting in agents with differing rates of elimination, concentrations, volumes of distribution, and potencies. In this chapter we will review the varied kinetics of available benzodiazepines and discuss the metabolic pathways leading to excretion of these medications. This review includes the enzymes (Phase I and Phase II) responsible for metabolism of the parent compound and their intermediates. We also review the pharmacologic and pharmacokinetic activity of the intermediate metabolites. Furthermore we will identify pharmacokinetic interactions of benzodiazepines including: drug drug interactions, P gylcoprotein interactions, protein binding interactions, and food/herbal interactions. Other factors that may require alterations in benzodiazepine dosing such as weight, sex, age, smoking status, genetic polymorphisms, and pharmacokinetic interactions will similarly be discussed. Finally we will identify the clinical monitoring that is required for individuals being prescribed benzodiazepines including respiratory depression, sedation, and withdrawal. The goal of this chapter is to give the reader a background of the factors that should be considered when choosing or monitoring the available benzodiazepines in clinical practice.
Expression of Alzheimer's disease risk genes in ischemic brain degeneration.
Venue: Pharmacological reports PR

We review the Alzheimer related expression of genes following brain ischemia as risk factors for late onset of sporadic Alzheimer's disease and their role in Alzheimer's disease ischemia reperfusion pathogenesis. More recent advances in understanding ischemic etiology of Alzheimer's disease have revealed dysregulation of Alzheimer associated genes including amyloid protein precursor, b secretase, presenilin 1 and 2, autophagy, mitophagy and apoptosis. We review the relationship between these genes dysregulated by brain ischemia and the cellular and neuropathological characteristics of Alzheimer's disease. Here we summarize the latest studies supporting the theory that Alzheimer related genes play an important role in ischemic brain injury and that ischemia is a needful and leading supplier to the onset and progression of sporadic Alzheimer's disease. Although the exact molecular mechanisms of ischemic dependent neurodegenerative disease and neuronal susceptibility finally are unknown, a downregulated expression of neuronal defense genes like alfa secretase in the ischemic brain makes the neurons less able to resist injury. The recent challenge is to find ways to raise the adaptive reserve of the brain to overcome such ischemic associated deficits and support and/or promote neuronal survival. Understanding the mechanisms underlying the association of these genes with risk for Alzheimer's disease will provide the most meaningful targets for therapeutic development to date.
FapR: From Control of Membrane Lipid Homeostasis to a Biotechnological Tool
Venue: Front. Mol. Biosci.

Phospholipids and fatty acids are not only one of the major components of cell membranes but also important metabolic intermediates in bacteria. Since the fatty acid biosynthetic pathway is essential and energetically expensive, organisms have developed a diversity of homeostatic mechanisms to fine tune the concentration of lipids at particular levels. FapR is the first global regulator of lipid synthesis discovered in bacteria and is largely conserved in Gram positive organisms including important human pathogens, such as Staphylococcus aureus, Bacillus anthracis, and Listeria monocytogenes. FapR is a transcription factor that negatively controls the expression of several genes of the fatty acid and phospholipid biosynthesis and was first identified in Bacillus subtilis. This review focuses on the genetic, biochemical and structural advances that led to a detailed understanding of lipid homeostasis control by FapR providing unique opportunities to learn how Gram positive bacteria monitor the status of fatty acid biosynthesis and adjust the lipid synthesis accordingly. Furthermore, we also cover the potential of the FapR system as a target for new drugs against Gram positive bacteria as well as its recent biotechnological applications in diverse organisms.
Semantic Web in data mining and knowledge discovery: A comprehensive survey
Venue: J. Web Semant.

Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.
Fully C/N Polynitro Functionalized 2,2' Biimidazole Derivatives as Nitrogen and Oxygen Rich Energetic Salts.
Venue: Chemistry

Through the use of a fully C/N functionalized imidazole based anion, it was possible to prepare nitrogen and oxygen rich energetic salts. When N,N dinitramino imidazole was paired with nitrogen rich bases, versatile ionic derivatives were prepared and fully characterized by IR, and 1 H, and 13 C NMR spectroscopy and elemental analysis. Both experimental and theoretical evaluations show promising properties for these energetic compounds, such as high density, positive heats of formation, good oxygen balance, and acceptable stabilities. The energetic salts exhibit promising energetic performance comparable to the benchmark explosive RDX (1,3,5 trinitrotriazacyclohexane)
Flow The Psychology Of Optimal Experience

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An in process laser localized pre deposition heating approach to inter layer bond strengthening in extrusion based polymer additive manufacturing

based 3D printing processes have shown importance in the overall development of the field of additive manufacturing and displayed tremendous potential to becoming a cross cutting tool for research, engineering, developmental work in a wide array of disciplines. In the context of the Fused Deposition Modeling (FDM, one form of Materials extrusion based 3D printing) one of the main process issues lies in the property anisotropy of parts built using this method, even with process optimization. To address this issue, we report a near IR laser based pre deposition heating method to locally heat up the region of an existing layer near the nozzle before an extrudate comes in contact with the heated region. This in process approach raises the inter layer interface temperature to above the critical temperature to increase the interpenetrating diffusion, and therefore the inter layer bond strength. A 50% increase in the inter layer bond strength in parts built with this approach is demonstrated. The in process pre deposition local heating approach reported here represents an effective means to increase the inter layer bond strength, and property isotropy of FDM parts. Further, this approach is capable of real time monitoring and controlling of temperatures at the inter layer and inter filament interfaces across the entire volume of a built part, allowing control of the physics of the FDM process to achieve desired mechanical properties.
A deterministic approach to stochastic computation
Venue: 2016 IEEE/ACM International Conference on Computer Aided Design (ICCAD)

Stochastic logic performs computation on data represented by random bit streams. The representation allows complex arithmetic to be performed with very simple logic, but it suffers from high latency and poor precision. Furthermore, the results are always somewhat inaccurate due to random fluctuations. The random or pseudorandom sources required to generate the representation are costly, consuming a majority of the circuit area (and diminishing the overall gains in area) In this paper, we show that randomness is not a requirement for this computational paradigm. If properly structured, the same arithmetical constructs can operate on deterministic bit streams, with the data represented uniformly by the fraction of 1's versus 0's. This paper presents three approaches for the computation: relatively prime stream lengths, rotation, and clock division. The three methods are evaluated on a collection of arithmetical functions. Unlike stochastic methods, all three of our deterministic methods produce completely accurate results. The cost of generating the deterministic streams is a small fraction of the cost of generating streams from random/pseudorandom sources. Most importantly, the latency is reduced by a factor of 1/2n, where n is the equivalent number of bits of precision.
Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Automatic detection and classification of dynamic hand gestures in real world systems intended for human computer interaction is challenging as: 1) there is a large diversity in how people perform gestures, making detection and classification difficult, 2) the system must work online in order to avoid noticeable lag between performing a gesture and its classification, in fact, a negative lag (classification before the gesture is finished) is desirable, as feedback to the user can then be truly instantaneous. In this paper, we address these challenges with a recurrent three dimensional convolutional neural network that performs simultaneous detection and classification of dynamic hand gestures from multi modal data. We employ connectionist temporal classification to train the network to predict class labels from inprogress gestures in unsegmented input streams. In order to validate our method, we introduce a new challenging multimodal dynamic hand gesture dataset captured with depth, color and stereo IR sensors. On this challenging dataset, our gesture recognition system achieves an accuracy of 83:8% outperforms competing state of the art algorithms, and approaches human accuracy of 88:4% Moreover, our method achieves state of the art performance on SKIG and ChaLearn2014 benchmarks.
A Continuous Hand Gestures Recognition Technique for Human Machine Interaction Using Accelerometer and Gyroscope Sensors
Venue: IEEE Sensors Journal

Recent advances in smart devices have sustained them as a better alternative for the design of human machine interaction (HMI) because they are equipped with accelerometer sensor, gyroscope sensor, and an advanced operating system. This paper presents a continuous hand gestures recognition technique that is capable of continuous recognition of hand gestures using three axis accelerometer and gyroscope sensors in a smart device. To reduce the influence of unstableness of a hand making the gesture and compress the data, a gesture coding algorithm is developed. An automatic gesture spotting algorithm is developed to detect the start and end points of meaningful gesture segments. Finally, a gesture is recognized by comparing the gesture code with gesture database using dynamic time warping algorithm. In addition, a prototype system is developed to recognize the continuous hand gestures based HMI. With the smartphone, the user is able to perform the predefined gestures and control smart appliances using the Samsung AllShare protocol.
Long non coding RNA ANRIL regulates inflammatory responses as a novel component of NF kB pathway
Venue: RNA biology

Antisense Noncoding RNA in the INK4 Locus (ANRIL) is the prime candidate gene at Chr9p21, the well defined genetic risk locus associated with multiple human diseases including coronary artery disease (CAD) while little is known regarding its role in the pathological processes. Endothelial dysfunction triggers atherosclerotic processes that are causatively linked to CAD. To evaluate the function of ANRIL in human endothelial cells (ECs) we examined ANRIL expression under pathological stimuli and found ANRIL was markedly induced by pro inflammatory factors. Loss of function and chromatin immunoprecipitation approaches revealed that NF kB mediates TNF a induced ANRIL expression. RNA sequencing revealed that ANRIL silencing dysregulated expression of inflammatory genes including IL6 and IL8 under TNF a treatment. We explored the regulatory mechanism of ANRIL on IL6/8 and found that Yin Yang 1 (YY1) an ANRIL binding transcriptional factor revealed by RNA immunoprecipitation, was required for IL6/8 expression under TNF a treatment. YY1 was enriched at promoter loci of IL6/8 and ANRIL silencing impaired the enrichment, indicating a cooperation between ANRIL and YY1 in the regulation of inflammatory genes. For the first time, we establish the connection between ANRIL and NF kB pathway and show that ANRIL regulates inflammatory responses through binding with YY1. The newly identified TNF a NF kB ANRIL/YY1 IL6/8 pathway enhances understanding of the etiology of CAD and provides potential therapeutic target for treatment of CAD.
Data Persistence on Curriculum Management System based on MyBatis

Course of information management and development of curriculum management system is to reduce the educational task complexity and the effective ways to improve the efficiency of the educational administration work. Data persistence is an important work of software development, this article is based on MyBatis technology research and provide technical support for the system development. First of all, research by the interface layer, data processing layer and foundation support layer of MyBatis function architecture; Then, based on the Sybase database management system, curriculum information table, for example, for data storage logic structure design; Finally, to design the persistence code, including total configuration files, object mapping files and entity class object. The actual application, attention should be paid to give full play to the advantage of the MyBatis, improve the overall performance of the system and the development efficiency.
Biosynthesis of milk fat, protein, and lactose: roles of transcriptional and posttranscriptional regulation.
Venue: Physiological genomics

The demand for high quality milk is increasing worldwide. The efficiency of milk synthesis can be improved by taking advantage of the accumulated knowledge of the transcriptional and posttranscriptional regulation of genes coding for proteins involved in the synthesis of fat, protein, and lactose in the mammary gland. Research in this area is relatively new, but data accumulated in the last 10 years provide a relatively clear picture. Milk fat synthesis appears to be regulated, at least in bovines, by an interactive network between SREBP1, PPARg, and LXRa, with a potential role for other transcription factors, such as Spot14, ChREBP, and Sp1. Milk protein synthesis is highly regulated by insulin, amino acids, and amino acid transporters via transcriptional and posttranscriptional routes, with the insulin mTOR pathway playing a central role. The transcriptional regulation of lactose synthesis is still poorly understood, but it is clear that glucose transporters play an important role. They can also cooperatively interact with amino acid transporters and the mTOR pathway. Recent data indicate the possibility of nutrigenomic interventions to increase milk fat synthesis by feeding long chain fatty acids and milk protein synthesis by feeding amino acids. We propose a transcriptional network model to account for all available findings. This model encompasses a complex network of proteins that control milk synthesis with a cross talk between milk fat, protein, and lactose regulation, with mTOR functioning as a central hub.
Near Infrared Spectroscopy (NIRS) for the determination of the milk fat fatty acid profile of goats.
Venue: Food chemistry

Milk fatty acid (FA) composition is important for the goat dairy industry because of its influence on cheese properties and human health. The aim of the present work was to evaluate the feasibility of NIRS reflectance (oven dried milk using the DESIR method) and transflectance (liquid milk) analysis to predict milk FA profile and groups of fats in milk samples from individual goats. NIRS analysis of milk samples allowed to estimate FA contents and their ratios and indexes in fat with high precision and accuracy. In general, transflectance analysis gave better or similar results than reflectance mode. Interestingly, NIRS analysis allowed direct prediction of the Atherogenicity and Thrombogenicity indexes, which are useful for the interpretation of the nutritional value of goat milk. Therefore, the calibrations obtained in the present work confirm the viability of NIRS as a fast, reliable and effective analytical method to provide nutritional information of milk samples.