nils-herrmann/clean_scirepeval huggingface dataset:
Pancreatic dissection in the procedure of pancreaticoduodenectomy (with videos).

The procedure of pancreaticoduodenectomy consists of three parts: resection, lymph node dissection, and reconstruction. A transection of the pancreas is commonly performed after a maneuver of the pancreatic head, exposing of the portal vein or lymph node dissection, and it should be confirmed as a safe method for pancreatic transection for decreasing the incidence of pancreatic fistula. However, there are only a few clinical trials with high levels of evidence for pancreatic surgery. In this report, we discuss the following issues: dissection of peripancreatic tissue, exposing the portal vein, pancreatic transection, dissection of the right hemicircle of the peri-superior mesenteric artery including plexus and lymph nodes, and dissection of the pancreatic parenchyma.
Penile amputation and scrotal urethrostomy in 18 dogs.

The objective of this study was to report the signalment, indications for surgery, postoperative complications and outcome in dogs undergoing penile amputation and scrotal urethrostomy. Medical records of three surgical referral facilities were reviewed for dogs undergoing penile amputation and scrotal urethrostomy between January 2003 and July 2010. Data collected included signalment, presenting signs, indication for penile amputation, surgical technique, postoperative complications and long-term outcome. Eighteen dogs were included in the study. Indications for surgery were treatment of neoplasia (n=6), external or unknown penile trauma (n=4), penile trauma or necrosis associated with urethral obstruction with calculi (n=3), priapism (n=4) and balanoposthitis (n=1). All dogs suffered mild postoperative haemorrhage (posturination and/or spontaneous) from the urethrostomy stoma for up to 21 days (mean 5.5 days). Four dogs had minor complications recorded at suture removal (minor dehiscence (n=1), mild bruising and swelling around the urethrostomy site and mild haemorrhage at suture removal (n=2), and granulation at the edge of stoma (n=1)). One dog had a major complication (wound dehiscence and subsequent stricture of the stoma). Long-term outcome was excellent in all dogs with non-neoplastic disease. Local tumour recurrence and/or metastatic disease occurred within five to 12 months of surgery in two dogs undergoing penile amputation for the treatment of neoplasia. Both dogs were euthanased.
Reinforcement Learning with A* and a Deep Heuristic

is a popular path-finding algorithm, but it can only be applied to those domains where a good heuristic function is known. Inspired by recent methods combining Deep Neural Networks (DNNs) and trees, this study demonstrates how to train a heuristic represented by a DNN and combine it with A∗ . This new algorithm which we call א∗ can be used efficiently in domains where the input to the heuristic could be processed by a neural network. We compare א∗ to N-Step Deep QLearning (DQN Mnih et al. 2013) in a driving simulation with pixel-based input, and demonstrate significantly better performance in this scenario.
Run-Length and Edge Statistics Based Approach for Image Splicing Detection

In this paper, a simple but efficient approach for blind image splicing detection is proposed. Image splicing is a common and fundamental operation used for image forgery. The detection of image splicing is a preliminary but desirable study for image forensics. Passive detection approaches of image splicing are usually regarded as pattern recognition problems based on features which are sensitive to splicing. In the proposed approach, we analyze the discontinuity of image pixel correlation and coherency caused by splicing in terms of image run-length representation and sharp image characteristics. The statistical features extracted from image run-length representation and image edge statistics are used for splicing detection. The support vector machine (SVM) is used as the classifier. Our experimental results demonstrate that the two proposed features outperform existing ones both in detection accuracy and computational complexity.
Stock Price Forecasting : Comparison of Short Term and Long Term Stock Price Forecasting using Various Techniques of Artificial Neural Networks

Artificial Neural Networks (ANN) play a very vital role in making stock market predictions. As per the literature survey, various researchers have used various approaches to predict the prices of stock market. Some popular approaches used by researchers are Artificial Neural Networks, Genetic Algorithms, Fuzzy Logic, Auto Regressive Models and Support Vector Machines. This study presents ANN based computational approach for predicting the one day ahead closing prices of companies from the three different sectors:IT Sector (Wipro, TCS and Infosys), Automobile Sector (Maruti Suzuki Ltd.) and Banking Sector (ICICI Bank). Different types of artificial neural networks based models like Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Generalized Regression Neural Network (GRNN) and Layer Recurrent Neural Network (LRNN) have been studied and used to forecast the short term and long term share prices of Wipro, TCS, Infosys, Maruti Suzuki and ICICI Bank. All the networks were trained with the 1100 days of trading data and predicted the prices up to next 6 months. Predicted output was generated through available historical data. Experimental results show that BPNN model gives minimum error (MSE) as compared to the RBFNN and GRNN models. GRNN model performs better as compared to RBFNN model. Forecasting performance of LRNN model is found to be much better than other three models. Keywordsartificial intelligence, back propagation, mean square error, artificial neural network.
An Investigation into the Induction Motor of Tesla Model S Vehicle

There are no scientific publications on a electric motor in Tesla cars, so let's try to deduce something. Tesla's induction motor is very enigmatic so the paper tries to introduce a basic model. This secrecy could be interesting for the engineering and physics students.  kinematics, mechanics, electric motors, numerical methods, control of electric drives. Identification based on three points in the steady-state torque-speed curve of the induction motor is presented. The field weakening mode of operation of the motor is analyzed. The Kloss' formula is obtained. The main aim of the article is determination of a mathematical description of the torque vs. speed curve of induction motor and its application for vehicle motion modeling. Additionally, the moment of inertia of the motor rotor and the electric vehicle mass are considered in one equation as electromechanical system. Presented approach may seem like speculation, but it allows to understand the problem of a vehicle motion. The article composition is different from classical approach - studying should be intriguing.
Timely cloud gaming

This work introduces a new model for cloud gaming systems aimed at optimizing the timeliness of video frames based on an age of information (AoI) metric. Mobile clients submit actions through an access network to a game server. The game server generates video frames at a constant frame rate. At the mobile device, the display of these frames represent game status updates. We develop a Markov model to characterize the frame delivery process in low-latency edge cloud gaming systems. Based on this model, we derive a simple formula for the average status age of a tightly synchronized low-latency mobile gaming system in which the inter-frame period is a significant contributor to the system latency. We validate the model by ns-3 simulation of a low-latency edge cloud gaming system. Our evaluation scenarios included single-player games as well as multi-player games in which the game processing was conducted by a combination of a centralized game server and edge cloud renderers.
Facial expression recognition with temporal modeling of shapes

Conditional Random Fields (CRFs) can be used as a discriminative approach for simultaneous sequence segmentation and frame labeling. Latent-Dynamic Conditional Random Fields (LDCRFs) incorporates hidden state variables within CRFs which model sub-structure motion patterns and dynamics between labels. Motivated by the success of LDCRFs in gesture recognition, we propose a framework for automatic facial expression recognition from continuous video sequence by modeling temporal variations within shapes using LDCRFs. We show that the proposed approach outperforms CRFs for recognizing facial expressions. Using Principal Component Analysis (PCA) we study the separability of various expression classes in lower dimension projected spaces. By comparing the performance of CRFs and LDCRFs against that of Support Vector Machines (SVMs), we demonstrate that temporal variations within shapes are crucial in classifying expressions especially for those with a small range of facial motion like anger and sadness. We also show empirically that only using changes in facial appearance over time, without using shape variations, is not sufficient to obtain high performance for facial expression recognition.
Multi-Modal Clique-Graph Matching for View-Based 3D Model Retrieval

Multi-view matching is an important but a challenging task in view-based 3D model retrieval. To address this challenge, we propose an original multi-modal clique graph (MCG) matching method in this paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi-modal feature space and hyper-edges that link pairwise cliques. Moreover, we propose an image set-based clique/edgewise similarity measure to address the issue of the set-to-set distance measure, which is the core problem in MCG matching. The proposed MCG provides the following benefits: 1) preserves the local and global attributes of a graph with the designed structure; 2) eliminates redundant and noisy information by strengthening inliers while suppressing outliers; and 3) avoids the difficulty of defining high-order attributes and solving hyper-graph matching. We validate the MCG-based 3D model retrieval using three popular single-modal data sets and one novel multi-modal data set. Extensive experiments show the superiority of the proposed method through comparisons. Moreover, we contribute a novel real-world 3D object data set, the multi-view RGB-D object data set. To the best of our knowledge, it is the largest real-world 3D object data set containing multi-modal and multi-view information.
A Rule-based Syllable Segmentation of Myanmar Text

Myanmar script uses no space between words and syllable segmentation represents a significant process in many NLP tasks such as word segmentation, sorting, line breaking and so on. In this study, a rulebased approach of syllable segmentation algorithm for Myanmar text is proposed. Segmentation rules were created based on the syllable structure of Myanmar script and a syllable segmentation algorithm was designed based on the created rules. A segmentation program was developed to evaluate the algorithm. A training corpus containing 32,283 Myanmar syllables was tested in the program and the experimental results show an accuracy rate of 99.96% for segmentation.
Tweet Segmentation and Its Application to Named Entity Recognition

Twitter has attracted millions of users to share and disseminate most up-to-date information, resulting in large volumes of data produced everyday. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer severely from the noisy and short nature of tweets. In this paper, we propose a novel framework for tweet segmentation in a batch mode, called HybridSeg. By splitting tweets into meaningful segments, the semantic or context information is well preserved and easily extracted by the downstream applications. HybridSeg finds the optimal segmentation of a tweet by maximizing the sum of the stickiness scores of its candidate segments. The stickiness score considers the probability of a segment being a phrase in English (i.e., global context) and the probability of a segment being a phrase within the batch of tweets (i.e., local context). For the latter, we propose and evaluate two models to derive local context by considering the linguistic features and term-dependency in a batch of tweets, respectively. HybridSeg is also designed to iteratively learn from confident segments as pseudo feedback. Experiments on two tweet data sets show that tweet segmentation quality is significantly improved by learning both global and local contexts compared with using global context alone. Through analysis and comparison, we show that local linguistic features are more reliable for learning local context compared with term-dependency. As an application, we show that high accuracy is achieved in named entity recognition by applying segment-based part-of-speech (POS) tagging.
Complex dismounted IED blast injuries: the initial management of bilateral lower extremity amputations with and without pelvic and perineal involvement.

The magnitude of recent combat blast injuries sustained by forces fighting in Afghanistan has escalated to new levels with more troops surviving higher-energy trauma. The most complex and challenging injury pattern is the emerging frequency of high-energy IED casualties presenting in extremis with traumatic bilateral lower extremity amputations with and without pelvic and perineal blast involvement. These patients require a coordinated effort of advanced trauma and surgical care from the point of injury through definitive management. Early survival is predicated upon a balance of life-saving damage control surgery and haemostatic resuscitation. Emergent operative intervention is critical with timely surgical hemostasis, adequate wound decontamination, revision amputations, and pelvic fracture stabilization. Efficient index surgical management is paramount to prevent further physiologic insult, and a team of orthopaedic and general surgeons operating concurrently may effectively achieve this. Despite the extent and complexity, these are survivable injuries but long-term followup is necessary.
Learning Outcomes in a Model-Based Approach to Curriculum Design

Curriculum or program design has received considerable attention recently. The EQF initiative, the CDIO curriculum or other integrated program developments based on learning outcomes and/or competencies have increased the complexity of program design, appropriation, diffusion and implementation. Various model-based or standardized approaches have been proposed, including IMS-LD, CDM, XCRI or MLO. In this paper, we propose a general model-based approach to curriculum design that has been successfully apply to course syllabi. We show how learning outcomes (and other features) could be incorporated either in a top-down or bottom-up approach. Our approach merges also seamlessly with learning outcome assessment in courses or in portfolios. We also discuss how accreditation reports can be automatically generated.
Direct conversion of human fibroblasts to dopaminergic neurons.

Recent reports demonstrate that somatic mouse cells can be directly converted to other mature cell types by using combined expression of defined factors. Here we show that the same strategy can be applied to human embryonic and postnatal fibroblasts. By overexpression of the transcription factors Ascl1, Brn2, and Myt1l, human fibroblasts were efficiently converted to functional neurons. We also demonstrate that the converted neurons can be directed toward distinct functional neurotransmitter phenotypes when the appropriate transcriptional cues are provided together with the three conversion factors. By combining expression of the three conversion factors with expression of two genes involved in dopamine neuron generation, Lmx1a and FoxA2, we could direct the phenotype of the converted cells toward dopaminergic neurons. Such subtype-specific induced neurons derived from human somatic cells could be valuable for disease modeling and cell replacement therapy.
Security Issues in Mobile Payment Systems

The national exchequer, the banking industry and regular citizens all incur a high overhead in using physical cash. Electronic cash and cell phone-based payment in particular is a viable alternative to physical cash since it incurs much lower overheads and offers more convenience. Because security is of paramount importance in financial transactions, it is imperative that attack vectors in this application be identified and analyzed. In this paper, we investigate vulnerabilities in several dimensions – in choice of hardware/software platform, in technology and in cell phone operating system. We examine how existing and future mobile worms can severely compromise the security of transacting payments through a cell phone.
Design of Cascaded H-Bridge Rectifier for Medium Voltage Applications

In this paper a new control method for voltage balancing of distinct DC buses in cascaded H-bridge rectifier has been proposed. The voltage balancing will be guaranteed even if the load values aren't equal. This structure with the proposed control method is applicable for arbitrary number of series H- bridges, different voltage levels, and different power levels. For reducing current harmonics and distortion, the input current is programmed to be in sinusoidal form and in phase with the input voltage. Besides, it is possible to change the phase angle between input current and input voltage to transfer active or reactive power between load and mains. To confirm the new control method, a 7-levels prototype of cascaded H-bridge rectifier has been made and the results have been shown.
Robust and Efficient Surface Reconstruction From Range Data

We describe a robust but simple algorithm to reconstruct a surface from a set of merged range scans. Our key contribution is the formulation of the surface reconstruction problem as an energy minimisation problem that explicitly models the scanning process. The adaptivity of the Delaunay triangulation is exploited by restricting the energy to inside/outside labelings of Delaunay tetrahedra. Our energy measures both the output surface quality and how well the surface agrees with soft visibility constraints. Such energy is shown to perfectly fit into the minimum s-t cuts optimisation framework, allowing fast computation of a globally optimal tetrahedra labeling, while avoiding the “shrinking bias” that usually plagues graph cuts methods. The behaviour of our method confronted to noise, undersampling and outliers is evaluated on several data sets and compared with other methods through different experiments: its strong robustness would make our method practical not only for reconstruction from range data but also from typically more difficult dense point clouds, resulting for instance from stereo image matching. Our effective modeling of the surface acquisition inverse problem, along with the unique combination of Delaunay triangulation and minimum s-t cuts, makes the computational requirements of the algorithm scale well with respect to the size of the input point cloud.
Advances in MIMO Techniques for Mobile Communications - A Survey

This paper provides a comprehensive overview of critical developments in the field of multiple-input multiple-output (MIMO) wireless communication systems. The state of the art in single-user MIMO (SU-MIMO) and multiuser MIMO (MU-MIMO) communications is presented, highlighting the key aspects of these technologies. Both open-loop and closed-loop SU-MIMO systems are discussed in this paper with particular emphasis on the data rate maximization aspect of MIMO. A detailed review of various MU-MIMO uplink and downlink techniques then follows, clarifying the underlying concepts and emphasizing the importance of MU-MIMO in cellular communication systems. This paper also touches upon the topic of MU-MIMO capacity as well as the promising convex optimization approaches to MIMO system design.
Big Data Quality-Towards an Explanation Model in a Smart City Context

In this paper we present initial research to develop a conceptual model for describing data quality effects in the context of Big Data. Despite the importance of data quality for modern businesses, current research on Big Data Quality is limited. It is particularly unknown how to apply previous data quality models to Big Data. Therefore in this paper we review data quality research from several perspectives and apply the data quality model developed by Helfert & Heinrich with its elements of quality of conformance and quality of design to the context of Big Data. We extend this model by analyzing the effect of three Big Data characteristics (Volume, Velocity and Variety) and discuss its application to the context of Smart Cities, as one interesting example in which Big Data is increasingly important. Although this paper provides only propositions and a first conceptual discussion, we believe that the paper can build a foundation for further empirical research to understand Big Data Quality and its implications in practice.
Design and analysis of a low-power high-speed charge-steering based StrongARM comparator

This paper presents an ultra low-power high-speed dynamic comparator. The proposed dynamic comparator is designed and simulated in a 65-nm CMOS technology. It dissipates 7 μW, 21.1 μW from a 0.9-V supply while operating at 1 GHz, 3 GHz sampling clock respectively. Proposed circuit can work up to 14 GHz. Ultra low power consumption is achieved by utilizing charge-steering concept and proper sizing. Monte Carlo simulations show that the input referred offset contribution of the internal devices is negligible compared to the effect of the input devices which results in 3.8 mV offset and 3 mV kick-back noise.
On Graphs, GPUs, and Blind Dating: A Workload to Processor Matchmaking Quest

Graph processing has gained renewed attention. The increasing large scale and wealth of connected data, such as those accrued by social network applications, demand the design of new techniques and platforms to efficiently derive actionable information from large scale graphs. Hybrid systems that host processing units optimized for both fast sequential processing and bulk processing (e.g., GPUaccelerated systems) have the potential to cope with the heterogeneous structure of real graphs and enable high performance graph processing. Reaching this point, however, poses multiple challenges. The heterogeneity of the processing elements (e.g., GPUs implement a different parallel processing model than CPUs and have much less memory) and the inherent irregularity of graph workloads require careful graph partitioning and load assignment. In particular, the workload generated by a partitioning scheme should match the strength of the processing element the partition is allocated to. This work explores the feasibility and quantifies the performance gains of such low-cost partitioning schemes. We propose to partition the workload between the two types of processing elements based on vertex connectivity. We show that such partitioning schemes offer a simple, yet efficient way to boost the overall performance of the hybrid system. Our evaluation illustrates that processing a 4-billion edges graph on a system with one CPU socket and one GPU, while offloading as little as 25% of the edges to the GPU, achieves 2x performance improvement over state-of-the-art implementations running on a dual-socket symmetric system. Moreover, for the same graph, a hybrid system with dualsocket and dual-GPU is capable of 1.13 Billion breadth-first search traversed edge per second, a performance rate that is competitive with the latest entries in the Graph500 list, yet at a much lower price point.