nils-herrmann/clean_scirepeval huggingface dataset:
A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEGs), electrooculograms (EOGs), electrocardiograms, and electromyograms (EMGs). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting handcrafted features, that exploits all multivariate and multimodal polysomnography (PSG) signals (EEG, EMG, and EOG), and that can exploit the temporal context of each 30-s window of data. For each modality, the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields the state-of-the-art performance. Our study reveals a number of insights on the spatiotemporal distribution of the signal of interest: a good tradeoff for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting 1 min of data before and after each data segment offers the strongest improvement when a limited number of channels are available. As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver the state-of-the-art classification performance with a small computational cost.
Competition between functional brain networks mediates behavioral variability

Increased intraindividual variability (IIV) is a hallmark of disorders of attention. Recent work has linked these disorders to abnormalities in a "default mode" network, comprising brain regions routinely deactivated during goal-directed cognitive tasks. Findings from a study of the neural basis of attentional lapses suggest that a competitive relationship between the "task-negative" default mode network and regions of a "task-positive" attentional network is a potential locus of dysfunction in individuals with increased IIV. Resting state studies have shown that this competitive relationship is intrinsically represented in the brain, in the form of a negative correlation or antiphase relationship between spontaneous activity occurring in the two networks. We quantified the negative correlation between these two networks in 26 subjects, during active (Eriksen flanker task) and resting state scans. We hypothesized that the strength of the negative correlation is an index of the degree of regulation of activity in the default mode and task-positive networks and would be positively related to consistent behavioral performance. We found that the strength of the correlation between the two networks varies across individuals. These individual differences appear to be behaviorally relevant, as interindividual variation in the strength of the correlation was significantly related to individual differences in response time variability: the stronger the negative correlation (i.e., the closer to 180 degrees antiphase), the less variable the behavioral performance. This relationship was moderately consistent across resting and task conditions, suggesting that the measure indexes moderately stable individual differences in the integrity of functional brain networks. We discuss the implications of these findings for our understanding of the behavioral significance of spontaneous brain activity, in both healthy and clinical populations.
Main memory organization trade-offs with DRAM and STT-MRAM options based on gem5-NVMain simulation frameworks

Current main memory organizations in embedded and mobile application systems are DRAM dominated. The ever-increasing gap between today's processor and memory speeds makes the DRAM subsystem design a major aspect of computer system design. However, the limitations to DRAM scaling and other challenges like refresh provide undesired trade-offs between performance, energy and area to be made by architecture designers. Several emerging NVM options are being explored to at least partly remedy this but today it is very hard to assess the viability of these proposals because the simulations are not fully based on realistic assumptions on the NVM memory technologies and on the system architecture level. In this paper, we propose to use realistic, calibrated STT-MRAM models and a well calibrated cross-layer simulation and exploration framework, named SEAT, to better consider technologies aspects and architecture constraints. We will focus on general purpose/mobile SoC multi-core architectures. We will highlight results for a number of relevant benchmarks, representatives of numerous applications based on actual system architecture. The most energy efficient STT-MRAM based main memory proposal provides an average energy consumption reduction of 27% at the cost of 2x the area and the least energy efficient STT-MRAM based main memory proposal provides an average energy consumption reduction of 8% at the around the same area or lesser when compared to DRAM.
REX-J: Japanese referring expression corpus of situated dialogs

Identifying objects in conversation is a fundamental human capability necessary to achieve efficient collaboration on any real world task. Hence the deepening of our understanding of human referential behaviour is indispensable for the creation of systems that collaborate with humans in a meaningful way. We present the construction of REX-J, a multi-modal Japanese corpus of referring expressions in situated dialogs, based on the collaborative task of solving the Tangram puzzle. This corpus contains 24 dialogs with over 4 hours of recordings and over 1400 referring expressions. We outline the characteristics of the collected data and point out the important differences from previous corpora. The corpus records extra-linguistic information during the interaction (e.g. the position of pieces, the actions on the pieces) in synchronization with the participants’ utterances. This in turn allows us to discuss the importance of creating a unified model of linguistic and extralinguistic information from a new perspective. Demonstrating the potential uses of this corpus, we present the analysis of a specific type of referring expression (“action-mentioning expression”) as well as the results of research into the generation of demonstrative pronouns. Furthermore, we discuss some perspectives on potential uses of this corpus as well as our planned future work, underlining how it is a valuable addition to the existing databases in the community for the study and modeling of referring expressions in situated dialog.
Prospective study on the association between diet quality and depression in mid-aged women over 9 years.

To examine the longitudinal association between diet quality and depression using prospective data from the Australian Longitudinal Study on Women's Health.



Women born in 1946-1951 (n = 7877) were followed over 9 years starting from 2001. Dietary intake was assessed using the Dietary Questionnaire for Epidemiological Studies (version 2) in 2001 and a shortened form in 2007 and 2010. Diet quality was summarised using the Australian Recommended Food Score. Depression was measured using the 10-item Centre for Epidemiologic Depression Scale and self-reported physician diagnosis. Pooled logistic regression models including time-varying covariates were used to examine associations between diet quality tertiles and depression. Women were also categorised based on changes in diet quality during 2001-2007. Analyses were adjusted for potential confounders.



The highest tertile of diet quality was associated marginally with lower odds of depression (OR 0.94; 95 % CI 0.83, 1.00; P = 0.049) although no significant linear trend was observed across tertiles (OR 1.00; 95 % CI 0.94, 1.10; P = 0.48). Women who maintained a moderate or high score over 6 years had a 6-14 % reduced odds of depression compared with women who maintained a low score (moderate vs low score-OR 0.94; 95 % CI 0.80, 0.99; P = 0.045; high vs low score-OR 0.86; 95 % CI 0.77, 0.96; P = 0.01). Similar results were observed in analyses excluding women with prior history of depression.



Long-term maintenance of good diet quality may be associated with reduced odds of depression. Randomised controlled trials are needed to eliminate the possibility of residual confounding.
Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain

Cities across the United States are implementing information communication technologies in an effort to improve government services. One such innovation in e-government is the creation of 311 systems, offering a centralized platform where citizens can request services, report non-emergency concerns, and obtain information about the city via hotline, mobile, or web-based applications. The NYC 311 service request system represents one of the most significant links between citizens and city government, accounting for more than 8,000,000 requests annually. These systems are generating massive amounts of data that, when properly managed, cleaned, and mined, can yield significant insights into the real-time condition of the city. Increasingly, these data are being used to develop predictive models of citizen concerns and problem conditions within the city. However, predictive models trained on these data can suffer from biases in the propensity to make a request that can vary based on socio-economic and demographic characteristics of an area, cultural differences that can affect citizens’ willingness to interact with their government, and differential access to Internet connectivity. Using more than 20,000,000 311 requests together with building violation data from the NYC Department of Buildings and the NYC Department of Housing Preservation and Development; property data from NYC Department of City Planning; and demographic and socioeconomic data from the U.S. Census American Community Survey we develop a two-step methodology to evaluate the propensity to complain: (1) we predict, using a gradient boosting regression model, the likelihood of heating and hot water violations for a given building, and (2) we then compare the actual complaint volume for buildings with predicted violations to quantify discrepancies across the City. Our model predicting service request volumes over time will contribute to the efficiency of the 311 system by informing shortand long-term resource allocation strategy and improving the agency’s performance in responding to requests. For instance, the outcome of our longitudinal pattern analysis allows the city to predict building safety hazards early and take action, leading to anticipatory safety and inspection actions. Furthermore, findings will provide novel insight into equity and community engagement through 311, and provide the basis for acknowledging and accounting for Bloomberg Data for Good Exchange Conference. 24-Sep-2017, Chicago, IL, USA. bias in machine learning applications trained on 311 data.
Buck converter MPPT control of a photovoltaic system

This work concerns the behavior study of a MPPT algorithm based on the incremental conductance. An open loop analysis of a photovoltaic chain, modeled in matlab-simulink, allows to extract the maximum power of the photovoltaic panel. The principle is based on the determination instantaneously of the conductance and its tendency materialized by a signed increment. A buck step down converter is used to adapt the voltage to its appropriate value to reach a maximal power extraction. This novel analysis method applied to the photovoltaic system is made under different atmospheric parameters. The performances are shown using Matlab/Simulink software.
Fair Adaptive Data Rate Allocation and Power Control in LoRaWAN

In this paper, we present results of a study of the data rate fairness among nodes within a LoRaWAN cell. Since LoRa/LoRaWAN supports various data rates, we firstly derive the fairest ratios of deploying each data rate within a cell for a fair collision probability. LoRa/LoRaWan, like other frequency modulation based radio interfaces, exhibits the capture effect in which only the stronger signal of colliding signals will be extracted. This leads to unfairness, where far nodes or nodes experiencing higher attenuation are less likely to see their packets received correctly. Therefore, we secondly develop a transmission power control algorithm to balance the received signal powers from all nodes regardless of their distances from the gateway for a fair data extraction. Simulations show that our approach achieves higher fairness in data rate than the state-of-art in almost all network configurations.
Precise Detection of Memory Leaks

A memory leak occurs when a program allocates a block of memory, but does not release it after its last use. In case such a block is still referenced by one or more reachable pointers at the end of the execution, fixing the leak is often quite simple as long as it is known where the block was allocated. If, however, all references to the block are overwritten or lost during the program’s execution, only knowing the allocation site is not enough in most cases. This paper describes an approach based on dynamic instrumentation and garbage collection techniques, which enables us to also inform the user about where the last reference to a lost memory block was created and where it was lost, without the need for recompilation or relinking.
Indoor mapping using gmapping on embedded system

In recent years, the core technology enable the rapid rise of autonomous vehicles like the solving the Simultaneous Localization And Mapping (SLAM) problem on the embedded system. And the gmapping package on the ROS platform providing laser-based SLAM is widely used and studied. However, implementing it on embedded system imposes two challenges to us: running ROS requires large CPU consumption and the algorithm of SLAM is computationally intensive. In this paper, we present a system implementation of the gmapping on an ARM based embedded hardware not installing ROS and we also improve the architecture of the mapping system by parallel execution of mapping and estimating pose. The mapping process is running on the embedded system and it can create a 2-D occupancy map from laser and pose data collected by a mobile robot with low CPU load and time consumption. Experimental results carried out with mobile robots in indoor environments illustrate the accuracy of our implementation and the low consumption of time and CPU load of the mapping system.
Proposal for CSE 190 Spring 2011 : Identifying the Cuisine of a Plate of Food Mengzi Zhang

Food is an indispensible part of our lives. In today’s globalized market, food from different geographical regions show remarkable variations in the choice of ingredients and the ways to prepare them. Some cuisines have ingredient choices unimaginable to customers habituated to other cuisines, but still present surprisingly tasty dishes. Besides unique ingredients, even the same ingredients, depending on the preparation process, may end up preserving different fractions of the nutritional value and very different calories. If restaurant networks provide a mobile app that can aim at a photograph of a plate of food and report its cuisine, the composing ingredients, and list other similar dishes, it could be a good marketing strategy. In addition, restaurants of a specific cuisine can also refer to other cuisines’ use of ingredients to sparkle ideas for more creative dishes that utilize similar ingredients in new ways. One core component of such an application is to recognize the cuisine and ingredients on a plate of food. Then the information may later be used to compare cuisines and find a network of common ingredients in the dishes.
Intelligent phishing website detection using random forest classifier

Phishing is defined as mimicking a creditable company's website aiming to take private information of a user. In order to eliminate phishing, different solutions proposed. However, only one single magic bullet cannot eliminate this threat completely. Data mining is a promising technique used to detect phishing attacks. In this paper, an intelligent system to detect phishing attacks is presented. We used different data mining techniques to decide categories of websites: legitimate or phishing. Different classifiers were used in order to construct accurate intelligent system for phishing website detection. Classification accuracy, area under receiver operating characteristic (ROC) curves (AUC) and F-measure is used to evaluate the performance of the data mining techniques. Results showed that Random Forest has outperformed best among the classification methods by achieving the highest accuracy 97.36%. Random forest runtimes are quite fast, and it can deal with different websites for phishing detection.
Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

Along with the improvement of radar technologies Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programing, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is utilize our multiradar technique and determine the optimal number of radars needed to detect and classify targets.
Molecular alterations of cancer cell and tumour microenvironment in metastatic gastric cancer

The term metastasis is widely used to describe the endpoint of the process by which tumour cells spread from the primary location to an anatomically distant site. Achieving successful dissemination is dependent not only on the molecular alterations of the cancer cells themselves, but also on the microenvironment through which they encounter. Here, we reviewed the molecular alterations of metastatic gastric cancer (GC) as it reflects a large proportion of GC patients currently seen in clinic. We hope that further exploration and understanding of the multistep metastatic cascade will yield novel therapeutic targets that will lead to better patient outcomes.
Resource Use Efficiency in Indian Agriculture

The term ‘resource use efficiency in agriculture’ may be broadly defined to include the concepts of technical efficiency, allocative efficiency and environmental efficiency. An efficient farmer allocates his land, labour, water and other resources in an optimal manner, so as to maximise his income, at least cost, on sustainable basis. However, there are countless studies showing that farmers often use their resources sub-optimally. While some farmers may attain maximum physical yield per unit of land at a high cost, some others achieve maximum profit per unit of inputs used. Also in the process of achieving maximum yield and returns, some farmers may ignore the environmentally adverse consequences, if any, of their resource use intensity. Logically all enterprising farmers would try to maximise their farm returns by allocating resources in an efficient manner. But as resources (both qualitatively and quantitatively) and managerial efficiency of different farmers vary widely, the net returns per unit of inputs used also vary significantly from farm to farm. Also a farmer’s access to technology, credit, market and other infrastructure and policy support, coupled with risk perception and risk management capacity under erratic weather and price situations would determine his farm efficiency. Moreover, a farmer knowingly or unknowingly may over-exploit his land and water resources for maximising farm income in the short run, thereby resulting in soil and water degradation and rapid depletion of ground water, and also posing a problem of sustainability of agriculture in the long run. In fact, soil degradation, depletion of groundwater and water pollution due to farmers’ managerial inefficiency or otherwise, have a social cost, while farmers who forego certain agricultural practices which cause any such sustainability problem may have a high opportunity cost. Furthermore, a farmer may not be often either fully aware or properly guided and aided for alternative, albeit best possible uses of his scarce resources like land and water. Thus, there are economic as well as environmental aspects of resource use efficiency. In addition, from the point of view of public exchequer, the resource use efficiency would mean that public investment, subsidies and credit for agriculture are
Logic-Based Question Answering

Question answering systems aim to provide concise and correct responses to arbitrary questions, communicating with the user in a natural language. This way they help making the knowledge of large textual sources accessible in an intuitive manner which goes beyond the capabilities of conventional search engines. In the LogAnswer project the universities of Hagen and Koblenz cooperate to build a German language question answering system which combines computational linguistics and automated reasoning to deduce answers from a knowledge base derived from Wikipedia.
Ruptured silicone breast implant: a misleading chest X-ray.

A 55 year old female was referred for investigation and possible surgery of a thyroid swelling. She had smoked 30 cigarettes per day for many years. Past medical history consisted of insertion of bilateral silicone breast implants 10 years previously. Clinical examination suggested a multinodular goitre and identified slight thickening superior to the left breast implant. Investigations revealed normal blood tests, and a multinodular goitre was confirmed on ultrasound scan. Routine chest X-ray  identified an opacity in the left upper lobe showing features suggestive of a primary lung tumour. However, a lateral view failed to detect an abnormality in the thoracic cavity. CT scanning, performed with a view to percutaneous biopsy, revealed that the "lung tumour" was in fact related to the silicone implant . Subsequent surgery confirmed rupture of the left breast prosthesis.
A Very Brief and Critical Discussion on AutoML

This contribution presents a very brief and critical discussion on automated machine learning (AutoML), which is categorized here into two classes, referred to as narrow AutoML and generalized AutoML, respectively. The conclusions yielded from this discussion can be summarized as follows: (1) most existent research on AutoML belongs to the class of narrow AutoML; (2) advances in narrow AutoML are mainly motivated by commercial needs, while any possible benefit obtained is definitely at a cost of increase in computing burdens; (3)the concept of generalized AutoML has a strong tie in spirit with artificial general intelligence (AGI), also called “strong AI”, for which obstacles abound for obtaining pivotal progresses.