nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of RadLex Coverage and Term Co-occurrence in Radiology Reporting Templates

Radiologists are critically interested in promoting best practices in medical imaging, and to that end, they are actively developing tools that will optimize terminology and reporting practices in radiology. The RadLex® vocabulary, developed by the Radiological Society of North America (RSNA), is intended to create a unifying source for the terminology that is used to describe medical imaging. The RSNA Reporting Initiative has developed a library of reporting templates to integrate reusable knowledge, or meaning, into the clinical reporting process. This report presents the initial analysis of the intersection of these two major efforts. From 70 published radiology reporting templates, we extracted the names of 6,489 reporting elements. These terms were reviewed in conjunction with the RadLex vocabulary and classified as an exact match, a partial match, or unmatched. Of 2,509 unique terms, 1,017 terms (41%) matched exactly to RadLex terms, 660 (26%) were partial matches, and 832 reporting terms (33%) were unmatched to RadLex. There is significant overlap between the terms used in the structured reporting templates and RadLex. The unmatched terms were analyzed using the multidimensional scaling (MDS) visualization technique to reveal semantic relationships among them. The co-occurrence analysis with the MDS visualization technique provided a semantic overview of the investigated reporting terms and gave a metric to determine the strength of association among these terms.
A PARAMETRIC STUDY OF CODE-BASED PERFORMANCE LIMITS FOR WIDE BEAMS

In this study, we compared the performance limits of building codes and assessed whether these limits could be applied to wide beams. We performed a parametric study, investigating the longitudinal reinforcement ratio, beam width, steel class, and type and spacing of the transverse reinforcement. We obtained the following results from these analyses: Increasing the longitudinal reinforcement ratio reduced the deformation limit corresponding to code-based performance limits. Changing the confinement properties significantly affected the TEC 2007 and ASCE/SEI 41 limits. The performance limits defined by EC8 and ASCE/SEI 41 had similar corresponding plastic deformations, lower than those corresponding to the TEC 2007 strain-based damage limits. The results show that assessing the performance of wide beams by using the performance limits from different codes could produce contradictory results.
Improving Protein Fold Recognition by Deep Learning Networks

For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.
Impact of increasing number of neurons on performance of neuromorphic architecture

Pattern recognition is used to classify the input data into different classes based on extracted key features. Increasing the recognition rate of pattern recognition applications is a challenging task. The spike neural networks inspired from physiological brain architecture, is a neuromorphic hardware implementation of network of neurons. A sample of neuromorphic architecture has two layers of neurons, input and output. The number of input neurons is fixed based on the input data patterns. While the number of outputs neurons can be different. The goal of this paper is performance evaluation of neuromorphic architecture in terms of recognition rates using different numbers of output neurons. For this purpose a simulation environment of N2S3 and MNIST handwritten digits are used. Our simulation results show the recognition rate for various number of output neurons, 20, 30, 50, 100, 200, and 300 is 70%, 74%, 79%, 85%, 89%, and 91%, respectively.
Transcription factors that regulate monocyte/macrophage differentiation.

Although all the cells in an organism contain the same genetic information, differences in the cell phenotype arise from the expression of lineage-specific genes. During myelopoiesis, external differentiating signals regulate the expression of a set of transcription factors. The combined action of these transcription factors subsequently determines the expression of myeloid-specific genes and the generation of monocytes and macrophages. In particular, the transcription factor PU.1 has a critical role in this process. We review the contribution of several transcription factors to the control of macrophage development.
Rethinking Blockchain Security: Position Paper

Blockchain technology has become almost as famous for incidents involving security breaches as for its innovative potential. We shed light on the prevalence and nature of these incidents through a database structured using the STIX format. Apart from OPSEC-related incidents, we find that the nature of many incidents is specific to blockchain technology. Two categories stand out: smart contracts, and techno-economic protocol incentives. For smart contracts, we propose to use recent advances in software testing to find flaws before deployment. For protocols, we propose the PRESTO framework that allows us to compare different protocols within a five-dimensional framework.
Activity of the anterior cingulate cortex and ventral hippocampus underlie increases in contextual fear generalization

Memories for context become less specific with time resulting in animals generalizing fear from training contexts to novel contexts. Though much attention has been given to the neural structures that underlie the long-term consolidation of a context fear memory, very little is known about the mechanisms responsible for the increase in fear generalization that occurs as the memory ages. Here, we examine the neural pattern of activation underlying the expression of a generalized context fear memory in male C57BL/6J mice. Animals were context fear conditioned and tested for fear in either the training context or a novel context at recent and remote time points. Animals were sacrificed and fluorescent in situ hybridization was performed to assay neural activation. Our results demonstrate activity of the prelimbic, infralimbic, and anterior cingulate (ACC) cortices as well as the ventral hippocampus (vHPC) underlie expression of a generalized fear memory. To verify the involvement of the ACC and vHPC in the expression of a generalized fear memory, animals were context fear conditioned and infused with 4% lidocaine into the ACC, dHPC, or vHPC prior to retrieval to temporarily inactivate these structures. The results demonstrate that activity of the ACC and vHPC is required for the expression of a generalized fear memory, as inactivation of these regions returned the memory to a contextually precise form. Current theories of time-dependent generalization of contextual memories do not predict involvement of the vHPC. Our data suggest a novel role of this region in generalized memory, which should be incorporated into current theories of time-dependent memory generalization. We also show that the dorsal hippocampus plays a prolonged role in contextually precise memories. Our findings suggest a possible interaction between the ACC and vHPC controls the expression of fear generalization.
Rotated Word Vector Representations and their Interpretability

Vector representation of words improves performance in various NLP tasks, but the high-dimensional word vectors are very difficult to interpret. We apply several rotation algorithms to the vector representation of words to improve the interpretability. Unlike previous approaches that induce sparsity, the rotated vectors are interpretable while preserving the expressive performance of the original vectors. Furthermore, any pre-built word vector representation can be rotated for improved interpretability. We apply rotation to skipgrams and glove and compare the expressive power and interpretability with the original vectors and the sparse overcomplete vectors. The results show that the rotated vectors outperform the original and the sparse overcomplete vectors for interpretability and expressiveness tasks.
Saccadic adaptation shapes visual space in macaques.

Saccadic eye movements are an integral part of many visually guided behaviors. Recent research in humans has shown that processes which control saccades are also involved in establishing perceptual space: A shift in object localization during fixation occurred after saccade amplitudes had been shortened or lengthened by saccadic adaptation. We tested whether similar effects can be established in nonhuman primates. Two trained macaque monkeys localized briefly presented stimuli on a touch screen by indicating the memorized target position with the hand on the screen. The monkeys performed this localization task before and after saccade amplitudes were modified through saccadic adaptation. During localization trials they had to maintain fixation. Successful saccadic adaptation led to a concurrent shift of the touched position on the screen. This mislocalization occurred for both adaptive shortening and lengthening of saccade amplitude. We conclude that saccadic adaptation has the potential to influence localization performance in monkeys, similar to the results found in humans.
Psychiatric distress in animals versus animal models of psychiatric distress

The realm of human uniqueness steadily shrinks; reflecting this, other primates suffer from states closer to depression or anxiety than 'depressive-like' or 'anxiety-like behavior'. Nonetheless, there remain psychiatric domains unique to humans. Appreciating these continuities and discontinuities must inform the choice of neurobiological approach used in studying any animal model of psychiatric disorders. More fundamentally, the continuities reveal how aspects of psychiatric malaise run deeper than our species' history.
Transresistance instrumentation amplifier

A transresistance instrumentation amplifier (dual-input transresistance amplifier) was designed, and a prototype was fabricated and tested in a gamma-ray dosimeter. The circuit, explained in this letter, is a differential amplifier which is suitable for amplification of signals from current-source transducers. In the dosimeter application, the amplifier proved superior to a regular (single) transresistance amplifier, giving better temperature stability and better common-mode rejection.
Quasi-Z source inverter with battery based PV power generation system

A new PV power generation topology, based on the quasi-Z source inverter (QZSI) with battery, is proposed in this paper. With a battery paralleled with one of the capacitors, this system can smooth the grid-injected/load power when the PV panel outputs a variable power with fluctuations. The battery can be charged or discharged without any extra circuit, because of the unique impedance network of QZSI. The operating principle and power flow of this system are analyzed. The closed-loop control strategy is used to achieve Maximum Power Point Tracking (MPPT) and keep the output voltage stable. Three operating modes of the proposed PV power generation system are simulated in the MATLAB/Simulink to verify its principle and theoretical analysis. A prototype is built in the lab, and the experimental results validate the whole system.
Distribution center location for green supply chain

In this paper, a simple mix integer programming for distribution center location is proposed. Based on this simple model, we introduce two important factors, transport mode and carbon emission, and extend it a model to describe the location problem for green supply chain. Sequently, IBM Watson implosion technologh (WIT) tool was introduced to describe them and solve them. By changing the price of crude oil, we illustrate the its impact on distribution center locations and transportation mode option for green supply chain. From the cases studies, we have known that, as the crude oil price increasing, the profits of the whole supply chain will decrease, carbon emission will also decrease to some degree, while the number of opened distribution center will increase.
Static repositioning in a bike-sharing system: models and solution approaches

Bike-sharing systems allow people to rent a bicycle at one of many automatic rental stations scattered around the city, use them for a short journey and return them at any station in the city. A crucial factor for the success of a bikesharing system is its ability to meet the fluctuating demand for bicycles and for vacant lockers at each station. This is achieved by means of a repositioning operation, which consists of removing bicycles from some stations and transferring them to other stations, using a dedicated fleet of trucks. Operating such a fleet in a large bike-sharing system is an intricate problem consisting of decisions regarding the routes that the vehicles should follow and the number of bicycles that should be removed or placed at each station on each visit of the vehicles. In this paper, we present our modeling approach to the problem that generalizes existing routing models in the literature. This is done by introducing a unique convex objective function as well as time-related considerations. We present two mixed integer linear program formulations, discuss the assumptions associated with each, strengthen them by several valid inequalities and dominance rules, and compare their performances through an extensive numerical study. The results indicate that one of the formulations is very effective in obtaining high quality solutions to real life instances of the problem consisting of up to 104 stations and two vehicles. Finally, we draw insights on the characteristics of good solutions. T. Raviv (&) M. Tzur I. A. Forma Industrial Engineering Department, Tel Aviv University, 69978 Tel Aviv,  talraviv@eng.tau.ac.il M. Tzur e-mail: tzur@eng.tau.ac.il I. A. Forma e-mail: irisforma@eng.tau.ac.il; Irisf@afeka.ac.il I. A. Forma Afeka Tel Aviv Academic College of Engineering, Bnei Efraim 218, Tel Aviv,  DOI 10.1007/s13676-012-0017-6
Organ Segmentation in Poultry Viscera Using RGB-D

We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. This is a step towards replacing the current strenuous manual inspection at poultry processing plants. Features are extracted from feature maps such as activation maps from a convolutional neural network (CNN). A random forest classifier assigns class probabilities, which are further refined by utilizing context in a conditional random field. The presented method is compatible with both 2D and 3D features, which allows us to explore the value of adding 3D and CNN-derived features. The dataset consists of 604 RGB-D images showing 151 unique sets of eviscerated viscera from four different perspectives. A mean Jaccard index of 78.11 % is achieved across the four classes of organs by using features derived from 2D, 3D and a CNN, compared to 74.28 % using only basic 2D image features.
3D-Aware Scene Manipulation via Inverse Graphics

We aim to obtain an interpretable, expressive, and disentangled scene representation that contains comprehensive structural and textural information for each object. Previous scene representations learned by neural networks are often uninterpretable, limited to a single object, or lacking 3D knowledge. In this work, we propose 3D scene de-rendering networks (3D-SDN) to address the above issues by integrating disentangled representations for semantics, geometry, and appearance into a deep generative model. Our scene encoder performs inverse graphics, translating a scene into a structured object-wise representation. Our decoder has two components: a differentiable shape renderer and a neural texture generator. The disentanglement of semantics, geometry, and appearance supports 3D-aware scene manipulation, e.g., rotating and moving objects freely while keeping the consistent shape and texture, and changing the object appearance without affecting its shape. Experiments demonstrate that our editing scheme based on 3D-SDN is superior to its 2D counterpart.
A low cost wireless sensor network for water quality monitoring in natural water bodies

Water is one of the precious factors in human life. It plays a vital role in the survival and existence of flora and fauna. But, now a days it is been exploited heavily by dumping of human waste, industrial and chemical waste in to the water bodies which is the resultant of water being contaminated with toxic chemicals. Water scarcity and water contamination has been one of the major issues faced by the society around the world. In many developing countries like India, the main focus is given to industrialization and urbanization. But this has caused an increased water usage and water contamination. This paper deals with the monitoring of water quality by mainly considering the physical and chemical properties of water. For this purpose, a Wireless Sensor node is designed which will contain several sensors to sense the chemical contents of water. The highlight of sensor node is that it is water proof. The main components of WSN include microcontroller for processing the system, communication system for inter and intra node communication and several sensors. The power for the sensor nodes are obtained from the solar panels connected to the sensor nodes. Though, in earlier times, there were water quality monitoring systems, they were power hungry and not real time. Apart from acting as a single system, if this system integrates with other innovative solutions like Smart Home or Smart Building, the application of this system would increase leading to an increased demand for it. This paper highlights on how a real time monitoring system can be made generic, using Network Virtualization. Also, new technologies like Internet of Things (IoT) could be incorporated in this system to make it advanced.
An Efficient Algorithm for Finding Maximum Cycle Packings in Reducible Flow Graphs

Reducible flow graphs occur naturally in connection with flowcharts of computer programs and are used extensively for code optimization and global data flow analysis. In this paper we present an O(n2 log(n2/m)) algorithm for finding a maximum cycle packing in any weighted reducible flow graph with n vertices and m arcs; our algorithm heavily relies on Ramachandran's earlier work concerning reducible flow graphs.
Trajectory planning and four-leg coordination for stair climbing in a quadruped robot

We report on the algorithm of trajectory planning and four leg coordination for quasi-static stair climbing in a quadruped robot. The development is based on the geometrical interactions between the robot legs and the stair, starting from single-leg analysis, followed by two-leg collaboration, and then four-leg coordination. In addition, a brief study on stability of the robot is also reported. Finally, simulation and experimental test are also executed to evaluate the performance of the algorithm.
Shamanism and Cognitive Evolution

based on cross-cultural research. This approach expands earlier theoretical interpretations offered for the significance of cave art that fail to account for central aspects of cave art material. Clottes & Lewis-Williams (1998), Smith (1992) and Ryan (1999) concur in the interpretation that neurologically-based shamanic practices were central to cave art (cf. Williams 1997a,b). Clottes & Lewis-Williams suggest that, in spite of the temporal distance, we have better access to Upper Palaeolithic peoples’ religious experiences than other aspects of their lives because of the neuropsychological basis of those experiences. The commonality in the experiences of shamanism across space and time provides a basis for forming ‘some idea of the social and mental context out of which Upper Palaeolithic religion and art came’ (Clottes & LewisMichael Winkelman
LoRa Backscatter: Enabling The Vision of Ubiquitous Connectivity

The vision of embedding connectivity into billions of everyday objects runs into the reality of existing communication technologies -- there is no existing wireless technology that can provide reliable and long-range communication at tens of microwatts of power as well as cost less than a dime. While backscatter is low-power and low-cost, it is known to be limited to short ranges. This paper overturns this conventional wisdom about backscatter and presents the first wide-area backscatter system. Our design can successfully backscatter from any location between an RF source and receiver, separated by 475 m, while being compatible with commodity LoRa hardware. Further, when our backscatter device is co-located with the RF source, the receiver can be as far as 2.8 km away. We deploy our system in a 4,800 ft2 (446 m2) house spread across three floors, a 13,024 ft2 (1210 m2) office area covering 41 rooms, as well as a one-acre (4046 m2) vegetable farm and show that we can achieve reliable coverage, using only a single RF source and receiver. We also build a contact lens prototype as well as a flexible epidermal patch device attached to the human skin. We show that these devices can reliably backscatter data across a 3,328 ft2 (309 m2) room. Finally, we present a design sketch of a LoRa backscatter IC that shows that it costs less than a dime at scale and consumes only 9.25 μW of power, which is more than 1000x lower power than LoRa radio chipsets.
The Eras and Trends of Automatic Short Answer Grading

Automatic short answer grading (ASAG) is the task of assessing short natural language responses to objective questions using computational methods. The active research in this field has increased enormously of late with over 80 papers fitting a definition of ASAG. However, the past efforts have generally been ad-hoc and non-comparable until recently, hence the need for a unified view of the whole field. The goal of this paper is to address this aim with a comprehensive review of ASAG research and systems according to history and components. Our historical analysis identifies 35 ASAG systems within 5 temporal themes that mark advancement in methodology or evaluation. In contrast, our component analysis reviews 6 common dimensions from preprocessing to effectiveness. A key conclusion is that an era of evaluation is the newest trend in ASAG research, which is paving the way for the consolidation of the field.