nils-herrmann/clean_scirepeval huggingface dataset:
Mathematical modeling of cancer cell invasion of tissue: biological insight from mathematical analysis and computational simulation.

The ability of cancer cells to break out of tissue compartments and invade locally gives solid tumours a defining deadly characteristic. One of the first steps of invasion is the remodelling of the surrounding tissue or extracellular matrix (ECM) and a major part of this process is the over-expression of proteolytic enzymes, such as the urokinase-type plasminogen activator (uPA) and matrix metalloproteinases (MMPs), by the cancer cells to break down ECM proteins. Degradation of the matrix enables the cancer cells to migrate through the tissue and subsequently to spread to secondary sites in the body, a process known as metastasis. In this paper we undertake an analysis of a mathematical model of cancer cell invasion of tissue, or ECM, which focuses on the role of the urokinase plasminogen activation system. The model consists of a system of five reaction-diffusion-taxis partial differential equations describing the interactions between cancer cells, uPA, uPA inhibitors, plasmin and the host tissue. Cancer cells react chemotactically and haptotactically to the spatio-temporal effects of the uPA system. The results obtained from computational simulations carried out on the model equations produce dynamic heterogeneous spatio-temporal solutions and using linear stability analysis we show that this is caused by a taxis-driven instability of a spatially homogeneous steady-state. Finally we consider the biological implications of the model results, draw parallels with clinical samples and laboratory based models of cancer cell invasion using three-dimensional invasion assay, and go on to discuss future development of the model.
Performance improvement of a double stage switch mode AC voltage regulator

Widespread use of sensitive loads in industries has required the need for high efficiency voltage regulators in order to provide continuous and fast dynamic adjustable AC voltage. This paper introduces a new control strategy to improve the response time and performance of a double stage switch mode regulator. The proposed strategy responds to input voltage variations quickly and improves the efficiency and performance of the circuit. Results of some simulations are presented and analyzed. Experimental tests on a laboratory prototype verify the excellent performance of the proposed system.
A −8 mV/+15 mV Double Polarity Piezoelectric Transformer-Based Step-Up Oscillator for Energy Harvesting Applications

This paper presents two circuit topologies of battery-less integrated boost oscillators suitable for kick-starting electronic systems in fully discharged states with ultra-low input voltages, in the context of energy harvesting applications based on thermoelectric generators, by coupling a piezoelectric transformer in a feedback loop. With respect to the prior work, the first presented solution is a double polarity circuit designed in a <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS technology able to boost ultra-low positive and negative voltages without using switching matrixes. The circuit exploits a CMOS inverter made up of low threshold transistors, and also includes a hysteretic voltage monitor consuming only ~15 nW to enable an external circuit. The minimum achieved positive and negative oscillation voltages are +15 and −8 mV, which to the best of the authors’ knowledge, are among the lowest start-up voltages achieved in literature up to now without using magnetic components. Moreover, the input impedance in the range of several <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula> makes the presented solution suitable also for high impedances sources, such as rectennas. The second presented circuit, designed in a <inline-formula> <tex-math notation="LaTeX">$0.32~\mu \text{m}$ </tex-math></inline-formula> CMOS technology, exploits an input stage based on depletion-mode MOSFETs in a common source stage configuration and achieves a maximum step ratio of ~60.
Transanal total mesorectal excision: technical aspects of approaching the mesorectal plane from below.

Standardization of transanal total mesorectal excision requires the delineation of the principal procedural components before implementation in practice. This technique is a bottom-up approach to a proctectomy with the goal of a complete mesorectal excision for optimal outcomes of oncologic treatment. A detailed stepwise description of the approach with technical pearls is provided to optimize one's understanding of this technique and contribute to reducing the inherent risk of beginning a new procedure. Surgeons should be trained according to standardized pathways including online preparation, observational or hands-on courses as well as the potential for proctorship of early cases experiences. Furthermore, technological pearls with access to the "video-in-photo" (VIP) function, allow surgeons to link some of the images in this article to operative demonstrations of certain aspects of this technique.
Persistent RNNs: Stashing Recurrent Weights On-Chip

This paper introduces a new technique for mapping Deep Recurrent Neural Networks (RNN) efficiently onto GPUs. We show how it is possible to achieve substantially higher computational throughput at low mini-batch sizes than direct implementations of RNNs based on matrix multiplications. The key to our approach is the use of persistent computational kernels that exploit the GPU’s inverted memory hierarchy to reuse network weights over multiple timesteps. Our initial implementation sustains 2.8 TFLOP/s at a minibatch size of 4 on an NVIDIA TitanX GPU. This provides a 16x reduction in activation memory footprint, enables model training with 12x more parameters on the same hardware, allows us to strongly scale RNN training to 128 GPUs, and allows us to efficiently explore end-to-end speech recognition models with over 100 layers.
Deep Convolutional Neural Networks for Anomaly Event Classification on Distributed Systems

The increasing popularity of server usage has brought a plenty of anomaly log events, which have threatened a vast collection of machines. Recognizing and categorizing the anomalous events thereby is a much salient work for our systems, especially the ones generate the massive amount of data and harness it for technology value creation and business development. To assist in focusing on the classification and the prediction of anomaly events, and gaining critical insights from system event records, we propose a novel log preprocessing method which is very effective to filter abundant information and retain critical characteristics. Additionally, a competitive approach for automated classification of anomalous events detected from the distributed system logs with the state-ofthe-art deep (Convolutional Neural Network) architectures is proposed in this paper. We measure a series of deep CNN algorithms with varied hyper-parameter combinations by using standard evaluation metrics, the results of our study reveals the advantages and potential capabilities of the proposed deep CNN models for anomaly event classification tasks on real-world systems. The optimal classification precision of our approach is 98.14%, which surpasses the popular traditional machine learning methods. Keywords-anomaly event classification; deep learning; convolutional neural network; log preprocessing; distributed system
Impact of Emotional Intelligence on Team Performance in Higher Education Institutes

Claims about the positive influence of emotional intelligence (EI) on work team performance are very numerous, both in commercial and scientific literature. In this study, EI was assessed using the Wong and Law Emotional Intelligence Scale (WLEIS). Paper examined the relationship between emotional intelligence and performance of 15 teams selected from higher education institutes of Pakistan. A single questionnaire was used to gather data from the teams, each team consisting of 5-15 members. Simple and multiple regression was applied to investigate the relationships between emotional intelligence as a whole and team performance and then between different dimensions of emotional intelligence. Results indicated that emotional intelligence had positive impact on team performance. The study recommended that experimental study may be conducted to compare the performance of teams before and after providing the training on emotional intelligence so that a clear picture may emerge.
Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks

This paper describes a language-independent model for multi-class sentiment analysis using a simple neural network architecture of five layers (Embedding, Conv1D, GlobalMaxPooling and two Fully-Connected). The advantage of the proposed model is that it does not rely on language-specific features such as ontologies, dictionaries, or morphological or syntactic pre-processing. Equally important, our system does not use pre-trained word2vec embeddings which can be costly to obtain and train for some languages. In this research, we also demonstrate that oversampling can be an effective approach for correcting class imbalance in the data. We evaluate our methods on three publicly available datasets for English, German and Arabic, and the results show that our system’s performance is comparable to, or even better than, the state of the art for these datasets. We make our source-code publicly available.
Scheduling for heterogeneous Systems using constrained critical paths

A complex computing problem may be efficiently solved on a system with multiple processing elements by dividing its implementation code into several tasks or modules that execute in parallel. The modules may then be assigned to and scheduled on the processing elements so that the total execution time is minimum. Finding an optimal schedule for parallel programs is a non-trivial task and is considered to be NP-complete. For heterogeneous systems having processors with different characteristics, most of the scheduling algorithms use greedy approach to assign processors to the modules. This paper suggests a novel approach called constrained earliest finish time (CEFT) to provide better schedules for heterogeneous systems using the concept of the constrained critical paths (CCPs). In contrast to other approaches used for heterogeneous systems, the CEFT strategy takes into account a broader view of the input task graph. Furthermore, the statically generated CCPs may be efficiently scheduled in comparison with other approaches. The experimentation results show that the CEFT scheduling strategy outperforms the well-known HEFT, DLS and LMT strategies by producing shorter schedules for a diverse collection of task graphs.
Empedobacter brevis Bacteremia in a Patient Infected with HIV: Case Report and Review of Literature

Clinical disease caused by Empedobacter brevis (E. brevis) is very rare. We report the first case of E. brevis bacteremia in a patient with HIV and review the current literature. A 69-year-old man with human immunodeficiency virus (HIV) and CD4 count of 319 presented with chief complaints of black tarry stools, nausea and vomiting for 2 days. Physical exam was significant for abdominal pain on palpation with no rebound or guarding. His total leukocyte count was 32,000 cells/μL with 82% neutrophils and 9% bands. Emergent colonoscopy and endoscopic esophagogastroduodenoscopy showed esophageal candidiasis, a nonbleeding gastric ulcer, and diverticulosis. Blood cultures drawn on days 1, 2, and 3 of hospitalization grew E. brevis. Patient improved with intravenous antibiotics. This case is unusual, raising the possibility of gastrointestinal colonization as a source of the patient's bacteremia. In conclusion, E. brevis is an emerging pathogen that can cause serious health care associated infections.
Viability and antigenicity of anisakis simplex after conventional and microwave heating at fixed temperatures.

Inactivation of parasites in food by microwave treatment may vary due to differences in the characteristics of microwave ovens and food properties. Microwave treatment in standard domestic ovens results in hot and cold spots, and the microwaves do not penetrate all areas of the samples depending on the thickness, which makes it difficult to compare microwave with conventional heat treatments. The viability of Anisakis simplex (isolated larvae and infected fish muscle) heated in a microwave oven with precise temperature control was compared with that of larvae heated in a water bath to investigate any additional effect of the microwaves. At a given temperature, less time was required to kill the larvae by microwaves than by heated water. Microwave treatment killed A. simplex larvae faster than did conventional cooking when the microwaves fully penetrated the samples and resulted in fewer changes in the fish muscle. However, the heat-stable allergen Ani s 4 was detected by immunohistochemistry in the fish muscle after both heat treatments, even at 70°C, suggesting that Ani s 4 allergens were released from the larvae into the surrounding tissue and that the tissues retained their allergenicity even after the larvae were killed by both heat treatments. Thus, microwave cooking will not render fish safe for individuals already sensitized to A. simplex heat-resistant allergens.
3D object classification with point convolution network

Recognizing objects from the point cloud captured by modern 3D sensors is an important task for robots operating autonomously in real-world environments. However, the existing well-performing approaches typically suffer from a trade-off between resolution of representation and computational efficiency. In this paper, raw point cloud normals are fed into the Point Convolution Network (PCN) without any other representation converts. The point cloud set disordered and unstructured problems are tackled by Kd-tree-based local permutation and spatial commutative pooling strategies proposed in this paper. Experiments on ModelNet illustrate that our method has two orders of magnitude less floating point computation in each non-linear mapping layer while it contributes to significant classification accuracy improvement. Compared to some of the state-of-the-art methods using the 3D volumetric image convolution, the PCN method also yields comparable classification accuracy.
Predicting cyber attacks with bayesian networks using unconventional signals

The ability to predict cyber incidents before they occur will help mitigate malicious activities and their impact. This is a challenging task and a departure from intrusion detection where observables of malicious activities are analyzed. Since there is no direct observable before the cyber incident actually happens, the predictive analysis need to be based on non-conventional signals that may or may not be directly related to the potential victim entity. This paper presents our preliminary findings through the use of Bayesian classifier to process signals drawn from global events and social media. The preliminary results show promising prediction performance for an anonymized organization even though the signals are not specific to that organization.
Multiple suicidal injuries with shotgun slugs

Shotguns are usually used to fire multiple pellets, but they are capable of firing single projectiles. Shotgun slug injuries are rare, severe, and fully comparable to those inflicted by high-velocity projectiles. A case of gunshot suicide of a 59-year-old man with a shotgun loaded with shotgun slugs is presented. The first two shots were fired into the heart region, but did not hit the vital organs of the victim’s thorax and did not cause immediate incapacitation. The man was able to reload and refire. The third shot was fired into the region of right temple; the last shot caused severe cerebrocranial gunshot injury and was fatal. The victim did not pull aside his clothing to expose his skin before shooting into the heart region.
Cardiac manifestations of cutaneous disorders.

A number of cutaneous disorders encountered by the dermatologist have overlapping cardiac pathology. In recent years, many genetic linkages common to pathological processes in the cutaneous and cardiovascular systems have been identified. This review will describe primary cutaneous disorders with potential cardiac manifestations, including congenital syndromes, inherited cutaneous disorders associated with later cardiovascular disease, and syndromes associated with early cardiovascular pathology. The dermatologist may be the first to diagnose cutaneous findings associated with underlying cardiovascular disease; therefore, it is of prime importance for the dermatologist to be aware of these associations and to direct the appropriate workup.
Rectangular Slotted Patch Antenna for 5-6GHz Applications

A novel miniature wideband rectangular patch antenna is designed for wireless local area network (WLANs) applications and operating for 5-6 GHz ISM band, and wideband applications. The proposed antenna gives a bandwidth of 4.84 to 6.56 GHz for S11<-10dB. The antenna has the dimensions of 20 mm by 15 mm by 0.8 mm on FR4 substrate. Rectangular slot and step have been used for bandwidth improvement.
Demo abstract: Accurate power profiling of sensornets with the COOJA/MSPsim simulator

Power consumption is of utmost concern in sensor networks. Researchers have several ways of measuring the power consumption of a complete sensor network, but they are typically either impractical or inaccurate. To meet the need for practical and scalable measurement of power consumption of sensor networks, we have developed a cycle-accurate simulator, called COOJA/MSPsim, that enables live power estimation of systems running on MSP430 processors. This demonstration shows the ease of use and the power measurement accuracy of COOJA/MSPsim. The demo setup consists of a small sensor network and a laptop. Beside gathering software-based power measurements from the motes, the laptop runs COOJA/MSPsim to simulate the same network.We visualize the power consumption of both the simulated and the real sensor network, and show that the simulator produces matching results.
Network Verification in the Light of Program Verification

The fastest tools for network reachability queries use adhoc algorithms to compute all packets from a source S that can reach a destination D. This paper examines whether network reachability can be solved efficiently using existing verification tools. While most verification tools only compute reachability (“Can S reach D?”), we efficiently generalize them to compute all reachable packets. Using new and old benchmarks, we compare model checkers, SAT solvers and various Datalog implementations. The only existing verification method that worked competitively on all benchmarks in seconds was Datalog with a new composite Filter-Project operator and a Difference of Cubes representation. While Datalog is slightly slower than the Hassel C tool, it is far more flexible. We also present new results that more precisely characterize the computational complexity of network verification. This paper also provides a gentle introduction to program verification for the networking community.
A model of contextual interactions and contour detection in primary visual cortex

A new model of contour extraction and perceptual grouping in the primary visual cortex is presented and discussed. It differs from previous models since it incorporates four main mechanisms, according to recent physiological data: a feed-forward input from the lateral geniculate nucleus, characterized by Gabor elongated receptive fields; an inhibitory feed-forward input, maximally oriented in the orthogonal direction of the target cell, which suppresses non-optimal stimuli and warrants contrast invariance; an excitatory cortical feedback, which respects co-axial and co-modularity criteria; and a long-range isotropic feedback inhibition. Model behavior has been tested on artificial images with contours of different curvatures, in the presence of considerable noise or in the presence of broken contours, and on a few real images. A sensitivity analysis has also been performed on the role of intracortical synapses. Results show that the model can extract correct contours within acceptable time from image presentation (30-40 ms). The feed-forward input plays a major role to set an initial correct bias for the subsequent feedback and to ensure contrast-invariance. Long-range inhibition is essential to suppress noise, but it may suppress small contours due to excessive competition with greater contours. Cortical excitation sharpens the initial bias and improves saliency of the contours. Model results support the idea that contour extraction is one the primary steps in the visual processing stream, and that local processing in V1 is able to solve this task even in difficult conditions, without the participation of higher visual centers.
Literacy acquisition reduces the influence of automatic holistic processing of faces and houses

Writing was invented too recently to have influenced the human genome. Consequently, reading acquisition must rely on partial recycling of pre-existing brain systems. Prior fMRI evidence showed that in literates a left-hemispheric visual region increases its activation to written strings relative to illiterates and reduces its response to faces. Increasing literacy also leads to a stronger right-hemispheric lateralization for faces. Here, we evaluated whether this reorganization of the brain's face system has behavioral consequences for the processing of non-linguistic visual stimuli. Three groups of adult illiterates, ex-illiterates and literates were tested with the sequential composite face paradigm that evaluates the automaticity with which faces are processed as wholes. Illiterates were consistently more holistic than participants with reading experience in dealing with faces. A second experiment replicated this effect with both faces and houses. Brain reorganization induced by literacy seems to reduce the influence of automatic holistic processing of faces and houses by enabling the use of a more analytic and flexible processing strategy, at least when holistic processing is detrimental to the task.
Creating a gold standard corpus for terminological annotation from online forum data

We present ongoing work on a gold standard annotation of German terminology in an inhomogeneous domain. The text basis is thematically broad and contains various registers, from expert text to user-generated data taken from an online discussion forum. We identify issues related with these properties, and show our approach how to model the domain. Futhermore, we present our approach to handle multiword terms, including discontinuous ones. Finally, we evaluate the annotation quality.
Hierarchische hybride Planung für mobile Roboter

To act autonomously and robustly in complex environments, mobile robots require task planning to adapt to the given situation. With the growing capabilities of robot hardware and software this is becoming increasingly important. Yet, only few integrated robotic systems exist. This thesis attempts to make a next step towards robust plan-based robot control. For this, it first describes how the HTN-Planner SHOP2 [117] can be used in a robot control architecture that aims to improve its performance based on experiences. Based on that, means to increase the systems robustness by integrating the planner with other components are discussed. The environments and tasks of mobile robots contain various forms of knowledge like information about temporal requirements or resources that should be reasoned about in order to achieve a safe and robust behaviour. However, this cannot be fully used by planners like SHOP2. Therefore, this thesis presents the hierarchical hybrid planner CHIMP which combines the advantages of hierarchical planning and hybrid planning with different forms of knowledge as a MetaCSP [28, 104]. Furthermore, CHIMP’s plans can contain actions that can be executed in parallel, and additional goal tasks can be inserted into an existing plan during its execution.