nils-herrmann/clean_scirepeval huggingface dataset:
Sensory transduction at the frog semicircular canal: how hair cell membrane potential controls junctional transmission

At the frog semicircular canals, the afferent fibers display high spontaneous activity (mEPSPs), due to transmitter release from hair cells. mEPSP and spike frequencies are modulated by stimulation that activates the hair cell receptor conductance. The relation between receptor current and transmitter release cannot be studied at the intact semicircular canal. To circumvent the problem, we combined patch-clamp recordings at the isolated hair cell and electrophysiological recordings at the cytoneural junction in the intact preparation. At isolated hair cells, the K channel blocker tetraethylammonium (TEA) is shown to block a fraction of total voltage-dependent K-conductance (IKD) that depends on TEA concentration but not on membrane potential (V m). Considering the bioelectric properties of the hair cell, as previously characterized by this lab, a fixed fractional block of IKD is shown to induce a relatively fixed shift in V m, provided it lies in the range -30 to -10 mV. The same concentrations of TEA were applied to the intact labyrinth while recording from single afferent fibers of the posterior canal, at rest and during mechanical stimulation. At the peak of stimulation, TEA produced increases in mEPSP rate that were linearly related to the shifts produced by the same TEA concentrations (0.1-3 mM) in hair cell V m (0.7-5 mV), with a slope of 29.8 Hz/mV. The membrane potential of the hair cell is not linearly related to receptor conductance, so that the slope of quantal release vs. receptor conductance depends on the prevailing V m (19.8 Hz/nS at -20 mV; 11 Hz/nS at -10 mV). Changes in mEPSP peak size were negligible at rest as well as during stimulation. Since ample spatial summation of mEPSPs occurs at the afferent terminal and threshold-governed spike firing is intrinsically nonlinear, the observed increases in mEPSP frequency, though not very large, may suffice to trigger afferent spike discharge.
Local Monotonic Attention Mechanism for End-to-End Speech And Language Processing

Recently, encoder-decoder neural networks have shown impressive performance on many sequence-related tasks. The architecture commonly uses an attentional mechanism which allows the model to learn alignments between the source and the target sequence. Most attentional mechanisms used today is based on a global attention property which requires a computation of a weighted summarization of the whole input sequence generated by encoder states. However, it is computationally expensive and often produces misalignment on the longer input sequence. Furthermore, it does not fit with monotonous or left-to-right nature in several tasks, such as automatic speech recognition (ASR), grapheme-to-phoneme (G2P), etc. In this paper, we propose a novel attention mechanism that has local and monotonic properties. Various ways to control those properties are also explored. Experimental results on ASR, G2P and machine translation between two languages with similar sentence structures, demonstrate that the proposed encoderdecoder model with local monotonic attention could achieve significant performance improvements and reduce the computational complexity in comparison with the one that used the standard global attention architecture.
Unsupervised soccer video abstraction based on pitch, dominant color and camera motion analysis

We present a soccer video abstraction method based on the analysis of the audio and video streams. This method could be applied to other sports as rugby or american football. The main contribution of this paper is the design of an unsupervised summarization method, and more specifically, the introduction of an efficient detector of excited speech segments. An excited commentary is supposed to correspond to an interesting moment of the game. It is simultaneously characterized by an increase of the pitch (or fundamental frequency) within the voiced segments and an increase of the energy supported by the harmonics of the pitch. The pitch is estimated from the autocorrelation function and its local increases are detected from a multiresolution technique. We introduce a specific energy measure for the voiced segments. A statistical analysis of the energy measures is performed to detect the most excited parts of the speech. A deterministic combination of excited speech detection, dominant color identification and camera motion analysis is then performed in order to discriminate between excited speech sequences of the game and excited speech sequences in commercials or in studio shots included in the processed TV programs.
 The method presented here does not need any learning stage. It has been tested on seven soccer videos for a total duration of almost 20 hours.
Quorum Sensing Circuit and Reactive Oxygen Species Resistance in Deinococcus sp.

Genus Deinococcus is characterized by an increased resistance toward reactive oxygen species (ROS). The chromosome of five strains belonging to this genus has been sequenced and the presence of a luxS-like gene was deduced from their genome sequences. The aim of this study was to assess if a complete QS circuit is present in Deinococcus sp. and if this QS is associated with ROS. Primers for searching luxS-like gene and the putative receptor gene, namely ai2R, were designed. AI-2 signal production was evaluated by luminescence analysis using Vibrio harveyi BB170 as reporter strain. AI-2 signal was also evaluated by competitive assays using cinnamaldehyde, ascorbic acid, and 3-mercaptopropionic acid as interfering molecules. Potassium tellurite and metronidazole were used as oxidative stressors. A luxS-like gene as well as an ai2R gene was detected in strain UDEC-P1 by PCR. Cell-free supernatant of strain UDEC-P1 culture induced luminescence in V. harveyi BB170, and this property was inhibited with the three interfering molecules. The oxidative stressors metronidazole and potassium tellurite decreased Deinococcus sp. viability, but increased luminescence of the reporter strain. The results demonstrate that both a functional luxS-like gene and a putative receptor for AI-2 signal are present in Deinococcus sp. strain UDEC-P1. This finding also suggests that a complete QS circuit is present in this genus, which could be related to oxidative stress.
Topic Aware Neural Response Generation

We consider incorporating topic information into a sequenceto-sequence framework to generate informative and interesting responses for chatbots. To this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model. The model utilizes topics to simulate prior human knowledge that guides them to form informative and interesting responses in conversation, and leverages topic information in generation by a joint attention mechanism and a biased generation probability. The joint attention mechanism summarizes the hidden vectors of an input message as context vectors by message attention and synthesizes topic vectors by topic attention from the topic words of the message obtained from a pre-trained LDA model, with these vectors jointly affecting the generation of words in decoding. To increase the possibility of topic words appearing in responses, the model modifies the generation probability of topic words by adding an extra probability item to bias the overall distribution. Empirical studies on both automatic evaluation metrics and human annotations show that TA-Seq2Seq can generate more informative and interesting responses, significantly outperforming state-of-theart response generation models.
Hand Gesture Recognition With Multiscale Weighted Histogram of Contour Direction Normalization for Wearable Applications

This paper proposes a static hand gesture recognition method with low computation and memory consumption for wearable applications. The hand contour is chosen as the hand gesture feature and support vector machine is used to classify the feature. A multiscale weighted histogram of contour direction-based direction normalization is proposed to ensure good recognition performance. In order to improve efficiency, the proposed histogram only counts the direction of the contour point to focus on the most significant hand feature in the first-person view of wearable devices. Based on the hand’s anatomy, the proposed histogram is weighted by considering each contour point’s position and direction jointly using the direction-angle map, to ensure robustness. Experimental results show that the proposed method can give a recognition accuracy of 97.1% with a frame rate of 30 fps on a PC.
Attributed based access control (ABAC) for Web services

For companies and government agencies alike, the emergence of Web services technologies and the evolution of distributed systems toward service oriented architectures (SOA) have helped promote collaboration and information sharing by breaking down "stove-piped" systems and connecting them via loosely coupled, interoperable system-to-system interfaces. Such architectures, however, also bring about their own security challenges that require due consideration. Unfortunately, the current information security mechanisms are insufficient to address these challenges. In particular, the access control models today are mostly static and coarsely grained; they are not well-suited for the service-oriented environments where information access is dynamic and ad-hoc in nature. This paper outlines the access control challenges for Web services and SOA, and proposes an attribute based access control (ABAC) model as a new approach, which is based on subject, object, and environment attributes and supports both mandatory and discretionary access control needs. The paper describes the ABAC model in terms of its authorization architecture and policy formulation, and makes a detailed comparison between ABAC and traditional role-based models, which clearly shows the advantages of ABAC. The paper then describes how this new model can be applied to securing Web service invocations, with an implementation based on standard protocols and open-source tools. The paper concludes with a summary of the ABAC model's benefits and some future directions.
LIMERIC: a linear message rate control algorithm for vehicular DSRC systems

Wireless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g. a vehicle's speed and location, or a traffic signal's state and timing). The aggregate of these uncoordinated broadcasts will lead to channel congestion under dense traffic scenarios, with a resulting reduction in the effectiveness of the collision avoidance applications making use of the transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology for deployment. This paper defines a new congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This "perfect" convergence avoids the limit cycle behavior inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including: guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust stability under all conditions, convergence with asynchronous updates, and the implications of measurement noise for statistical properties of convergence. The paper illustrates key analytical results using MATLAB numerical results, and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high density scenarios.
The biology of cancer: metabolic reprogramming fuels cell growth and proliferation.

Cell proliferation requires nutrients, energy, and biosynthetic activity to duplicate all macromolecular components during each passage through the cell cycle. It is therefore not surprising that metabolic activities in proliferating cells are fundamentally different from those in nonproliferating cells. This review examines the idea that several core fluxes, including aerobic glycolysis, de novo lipid biosynthesis, and glutamine-dependent anaplerosis, form a stereotyped platform supporting proliferation of diverse cell types. We also consider regulation of these fluxes by cellular mediators of signal transduction and gene expression, including the phosphatidylinositol 3-kinase (PI3K)/Akt/mTOR system, hypoxia-inducible factor 1 (HIF-1), and Myc, during physiologic cell proliferation and tumorigenesis.
A study on peer startup process and initial offset placement in P2P live streaming systems

In this paper, we measure and study the peer startup process in PPLive, a popular commercial P2P streaming system, and focus on a fundamental issue in this aspect: how a peer initializes its buffer when it joins a channel, i.e., initial offset placement of peers' buffers in the startup stage. We build a general model of peer startup process in chunk-based P2P streaming systems and present an initial offset placement scheme we inferred from the measurement results, i.e., proportional placement (PP) scheme. With FP scheme, the initial buffer offset is set to the offset of the reference neighbor peer plus an advance proportional to the reference neighbor peer's offset lag or buffer width. We evaluate the performance of PP scheme and find it is stable when the placement is based on offset lag, but will be unstable when it is based on buffer width if the chunk fetching strategy and neighbor peer selection mechanism are not properly designed. We finally report our detailed measurement results of the peer startup process and initial offset placement algorithms used in PPLive. Our models and measurement results could be useful for guiding the analysis and design of buffering protocols for a real P2P live streaming system.
Appropriate blood pressure control in hypertensive and normotensive type 2 diabetes mellitus: a summary of the ABCD trial

The hypertensive and normotensive Appropriate Blood Pressure Control in Diabetes (ABCD) studies were prospective, randomized, interventional clinical trials with 5 years of follow-up that examined the role of intensive versus standard blood pressure control in a total of 950 patients with type 2 diabetes mellitus. In the hypertensive ABCD study, a significant decrease in mortality was detected in the intensive blood pressure control group when compared with the standard blood pressure control group. There was also a marked reduction in the incidence of myocardial infarction when patients were randomly assigned to initial antihypertensive therapy with angiotensin-converting-enzyme inhibition rather than calcium channel blockade. The results of the normotensive ABCD study included associations between intensive blood pressure control and significant slowing of the progression of nephropathy (as assessed by urinary albumin excretion) and retinopathy, and fewer strokes. In both the hypertensive and normotensive studies, mean renal function (as assessed by 24 h creatinine clearance) remained stable during 5 years of either intensive or standard blood pressure intervention in patients with normoalbuminuria (<30 mg/24 h) or microalbuminuria (30–300 mg/24 h) at baseline. By contrast, the rate of creatinine clearance in patients with overt diabetic nephropathy (>300 mg/24 h; albuminuria) at baseline decreased by an average of 5 ml/min/year in spite of either intensive or standard blood pressure control. Analysis of the results of 5 years of follow-up revealed a highly significant correlation of all-cause and cardiovascular mortality with left ventricular mass and severity of albuminuria.
Medical Image Retrieval: A Multimodal Approach

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system.
Generalized formula for the calculation of a probabilistic metric for random hardware failures in redundant subsystems

The introduction of International Organization for Standardization (ISO) standard 26262 “Road vehicles — Functional safety” in 2011 provided a state-of-the-art methodology for achieving functional safety in automotive electrical and/or electronic (E/E) systems. The standard defines the probabilistic metric for random hardware failures (PMHF) as the average probability of a violation of a safety goal associated with a failure over a vehicle's lifetime and architecture metrics. Although a PMHF formula is provided, the explanation of the standard is insufficient. In this paper, we propose a method to calculate the PMHF and expand the application to redundant subsystems that are not adequately described in the standard.
Improved musical onset detection with Convolutional Neural Networks

Musical onset detection is one of the most elementary tasks in music analysis, but still only solved imperfectly for polyphonic music signals. Interpreted as a computer vision problem in spectrograms, Convolutional Neural Networks (CNNs) seem to be an ideal fit. On a dataset of about 100 minutes of music with 26k annotated onsets, we show that CNNs outperform the previous state-of-the-art while requiring less manual preprocessing. Investigating their inner workings, we find two key advantages over hand-designed methods: Using separate detectors for percussive and harmonic onsets, and combining results from many minor variations of the same scheme. The results suggest that even for well-understood signal processing tasks, machine learning can be superior to knowledge engineering.
An integrated approach for warehouse analysis and optimization: A case study

The paper focuses on the analysis and optimization of production warehouses, proposing a novel approach to reduce inefficiencies which employs three lean manufacturing tools in an integrated and iterative framework. The proposed approach integrates the Unified Modeling Language (UML) – providing a detailed description of the warehouse logistics – the Value Stream Mapping (VSM) tool – identifying non-value adding activities – and a mathematical formulation of the so-called Genba Shikumi philosophy – ranking such system anomalies and assessing how they affect the warehouse. The subsequent reapplication of the VSM produces a complete picture of the reengineered warehouse, and using the UML tool allows describing in detail the updated system. By applying the presented methodology to the warehouse of an Italian interior design producer, we show that it represents a useful tool to systematically and dynamically improve the warehouse management. Indeed, the application of the approach to the company leads to an innovative proposal for the warehouse analysis and optimization: a warehouse management system that leads to increased profitability and quality as well as to reduced errors.
A Semantic-Aware Profile Updating Model for Text Recommendation

Content-based recommender systems (CBRSs) rely on user-item similarities that are calculated between user profiles and item representations. Appropriate representation of each user profile based on the user's past preferences can have a great impact on user's satisfaction in CBRSs. In this paper, we focus on text recommendation and propose a novel profile updating model based on previously recommended items as well as semantic similarity of terms calculated using distributed representation of words. We evaluate our model using two standard text recommendation datasets: TREC-9 Filtering Track and CLEF 2008-09 INFILE Track collections. Our experiments investigate the importance of both past recommended items and semantic similarities in recommendation performance. The proposed profile updating method significantly outperforms the baselines, which confirms the importance of incorporating semantic similarities in the profile updating task.