nils-herrmann/clean_scirepeval huggingface dataset:
Overexpression of IFN-induced protein with tetratricopeptide repeats 3 (IFIT3) in pancreatic cancer: cellular “pseudoinflammation” contributing to an aggressive phenotype

Inflammation contributes to important traits that cancer cells acquire during malignant progression. Gene array data recently identified upregulation of interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) in aggressive pancreatic cancer cells. IFIT3 belongs to the group of interferon stimulated genes (ISG), can be induced by several cellular stress stimuli and by its tetratricopeptide repeats interacts with a multitude of cellular proteins. Upregulation of IFIT3 was confirmed in the aggressive pancreatic cancer cell line L3.6pl compared with its less aggressive cell line of origin, COLO357FG. Transgenic induction of IFIT3 expression in COLO357FG resulted in greater mass of orthotopic tumors and higher prevalence of metastases. Several important traits that mediate malignancy were altered by IFIT3: increased VEGF and IL-6 secretion, chemoresistance and decreased starvation-induced apoptosis. IFIT3 showed binding to JNK and STAT1, the latter being an important inducer of IFIT3 expression. Despite still being alterable by "classical" IFN or NFκB signaling, our findings indicate constitutive - possibly auto-regulated - upregulation of IFIT3 in L3.6pl without presence of an adequate inflammatory stimulus. The transcription factor SOX9, which is linked to regulation of hypoxia-related genes, was identified as a key mediator of upregulation of the oncogene IFIT3 and thereby sustaining a "pseudoinflammatory" cellular condition.
Attentive Aspect Modeling for Review-aware Recommendation

XINYU GUAN, Xi’an Jiaotong University, P. R. China ZHIYONG CHENG†, Qilu University of Technology (Shandong Academy of Sciences), China XIANGNAN HE, University of Science and Technology of China, China YONGFENG ZHANG, Rutgers University, USA ZHIBO ZHU, Xi’an Jiaotong University, P. R. China QINKE PENG, Xi’an Jiaotong University, P. R. China TAT-SENG CHUA, National University of Singapore, Singapore
Towards operationalizing complexity leadership : How generative , administrative and community-building leadership practices enact organizational outcomes

Over five years ago, The Leadership Quarterly published a special issue on complexity to advance a new way of thinking about leadership. In shifting attention away from the individual to the organizing process itself, complexity added an important focus on process and context to leadership and management research. Yet, the complexity approach creates challenges for researchers who must combine or replace individual level constructs—like those built through surveys or factor analysis—with richer theories that investigate networked meso dynamics, multilevel phenomena, emergent processes, and organizational outcomes. To address this challenge, the present analysis draws on theoretical and empirical work over the last several years to identify five specific areas where complexity inspired research has led to new insights about the mechanisms that enable the organization to perform and adapt. It suggests propositions that describe how leadership and management, defined holistically, might activate complexity mechanisms to perform five essential organizing functions.
Regulation of innate immune responses in the brain

Microglial cells are the main innate immune cells of the complex cellular structure of the brain. These cells respond quickly to pathogens and injury, accumulate in regions of degeneration and produce a wide variety of pro-inflammatory molecules. These observations have resulted in active debate regarding the exact role of microglial cells in the brain and whether they have beneficial or detrimental functions. Careful targeting of these cells could have therapeutic benefits for several types of trauma and disease specific to the central nervous system. This Review discusses the molecular details underlying the innate immune response in the brain during infection, injury and disease.
On non-preemptive scheduling of period and sporadic tasks

This paper examines a fundamental problem in the theory of real-time scheduling, that of scheduling a set of periodic or sporadic tasks on a uniprocessor without preemption and without inserted idle time. We exhibit a necessary and sufficient set of conditions C for a set of periodic or sporadic tasks to be schedulable for arbitrary release times of the tasks. We then show that any set of periodic or sporadic tasks that satisfies conditions C can be scheduled with an earliest deadline first (EDF) scheduling algorithm. We also address the question of schedulability of a set of tasks with specified release times. For sets of sporadic tasks with specified release times, we show that the conditions C are again necessary and sufficient for schedulability. However, for sets of periodic tasks with specified release times, the conditions C, while sufficient, are not necessary. In fact, we show that determining whether a set of periodic tasks with specified release times is schedulable is intractable ( i.e., NP-hard in the strong sense). Moreover, we show that the existence of a universal algorithm for scheduling periodic tasks with specified release times would imply that P = NP.
Electrophysiological studies of human face perception. I: Potentials generated in occipitotemporal cortex by face and non-face stimuli.

This and the following two papers describe event-related potentials (ERPs) evoked by visual stimuli in 98 patients in whom electrodes were placed directly upon the cortical surface to monitor medically intractable seizures. Patients viewed pictures of faces, scrambled faces, letter-strings, number-strings, and animate and inanimate objects. This paper describes ERPs generated in striate and peristriate cortex, evoked by faces, and evoked by sinusoidal gratings, objects and letter-strings. Short-latency ERPs generated in striate and peristriate cortex were sensitive to elementary stimulus features such as luminance. Three types of face-specific ERPs were found: (i) a surface-negative potential with a peak latency of approximately 200 ms (N200) recorded from ventral occipitotemporal cortex, (ii) a lateral surface N200 recorded primarily from the middle temporal gyrus, and (iii) a late positive potential (P350) recorded from posterior ventral occipitotemporal, posterior lateral temporal and anterior ventral temporal cortex. Face-specific N200s were preceded by P150 and followed by P290 and N700 ERPs. N200 reflects initial face-specific processing, while P290, N700 and P350 reflect later face processing at or near N200 sites and in anterior ventral temporal cortex. Face-specific N200 amplitude was not significantly different in males and females, in the normal and abnormal hemisphere, or in the right and left hemisphere. However, cortical patches generating ventral face-specific N200s were larger in the right hemisphere. Other cortical patches in the same region of extrastriate cortex generated grating-sensitive N180s and object-specific or letter-string-specific N200s, suggesting that the human ventral object recognition system is segregated into functionally discrete regions.
Similar image searching from image database using cluster mean sorting and performance estimation

Computer vision field over the last decades, Content-Based Image Retrieval (CBIR) systems are used in order to search, retrieve and browse image from databases. This accumulation of large collections of digital images has created the need for efficient and intelligent schemes for classifying and retrieval of images. In our proposed method, we are using, Clustering Algorithms for retrieving the images from huge volumes of data with better performance. This requires image processing, feature extraction, classification of images and retrieval steps in order to develop an efficient image retrieval system. In this work, processing is done through the image clustering method  which is used for feature extraction which is taken place. For retrieval of images, mean values are calculated between Query image and database images and all clustered mean values are considered as a sorted order. When the comparisons are allowed between the images, in our observation we founded excellent performance and similarities in between images. The main aim of this work is to extract images with good similarity when the images are retrieved based on query image.
A platform surveillance monitoring system using image processing for passenger safety in railway station

In this paper, we propose a platform surveillance monitoring system using image processing technology for passenger safety in railway station. The proposed system monitors almost entire length of the track line in the platform by using multiple cameras, and determines in real-time whether a human or dangerous obstacle is in the preset monitoring area by using image processing technology. According to the experimental results, we verity system performance in real condition. Detection of train state and object is conducted robustly by using proposed image processing algorithm. Moreover, to deal with the accident immediately, the system provides local station, central control room and train with the video information and alarm message.
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition

This work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities. The recognition is realized using a two-level hierarchy of Long Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture, which can be trained end-to-end. In comparison with existing architectures of LSTMs, we make two key contributions giving the name to our approach as Confidence-Energy Recurrent Network &#x2013; CERN. First, instead of using the common softmax layer for prediction, we specify a novel energy layer (EL) for estimating the energy of our predictions. Second, rather than finding the common minimum-energy class assignment, which may be numerically unstable under uncertainty, we specify that the EL additionally computes the p-values of the solutions, and in this way estimates the most confident energy minimum. The evaluation on the Collective Activity and Volleyball datasets demonstrates: (i) advantages of our two contributions relative to the common softmax and energy-minimization formulations and (ii) a superior performance relative to the state-of-the-art approaches.
Micro-Transfer-Printing: Heterogeneous integration of microscale semiconductor devices using elastomer stamps

Micro-transfer-printing is an emerging method to accurately assemble microscale semiconductor devices onto non-native substrates. In micro-transfer-printing an engineered elastomer stamp coupled to a precision motion controller is utilized to pick-up and transfer arrays of microscale devices. A wide range of materials and devices have been micro-transfer-printed, including; Silicon integrated circuits, Gallium Arsenide LEDs, Gallium Arsenide solar cells, Gallium Arsenide lasers and Gallium Nitride LEDs. Devices compatible with micro-transfer-printing are very thin (<; 10 microns) and small (<; 100um lateral dimensions) and are ideally suited for making flexible device formats and are also well-suited for wafer-level heterogeneous integration. Here, we review the state-of-the art in Micro-Transfer-Printing and also present Micro-Transfer-Printing as a cost-effective approach to heterogeneous integration of compound semiconductor sensors.
Semantically Reliable Multicast: Definition, Implementation, and Performance Evaluation

Semantic Reliability is a novel correctness criterion for multicast protocols based on the concept of message obsolescence: A message becomes obsolete when its content or purpose is superseded by a subsequent message. By exploiting obsolescence, a reliable multicast protocol may drop irrelevant messages to find additional buffer space for new messages. This makes the multicast protocol more resilient to transient performance perturbations of group members, thus improving throughput stability. This paper describes our experience in developing a suite of semantically reliable protocols. It summarizes the motivation, definition, and algorithmic issues and presents performance figures obtained with a running implementation. The data obtained experimentally is compared with analytic and simulation models. This comparison allows us to confirm the validity of these models and the usefulness of the approach. Finally, the paper reports the application of our prototype to distributed multiplayer games.
Exploring how deep neural networks form phonemic categories

Deep neural networks (DNNs) have become the dominant technique for acoustic-phonetic modeling due to their markedly improved performance over other models. Despite this, little is understood about the computation they implement in creating phonemic categories from highly variable acoustic signals. In this paper, we analyzed a DNN trained for phoneme recognition and characterized its representational properties, both at the single node and population level in each layer. At the single node level, we found strong selectivity to distinct phonetic features in all layers. Node selectivity to specific manners and places of articulation appeared from the first hidden layer and became more explicit in deeper layers. Furthermore, we found that nodes with similar phonetic feature selectivity were differentially activated to different exemplars of these features. Thus, each node becomes tuned to a particular acoustic manifestation of the same feature, providing an effective representational basis for the formation of invariant phonemic categories. This study reveals that phonetic features organize the activations in different layers of a DNN, a result that mirrors the recent findings of feature encoding in the human auditory system. These insights may provide better understanding of the limitations of current models, leading to new strategies to improve their performance.
The Effectiveness of Virtual Reality Pain Control With Multiple Treatments of Longer Durations: A Case Study

Immersive virtual reality (VR) has proved to be potentially valuable as a pain control technique for patients with severe burns undergoing wound care and physical therapy. Recent studies have shown that single, 3-min visits to a virtual world can dramatically reduce the amount of pain experienced during wound care, and the illusion of going inside the computer-generated world helps make VR analgesia unusually effective. This case study explored whether VR continues to reduce pain when the duration and frequency of VR treatments are increased to more practical levels. A patient with deep flash burns covering 42% of his body spent varying amounts of time performing physical therapy with and without virtual reality. Five subjective pain ratings INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION, 13(1), 1–12 Copyright
Game theory in cyber security defence

Cyber threats and the field of computer cyber defense are gaining more and more an increased importance in our lives. Starting from our regular personal computers and ending with thin clients such as netbooks or smartphones we find ourselves bombarded with constant malware attacks. In this paper we will present a new and novel way in which we can detect these kind of attacks by using elements of modern game theory. We will present the effects and benefits of game theory and we will talk about a defense exercise model that can be used to train cyber response specialists.
The stressed synapse: the impact of stress and glucocorticoids on glutamate transmission

Mounting evidence suggests that acute and chronic stress, especially the stress-induced release of glucocorticoids, induces changes in glutamate neurotransmission in the prefrontal cortex and the hippocampus, thereby influencing some aspects of cognitive processing. In addition, dysfunction of glutamatergic neurotransmission is increasingly considered to be a core feature of stress-related mental illnesses. Recent studies have shed light on the mechanisms by which stress and glucocorticoids affect glutamate transmission, including effects on glutamate release, glutamate receptors and glutamate clearance and metabolism. This new understanding provides insights into normal brain functioning, as well as the pathophysiology and potential new treatments of stress-related neuropsychiatric disorders.
Scalable Co-Optimization of Morphology and Control in Embodied Machines

Evolution sculpts both the body plans and nervous systems of agents together over time. By contrast, in artificial intelligence and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behaviour arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioural performance. Here, we further examine this hypothesis and demonstrate a technique for 'morphological innovation protection', which temporarily reduces selection pressure on recently morphologically changed individuals, thus enabling evolution some time to 'readapt' to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioural training-while simultaneously providing a test bed to investigate the theory of embodied cognition.
Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases

The problem of similarity search in large time series databases has attracted much attention recently. It is a non-trivial problem because of the inherent high dimensionality of the data. The most promising solutions involve first performing dimensionality reduction on the data, and then indexing the reduced data with a spatial access method. Three major dimensionality reduction techniques have been proposed: Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and more recently the Discrete Wavelet Transform (DWT). In this work we introduce a new dimensionality reduction technique which we call Piecewise Aggregate Approximation (PAA). We theoretically and empirically compare it to the other techniques and demonstrate its superiority. In addition to being competitive with or faster than the other methods, our approach has numerous other advantages. It is simple to understand and to implement, it allows more flexible distance measures, including weighted Euclidean queries, and the index can be built in linear time.
Mechanisms of Disease Cancer Stem Cells

2006;355:1253-61.  The deepening of our understanding of normal biology has made it clear that stem cells have a critical role not only in the generation of complex multicellular organisms, but also in the development of tumors. Recent findings support the concept that cells with the properties of stem cells are integral to the development and perpetuation of several forms of human cancer.1-3 Eradication of the stem-cell compartment of a tumor also may be essential to achieve stable, long-lasting remission, and even a cure, of cancer.4,5 Advances in our knowledge of the properties of stem cells have made specific targeting and eradication of cancer stem cells a topic of considerable interest. In this article, we discuss the properties of cancer stem cells, outline initial therapeutic strategies against them, and present challenges for the future.
Impact of thyroidectomy on cardiac manifestations of Graves' disease.

Graves' disease (GD) has multiple adverse effects on the cardiovascular system. We aimed to examine the outcome of thyroidectomy in patients with cardiac manifestations of GD and evaluate their associated postoperative complications.



Retrospective analysis using a prospectively collected database.



A retrospective analysis of our prospectively collected thyroid surgery database was performed. Forty patients with hyperthyroidism due to GD were identified, and each was appropriately age matched to a euthyroid patient with multinodular goiter (MNG). All patients underwent total thyroidectomy. Data relating to cardiac comorbidities were collected from preoperative and postoperative clinic notes, hospital admissions, electrocardiograms, echocardiograms, and blood work. Perioperative biochemical, cardiovascular, and postoperative outcomes were analyzed.



Twenty-four (60%) GD patients and 14 (35.0%) MNG patients had cardiac manifestations (P = .001). Hypertension resolved in 41.7% of GD patients and 7.7% of MNG patients (P = .00002). Two of the three GD patients with congestive heart failure (CHF) had resolution of CHF with significant improvement in ejection fraction, whereas the one MNG patient with CHF saw no change. Additionally, the majority of GD patients saw a resolution of their tachycardia (68.8%) and atrial fibrillation (100%). Four postoperative complications occurred in both the GD and MNG groups (4/40, 10%).



Surgical treatment of GD in patients with cardiac manifestations offers rapid clinical improvement of hypertension, impaired left ventricular systolic function, and arrhythmias. When performed by a high-volume surgeon, the complication rate is similar to thyroidectomy for other benign disease.
A planar ultra-wideband balanced doubler

A new low-cost design for a planar balanced doubler with ultra-wide bandwidth is presented. This doubler utilizes two types of ultra-wideband transitions: microstrip-to-CPW (coplanar waveguide) and microstrip-to-CPS (coplanar stripline) transitions. The transitions are designed to provide field and impedance matching between adjacent transmission lines. The fabricated doubler provides less than 10 dB conversion loss for output frequencies from 9 GHz to 26 GHz and less than 12 dB conversion loss from 7 GHz to 38 GHz.
Disaggregating shear stress: the roles of cell deformability and fibrinogen concentration.

Red blood cell (RBC) aggregation is greatly affected by cell deformability and reduced deformability and increased RBC aggregation are frequently observed in hypertension, diabetes mellitus, and sepsis, thus measurement of both these parameters is essential. In this study, we investigated the effects of cell deformability and fibrinogen concentration on disaggregating shear stress (DSS). The DSS was measured with varying cell deformability and geometry. The deformability of cells was gradually decreased with increasing concentration of glutaraldehyde (0.001~0.005%) or heat treatment at 49.0°C for increasing time intervals (0~7 min), which resulted in a progressive increase in the DSS. However, RBC rigidification by either glutaraldehyde or heat treatment did not cause the same effect on RBC aggregation as deformability did. The effect of cell deformability on DSS was significantly increased with an increase in fibrinogen concentration (2~6 g/L). These results imply that reduced cell deformability and increased fibrinogen levels play a synergistic role in increasing DSS, which could be used as a novel independent hemorheological index to characterize microcirculatory diseases, such as diabetic complications with high sensitivity.