nils-herrmann/clean_scirepeval huggingface dataset:
ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification

Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians, and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and the rate at which new data becomes available make manual interpretation a time-consuming, resource-hungry, and expensive process. In contrast, automated analysis of EEG data offers the potential to improve the quality of patient care by shortening the time to diagnosis and reducing manual error. In this paper, we focus on one of the first steps in interpreting an EEG session identifying whether the brain activity is abnormal or normal. To address this specific task, we propose a novel recurrent neural network (RNN) architecture termed ChronoNet which is inspired by recent developments from the field of image classification and designed to work efficiently with EEG data. ChronoNet is formed by stacking multiple 1D convolution layers followed by deep gated recurrent unit (GRU) layers where each 1D convolution layer uses multiple filters of exponentially varying lengths and the stacked GRU layers are densely connected in a feed-forward manner. We used the recently released TUH Abnormal EEG Corpus dataset for evaluating the performance of ChronoNet. Unlike previous studies using this dataset, ChronoNet directly takes time-series EEG as input and learns meaningful representations of brain activity patterns. ChronoNet outperforms previously reported results on this dataset thereby setting a new benchmark. Furthermore, we demonstrate the domain-independent nature of ChronoNet by successfully applying it to classify speech commands.
Model-Free Deep Inverse Reinforcement Learning by Logistic Regression

This paper proposes model-free deep inverse reinforcement learning to find nonlinear reward function structures. We formulate inverse reinforcement learning as a problem of density ratio estimation, and show that the log of the ratio between an optimal state transition and a baseline one is given by a part of reward and the difference of the value functions under the framework of linearly solvable Markov decision processes. The logarithm of density ratio is efficiently calculated by binomial logistic regression, of which the classifier is constructed by the reward and state value function. The classifier tries to discriminate between samples drawn from the optimal state transition probability and those from the baseline one. Then, the estimated state value function is used to initialize the part of the deep neural networks for forward reinforcement learning. The proposed deep forward and inverse reinforcement learning is applied into two benchmark games: Atari 2600 and Reversi. Simulation results show that our method reaches the best performance substantially faster than the standard combination of forward and inverse reinforcement learning as well as behavior cloning.
Manufacturing flexibility and business strategy : an empirical study of small and medium sized firms

This study investigates the practice of manufacturing flexibility in small and medium sized firms. Using the data collected from 87 firms from machinery and machine tool industries in Taiwan, we analyzed and prescribed the alignment of various manufacturing flexibility dimensions with business strategies. Several practical approaches to developing manufacturing flexibility in small and medium sized firms were discussed. In addition, statistical results indicate that the compatibility between business strategy and manufacturing flexibility is critical to business performance. The one-to-one relationship between business strategy and manufacturing flexibility is established to enable managers to set clear priorities in investing and developing necessary manufacturing flexibility. r 2002 Elsevier Science B.V. All rights reserved.
An automotive on-board 3.3 kW battery charger for PHEV application

An on-board charger is responsible for charging the battery pack in a plug-in hybrid electric vehicle (PHEV). In this paper, a 3.3kW two stage battery charger design is presented for a PHEV application. The objective of the design is to achieve high efficiency, which is critical to minimize the charger size, charging time and the amount and cost of electricity drawn from the utility. The operation of the charger power converter configuration is provided in addition to a detailed design procedure. The mechanical packaging design and key experimental results are provided to verify the suitability of the proposed charger power architecture.
Nutrition, Diet and Cancer

Of late, flavonoids, a class of polyphenolic compounds ubiquitously present in the human diet have gained increasing attention in cancer prevention. Defining the anti-cancer mechanisms of quercetin, a major dietary flavonoid has R.V. Priyadarsini • S. Nagini ( ) Department of Biochemistry and Biotechnology, Faculty of Science, Annamalai University, Annamalainagar 608 002, Tamil Nadu, India e-mail: s nagini@yahoo.com; snlabau@gmail.com S. Shankar and R.K. Srivastava (eds.), Nutrition, Diet and Cancer, DOI 10.1007/978-94-007-2923-0 1,  2012 1 2 R.V. Priyadarsini and S. Nagini been the topic of intense research over the last two decades. Evidences from experimental studies have shown that quercetin not only offers protection against chemically induced cancers but also suppresses the growth of cancer cells in vitro and in vivo by enhancing carcinogen detoxification and antioxidant defences, inducing cell cycle arrest and apoptosis, inhibiting matrix invasion and angiogenesis, and modulating intracellular signalling circuits. Epidemiological studies across different populations have also indicated that quercetin intake is associated with reduced risk of various cancers. This review summarizes the preclinical and clinical data on the anti-cancer effects of quercetin, the key molecular mechanisms of action, its synergistic interactions, and adverse side effects to warrant further clinical evaluation of quercetin for cancer prevention and therapy.
Soy isoflavones in the prevention of menopausal bone loss and menopausal symptoms: a randomized, double-blind trial.

Concerns regarding the risk of estrogen replacement have resulted in a significant increase in the use of soy products by menopausal women who, despite the lack of evidence of the efficacy of such products, seek alternatives to menopausal hormone therapy. Our goal was to determine the efficacy of soy isoflavone tablets in preventing bone loss and menopausal symptoms.



The study design was a single-center, randomized, placebo-controlled, double-blind clinical trial conducted from July 1, 2004, through March 31, 2009. Women aged 45 to 60 years within 5 years of menopause and with a bone mineral density T score of -2.0 or higher in the lumbar spine or total hip were randomly assigned, in equal proportions, to receive daily soy isoflavone tablets, 200 mg, or placebo. The primary outcome was changes in bone mineral density in the lumbar spine, total hip, and femoral neck at the 2-year follow-up. Secondary outcomes included changes in menopausal symptoms, vaginal cytologic characteristics, N -telopeptide of type I bone collagen, lipids, and thyroid function.



After 2 years, no significant differences were found between the participants receiving soy tablets (n = 122) and those receiving placebo (n = 126) regarding changes in bone mineral density in the spine (-2.0% and -2.3%, respectively), the total hip (-1.2% and -1.4%, respectively), or the femoral neck (-2.2% and -2.1%, respectively). A significantly larger proportion of participants in the soy group experienced hot flashes and constipation compared with the control group. No significant differences were found between groups in other outcomes.



In this population, the daily administration of tablets containing 200 mg of soy isoflavones for 2 years did not prevent bone loss or menopausal symptoms.
The relation between inflammation and neurodegeneration in multiple sclerosis brains

Some recent studies suggest that in progressive multiple sclerosis, neurodegeneration may occur independently from inflammation. The aim of our study was to analyse the interdependence of inflammation, neurodegeneration and disease progression in various multiple sclerosis stages in relation to lesional activity and clinical course, with a particular focus on progressive multiple sclerosis. The study is based on detailed quantification of different inflammatory cells in relation to axonal injury in 67 multiple sclerosis autopsies from different disease stages and 28 controls without neurological disease or brain lesions. We found that pronounced inflammation in the brain is not only present in acute and relapsing multiple sclerosis but also in the secondary and primary progressive disease. T- and B-cell infiltrates correlated with the activity of demyelinating lesions, while plasma cell infiltrates were most pronounced in patients with secondary progressive multiple sclerosis (SPMS) and primary progressive multiple sclerosis (PPMS) and even persisted, when T- and B-cell infiltrates declined to levels seen in age matched controls. A highly significant association between inflammation and axonal injury was seen in the global multiple sclerosis population as well as in progressive multiple sclerosis alone. In older patients (median 76 years) with long-disease duration (median 372 months), inflammatory infiltrates declined to levels similar to those found in age-matched controls and the extent of axonal injury, too, was comparable with that in age-matched controls. Ongoing neurodegeneration in these patients, which exceeded the extent found in normal controls, could be attributed to confounding pathologies such as Alzheimer's or vascular disease. Our study suggests a close association between inflammation and neurodegeneration in all lesions and disease stages of multiple sclerosis. It further indicates that the disease processes of multiple sclerosis may die out in aged patients with long-standing disease.
Finding a Heaviest Vertex-Weighted Triangle Is not Harder than Matrix Multiplication

We show that a maximum-weight triangle in an undirected graph with n vertices and real weights assigned to vertices can be found in time O(nω + n2+o(1)), where ω is the exponent of the fastest matrix multiplication algorithm. By the currently best bound on ω, the running time of our algorithm is O(n2.376). Our algorithm substantially improves the previous time-bounds for this problem, and its asymptotic time complexity matches that of the fastest known algorithm for finding any triangle (not necessarily a maximum-weight one) in a graph. We can extend our algorithm to improve the upper bounds on finding a maximum-weight triangle in a sparse graph and on finding a maximum-weight subgraph isomorphic to a fixed graph. We can find a maximum-weight triangle in a vertex-weighted graph with m edges in asymptotic time required by the fastest algorithm for finding any triangle in a graph with m edges, i.e., in time O(m1.41). Our algorithms for a maximum-weight fixed subgraph (in particular any clique of constant size) are asymptotically as fast as the fastest known algorithms for a fixed subgraph.
A Survey of Blue-Noise Sampling and Its Applications

In this paper, we survey recent approaches to blue-noise sampling and discuss their beneficial applications. We discuss the sampling algorithms that use points as sampling primitives and classify the sampling algorithms based on various aspects, e.g., the sampling domain and the type of algorithm. We demonstrate several well-known applications that can be improved by recent blue-noise sampling techniques, as well as some new applications such as dynamic sampling and blue-noise remeshing.
Indicators of socioeconomic position (part 1).

This glossary presents a comprehensive list of indicators of socioeconomic position used in health research. A description of what they intend to measure is given together with how data are elicited and the advantages and limitation of the indicators. The glossary is divided into two parts for journal publication but the intention is that it should be used as one piece. The second part highlights a life course approach and will be published in the next issue of the journal.
Degradation of Gamma-oryzanol in Rice Bran Oil during Heating : An Analysis Using Derivative UV-spectrophotometry

Gamma-oryzanol, a group of ferulic acid esters of phytosterols and triterpene alcohols, has been reported to exhibit antioxidant activity and other health beneficial properties. In this study, the degradation of gamma-oryzanol in rice bran oil during the heat treatment was investigated. The quantitative analysis of the gammaoryzanol in rice bran oil was performed by second order derivative UV-spectrophotometry. The degradation kinetics was described by the first-order reaction model and degradation rate constants were 0.0089, 0.0315 and 0.0763 at 120, 150 and 200oC, respectively. Temperature dependence of the rate constant obeyed Arrhenius relationship, and activation energy was calculated to be 40.76 kJ/mol.
Tympanoplasty outcomes in elderly patients.

To investigate the outcomes of tympanoplasty in elderly (≥60 years) compared with young patients (18-59 years).



Patients who had been performed type I tympanoplasty between 2009 and 2012 were retrospectively analyzed. Preoperative and postoperative audiological results and the graft success of 42 older patients were compared with those of 72 younger ones.



The mean preoperative air conduction levels were statistically significantly higher in older group (57.4±16.8dB) than younger group (37.3±10.3dB) (p<0.001). Preoperative bone conduction levels were statistically significantly higher in older group (28.5±13.4dB) than in the younger group (12.4±4.8dB) (p<0.001). The mean preoperative and postoperative air-bone gaps were statistically significantly larger in the older group (28.5±11.0dB, 16.4±9.0dB) than in the younger group (24.9±7.7dB, 12.4±8.0dB respectively) (p<0.001). The intragroup comparisons of preoperative and postoperative mean air-bone gaps revealed statistically significant improvements in both groups (p<0.001 for both). Graft success rates and the mean hearing gains were not statistically significantly different between the groups (p=0.225, p=0.786 respectively).



Although preoperative and postoperative air and bone conduction levels were worse in the older group, graft take rates and postoperative hearing gain did not differ between the groups. If the physical status is stable tympanoplasty procedure can be recommended for elderly patients.
Predicting outcome in traumatic brain injury: development of a novel computerized tomography classification system (Helsinki computerized tomography score).

Early computerized tomography (CT) abnormalities are important predictors of outcome after traumatic brain injury (TBI).



To develop a novel CT scoring system (Helsinki CT score) and to compare it with the Marshall CT classification and the Rotterdam CT score in predicting long-term outcome of patients with TBI.



Eight hundred sixty-nine consecutive TBI patients were included in this open-cohort, retrospective, single-center study. Logistic regression was used to develop the Helsinki CT score. The scores from the Marshall, Rotterdam, and Helsinki CT scoring methods were added to a clinical model based on age, motor score, and pupils to evaluate their value in predicting outcome. Internal validity was assessed by a bootstrap technique and expressed as area under the curve (AUC). Outcome was 6-month unfavorable neurological outcome and mortality.



Variables included in the Helsinki CT score were bleeding type and size, intraventricular hemorrhage, and suprasellar cisterns. In the present data set, the performance of the Helsinki CT score was superior to that of the Marshall CT and Rotterdam CT scores (AUC, 0.74-0.75 vs 0.63-0.70; P < .001). Addition of the Helsinki CT score modestly increased prognostic performance of the clinical model (AUC neurological outcome +0.02 [P = .002]; AUC mortality, +0.01 [P = .21]). In contrast, the Marshall and Rotterdam CT scores were of no additional predictive value to the clinical model (P > .05).



Use of the novel Helsinki CT score improved outcome prediction accuracy, and the Helsinki CT score is a feasible alternative to the Rotterdam and Marshall CT systems. External validation of the Helsinki CT score is advocated to show generalizability.
Antiemetic efficacy of prophylactic dimenhydrinate (Dramamine) vs ondansetron (Zofran)

The prophylactic administration of dimenhydrinate (Dramamine) is as effective as the use of ondansetron (Zofran) in preventing postoperative nausea and vomiting (PONV) in patients undergoing elective laparoscopic cholecystectomy. A prospective double-blind randomized study was performed in a tertiary care referral center. For this study, 128 American Society of Anesthesiology (ASA) physical statuses I, II, and III patients were randomly assigned to receive either ondansetron 4 mg intravenously (IV) at $17 per dose (group 1) or dimenhydrinate 50 mg IV at $2.50 per dose (group 2) before induction of anesthesia. The end points evaluated were frequency of PONV, need for rescue antiemetics, need for overnight hospitalization secondary to persistent nausea and vomiting, and frequency PONV 24 h after discharge. Chi-square tests and student’s t-test were used to determine the significance of differences among groups. Of the 128 patients enrolled in this study, 20 were excluded: 15 patients received an additional antiemetic preoperative; 4 were converted to open cholecystectomies; and 1 procedure was aborted due to carcinomatosis. Of the 108 remaining participants, 50 received ondansetron (group 1) and 58 received dimenhydrinate (group 2). Both groups were well matched for demographics including gender, ASA class, and history of motion sickness. The need for rescue antiemetics occurred in 34% of group 1 and 29% of Group 2 (p=0.376), postoperative vomiting in 6% of group 1 and 12% of group 2 (p=0.228), and postoperative nausea in 42% of group 1 and 34% of group 2 (p=0.422). One group 1 patient and two group 2 patients required overnight hospitalization for persistent nausea, a difference that was not significant. Rates of PONV 24 h after discharge were similar between groups 1 and 2 (10% vs 14%, p=0.397 and 2% vs 5%, p=0.375, respectively). Prophylactic administration of dimenhydrinate is as effective as the use of ondansetron in preventing PONV in patients undergoing elective laparoscopic cholecystectomy. Dimenhydrinate is the preferred drug because it is less expensive. With more than 500,000 laparoscopic cholecystectomies performed in the United States each year, the potential drug cost savings from the prophylactic administration of dimenhydrinate instead of ondansetron exceed $7.25 million per year.
Optimizing data permutations for SIMD devices

The widespread presence of SIMD devices in today's microprocessors has made compiler techniques for these devices tremendously important. One of the most important and difficult issues that must be addressed by these techniques is the generation of the data permutation instructions needed for non-contiguous and misaligned memory references. These instructions are expensive and, therefore, it is of crucial importance to minimize their number to improve performance and, in many cases, enable speedups over scalar code.Although it is often difficult to optimize an isolated data reorganization operation, a collection of related data permutations can often be manipulated to reduce the number of operations. This paper presents a strategy to optimize all forms of data permutations. The strategy is organized into three steps. First, all data permutations in the source program are converted into a generic representation. These permutations can originate from vector accesses to non-contiguous and misaligned memory locations or result from compiler transformations. Second, an optimization algorithm is applied to reduce the number of data permutations in a basic block. By propagating permutations across statements and merging consecutive permutations whenever possible, the algorithm can significantly reduce the number of data permutations. Finally, a code generation algorithm translates generic permutation operations into native permutation instructions for the target platform. Experiments were conducted on various kinds of applications. The results show that up to 77% of the permutation instructions are eliminated and, as a result, the average performance improvement is 48% on VMX and 68% on SSE2. For several applications, near perfect speedups have been achieved on both platforms.
Scalable training of 3D convolutional networks on multi- and many-cores

Convolutional networks (ConvNets) have become a popular approach to computer vision. Here we consider the parallelization of ConvNet training, which is computationally costly. Our novel parallel algorithm is based on decomposition into a set of tasks, most of which are convolutions or FFTs. Theoretical analysis suggests that linear speedup with the number of processors is attainable. To attain such performance on real shared-memory machines, our algorithm computes convolutions converging on the same node of the network with temporal locality to reduce cache misses, and sums the convergent convolution outputs via an almost wait-free concurrent method to reduce time spent in critical sections. Benchmarking with multi-core CPUs shows speedup roughly equal to the number of physical cores. We also demonstrate 90x speedup on a many-core CPU (Xeon Phi Knights Corner). Our algorithm can be either faster or slower than certain GPU implementations depending on specifics of the network architecture, kernel sizes, and density and size of the output patch.
On Uniquely List Colorable Graphs

Let G be a graph with n vertices and suppose that for each vertex v in G, there exists a list of k colors, L(v), such that there is a unique proper coloring for G from this collection of lists, then G is called a uniquely k–list colorable graph. Recently M. Mahdian and E.S. Mahmoodian characterized uniquely 2–list colorable graphs. Here we state some results which will pave the way in characterization of uniquely k–list colorable graphs. There is a relationship between this concept and defining sets in graph colorings and critical sets in latin squares.
Abnormal crowd behavior detection using high-frequency and spatio-temporal features

Abnormal crowd behavior detection is an important research issue in computer vision. The traditional methods first extract the local spatio-temporal cuboid from video. Then the cuboid is described by optical flow or gradient features, etc. Unfortunately, because of the complex environmental conditions, such as severe occlusion, over-crowding, etc., the existing algorithms cannot be efficiently applied. In this paper, we derive the high-frequency and spatio-temporal (HFST) features to detect the abnormal crowd behaviors in videos. They are obtained by applying the wavelet transform to the plane in the cuboid which is parallel to the time direction. The high-frequency information characterize the dynamic properties of the cuboid. The HFST features are applied to the both global and local abnormal crowd behavior detection. For the global abnormal crowd behavior detection, Latent Dirichlet allocation is used to model the normal scenes. For the local abnormal crowd behavior detection, Multiple Hidden Markov Models, with an competitive mechanism, is employed to model the normal scenes. The comprehensive experiment results show that the speed of detection has been greatly improved using our approach. Moreover, a good accuracy has been achieved considering the false positive and false negative detection rates.