nils-herrmann/clean_scirepeval huggingface dataset:
Deep learning based isolated Arabic scene character recognition

The technological advancement and sophistication in cameras and gadgets prompt researchers to have focus on image analysis and text understanding. The deep learning techniques demonstrated well to assess the potential for classifying text from natural scene images as reported in recent years. There are variety of deep learning approaches that prospects the detection and recognition of text, effectively from images. In this work, we presented Arabic scene text recognition using Convolutional Neural Networks (ConvNets) as a deep learning classifier. As the scene text data is slanted and skewed, thus to deal with maximum variations, we employ five orientations with respect to single occurrence of a character. The training is formulated by keeping filter size 3 × 3 and 5 × 5 with stride value as 1 and 2. During text classification phase, we trained network with distinct learning rates. Our approach reported encouraging results on recognition of Arabic characters from segmented Arabic scene images.
A Survey on Neutral-Point-Clamped Inverters

Neutral-point-clamped (NPC) inverters are the most widely used topology of multilevel inverters in high-power applications (several megawatts). This paper presents in a very simple way the basic operation and the most used modulation and control techniques developed to date. Special attention is paid to the loss distribution in semiconductors, and an active NPC inverter is presented to overcome this problem. This paper discusses the main fields of application and presents some technological problems such as capacitor balance and losses.
Drug development in the era of precision medicine

For the past three decades, the use of genomics to inform drug discovery and development pipelines has generated both excitement and scepticism. Although earlier efforts successfully identified some new drug targets, the overall clinical efficacy of developed drugs has remained unimpressive, owing in large part to the heterogeneous causes of disease. Recent technological and analytical advances in genomics, however, have now made it possible to rapidly identify and interpret the genetic variation underlying a single patient's disease, thereby providing a window into patient-specific mechanisms that cause or contribute to disease, which could ultimately enable the 'precise' targeting of these mechanisms. Here, we first examine and highlight the successes and limitations of the earlier phases of genomics in drug discovery and development. We then review the current major efforts in precision medicine and discuss the potential broader utility of mechanistically guided treatments going forward.
In-DBMS Sampling-based Sub-trajectory Clustering

In this paper, we propose an efficient in-DBMS solution for the problem of sub-trajectory clustering and outlier detection in large moving object datasets. The method relies on a two-phase process: a voting-and-segmentation phase that segments trajectories according to a local density criterion and trajectory similarity criteria, followed by a sampling-and-clustering phase that selects the most representative sub-trajectories to be used as seeds for the clustering process. Our proposal, called STClustering (for Sampling-based Sub-Trajectory Clustering) is novel since it is the first, to our knowledge, that addresses the pure spatiotemporal sub-trajectory clustering and outlier detection problem in a real-world setting (by ‘pure’ we mean that the entire spatiotemporal information of trajectories is taken into consideration). Moreover, our proposal can be efficiently registered as a database query operator in the context of extensible DBMS (namely, PostgreSQL in our current implementation). The effectiveness and the efficiency of the proposed algorithm are experimentally validated over synthetic and real-world trajectory datasets, demonstrating that STClustering outperforms an off-the-shelf in-DBMS solution using PostGIS by several orders of magnitude. CCS Concepts • Information systems ➝ Information systems applications ➝ Data mining ➝ Clustering • Information systems ➝ Information systems applications ➝ Spatio-temporal systems
A Bag-of-entities Approach to Document Focus Time Estimation

Detecting the document focus time, defined as the time the content of a document refers to, is an important task to support temporal information retrieval systems. In this paper we propose a novel approach to focus time estimation based on a bag-of-entity representation. In particular, we are interested in understanding if and to what extent existing open data sources can be leveraged to achieve focus time estimation. We leverage state of the art Named Entity Extraction tools and exploit links to Wikipedia and DBpedia to derive temporal information relevant to entities, namely years and intervals of years. We then estimate focus time as the point in time that is more relevant to the entity set associated to a document. Our method does not rely on explicit temporal expressions in the documents, so it is therefore applicable to a general context. We tested our methodology on two datasets of historical events and evaluated it against a state of the art approach, measuring improvement in average estimation error.
Liposome-targeted delivery for highly potent drugs.

Considerable adverse side effects and cytotoxicity of highly potent drugs for healthy tissue require the development of novel drug delivery systems to improve pharmacokinetics and result in selective distribution of the loaded agent. The introduction of targeted liposomal formulations has provided potential solutions for improved drug delivery to cancer cells, penetrating delivery through blood-brain barrier and gene therapy. A large number of investigations have been developed over the past few decades to overcome pharmacokinetics and unfavorable side effects limitations. These improvements have enabled targeted liposome to meet criteria for successful and improved potent drug targeting. Promising in vitro and in vivo results for liposomal-directed delivery systems appear to be effective for vast variety of highly potent therapeutics. This review will focus on the past decade's potential use and study of highly potent drugs using targeted liposomes.
Hiding information and signatures in trapdoor knapsacks

Compressibiity of individuai sequences by the ciam of generaihd finite-atate information-losales encoders ia investigated These encodersrpnoperateinavariabie-ratemodeasweUasaflxedrateone,nnd they aiiow for any fhite-atate acheme of variabie-iength-to-variable-ien@ coding. For every individuai hfiite aeqence x a quantity p (x) ia defined, calledthecompressibilityofx,whirhisshowntobetheasymptotieatly attainable lower bound on the compression ratio tbat cao be achieved for x by any finite-state encoder. ‘flds is demonstrated by means of a amatructivecodtngtbeoremanditsconversethat,apartfnnntheirafymptotic significance, also provide useful performance criteria for finite and practicai data-compression taaka. The proposed concept of compressibility ia aiao shown to play a role analogous to that of entropy in ciaasicai informatfon theory where onedeaia with probabilistic ensembles of aequencea ratk Manuscript received June 10, 1977; revised February 20, 1978.  on leave from the Department of Electrical Engineering, Techmon-Israel Institute of Technology, Halfa, Israel. A. Lempel is with Sperry Research Center, Sudbury, MA 01776, on leave from the Department of Electrical Engineer@, Technion-Israel Institute of Technology, Haifa, Israel. tium with individuai sequences. Widie the delinition of p (x) aiiows a different machine for each different sequence to be compresse4 the constructive coding theorem ieada to a universal algorithm that is aaymik toticaiiy optfmai for au sequencea.
A Framework to Assess Legacy Software Systems

Enterprise organizations require flexible software systems that can handle emerging market needs from time to time, chiefly to ensure the organization has a competitive edge in the market. On the other hand, several organizations still possess legacy software systems, which have definite functionalities but may limit their development progress to comply with emerging e-business needs. Off-theshelf systems might be one possible option if a decision for replacement is made. Equally, improving the architecture of a legacy system can be a valid option to consider as well. The decision on which approach to follow when dealing with a legacy software system must be made based on a thorough investigation of the nature of the system. This paper develops a conceptual framework to analyse legacy software systems in order to support CEOs in making informed decisions about the appropriate approaches to follow in order to keep their organizations functional and
Interpretable Deep Learning applied to Plant Stress Phenotyping

Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce. Here we consider one such real world example viz., accurate identification, classification and quantification of biotic and abiotic stresses in crop research and production. Up until now, this has been predominantly done manually by visual inspection and require specialized training. However, such techniques are hindered by subjectivity resulting from interand intra-rater cognitive variability. Here, we demonstrate the ability of a machine learning framework to identify and classify a diverse set of foliar stresses in the soybean plant with remarkable accuracy. We also present an explanation mechanism using gradientweighted class activation mapping that isolates the visual symptoms used by the model to make predictions. This unsupervised identification of unique visual symptoms for each stress provides a quantitative measure of stress severity, allowing for identification, classification and quantification in one framework. The learnt model appears to be agnostic to species and make good predictions for other (non-soybean) species, demonstrating an ability of transfer learning. Disciplines Agriculture | Agronomy and Crop Sciences | Computer-Aided Engineering and Design Comments This is a pre-print made available through arxiv: https://arxiv.org/abs/1710.08619. Authors Sambuddha Ghosal, David Blystone, Asheesh K. Singh, Baskar Ganapathysubramanian, Arti Singh, and Soumik Sarkar This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/agron_pubs/540 Interpretable Deep Learning applied to Plant Stress Phenotyping Sambuddha Ghosal Department of Mechanical Engineering Iowa State University sghosal@iastate.edu David Blystone Department of Agronomy Iowa State University blystone@iastate.edu Asheesh K. Singh Department of Agronomy Iowa State University singhak@iastate.edu Baskar Ganapathysubramanian Department of Mechanical Engineering Iowa State University baskarg@iastate.edu Arti Singh Department of Agronomy Iowa State University arti@iastate.edu Soumik Sarkar Department of Mechanical Engineering Iowa State University soumiks@iastate.edu
Chambolle's Projection Algorithm for Total Variation Denoising

Denoising is the problem of removing the inherent noise from an image. The standard noise model is additive white Gaussian noise, where the observed image f is related to the underlying true image u by the degradation model f = u+ η, and η is supposed to be at each pixel independently and identically distributed as a zero-mean Gaussian random variable. Since this is an ill-posed problem, Rudin, Osher and Fatemi introduced the total variation as a regularizing term. It has proved to be quite efficient for regularizing images without smoothing the boundaries of the objects. This paper focuses on the simple description of the theory and on the implementation of Chambolle’s projection algorithm for minimizing the total variation of a grayscale image. Furthermore, we adapt the algorithm to the vectorial total variation for color images. The implementation is described in detail and its parameters are analyzed and varied to come up with a reliable implementation. Source Code ANSI C source code to produce the same results as the demo is accessible at the IPOL web page of this article1.
Bed exiting monitoring system with fall detection for the elderly living alone

A bed exiting monitoring system with fall detection function for the elderly living alone is proposed in this paper. By separating the process of exiting or getting on the bed into several significant movements, the sensor system composed of infrared and pressure sensors attached to the bed will correspondingly respond to these movements. Using the finite state machine (FSM) method, the bed exiting state and fall events can be detected according to specific transitions recognized by the sensor system. Experiments with plausible assessment are conducted to find the optimal sensor combination solution and to verify the FSM algorithm, which is demonstrated feasible and effective in practical use.
Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model

The continued increase in the atmospheric concentration of carbon dioxide due to anthropogenic emissions is predicted to lead to significant changes in climate. About half of the current emissions are being absorbed by the ocean and by land ecosystems, but this absorption is sensitive to climate as well as to atmospheric carbon dioxide concentrations, creating a feedback loop. General circulation models have generally excluded the feedback between climate and the biosphere, using static vegetation distributions and CO2 concentrations from simple carbon-cycle models that do not include climate change. Here we present results from a fully coupled, three-dimensional carbon–climate model, indicating that carbon-cycle feedbacks could significantly accelerate climate change over the twenty-first century. We find that under a ‘business as usual’ scenario, the terrestrial biosphere acts as an overall carbon sink until about 2050, but turns into a source thereafter. By 2100, the ocean uptake rate of 5 Gt C yr-1 is balanced by the terrestrial carbon source, and atmospheric CO2 concentrations are 250 p.p.m.v. higher in our fully coupled simulation than in uncoupled carbon models, resulting in a global-mean warming of 5.5 K, as compared to 4 K without the carbon-cycle feedback.
Multi-agent based intrusion prevention and mitigation architecture for software defined networks

Software Defined Networking (SDN) has proved to be a promising approach for creating next generation software based network ecosystems. It has provided us with a centralized network provision, a holistic management plane and a well-defined level of abstraction. But, at the same time brings forth new security and management challenges. Research in the field of SDN is primarily focused on reconfiguration, forwarding and network management issues. However in recent times the interest has moved to tackling security and maintenance issues. This work is based on providing a means to mitigate security challenges in an SDN environment from a DDoS attack based point of view. This paper introduces a Multi-Agent based intrusion prevention and mitigation architecture for SDN. Thus allowing networks to govern their behavior and take appropriate measures when the network is under attack. The architecture is evaluated against filter based intrusion prevention architectures to measure efficiency and resilience against DDoS attacks and false policy based attacks.
Muscle tissue adaptations to hypoxia.

This review reports on the effects of hypoxia on human skeletal muscle tissue. It was hypothesized in early reports that chronic hypoxia, as the main physiological stress during exposure to altitude, per se might positively affect muscle oxidative capacity and capillarity. However, it is now established that sustained exposure to severe hypoxia has detrimental effects on muscle structure. Short-term effects on skeletal muscle structure can readily be observed after 2 months of acute exposure of lowlanders to severe hypoxia, e.g. during typical mountaineering expeditions to the Himalayas. The full range of phenotypic malleability of muscle tissue is demonstrated in people living permanently at high altitude (e.g. at La Paz, 3600-4000 m). In addition, there is some evidence for genetic adaptations to hypoxia in high-altitude populations such as Tibetans and Quechuas, who have been exposed to altitudes in excess of 3500 m for thousands of generations. The hallmark of muscle adaptation to hypoxia in all these cases is a decrease in muscle oxidative capacity concomitant with a decrease in aerobic work capacity. It is thought that local tissue hypoxia is an important adaptive stress for muscle tissue in exercise training, so these results seem contra-intuitive. Studies have therefore been conducted in which subjects were exposed to hypoxia only during exercise sessions. In this situation, the potentially negative effects of permanent hypoxic exposure and other confounding variables related to exposure to high altitude could be avoided. Training in hypoxia results, at the molecular level, in an upregulation of the regulatory subunit of hypoxia-inducible factor-1 (HIF-1). Possibly as a consequence of this upregulation of HIF-1, the levels mRNAs for myoglobin, for vascular endothelial growth factor and for glycolytic enzymes, such as phosphofructokinase, together with mitochondrial and capillary densities, increased in a hypoxia-dependent manner. Functional analyses revealed positive effects on V(O(2)max) (when measured at altitude) on maximal power output and on lean body mass. In addition to the positive effects of hypoxia training on athletic performance, there is some recent indication that hypoxia training has a positive effect on the risk factors for cardiovascular disease.
Bandwidth calendaring: Dynamic services scheduling over Software Defined Networks

Software Defined Networking enables centralized network control and hence paves the way for new services that use network resources more efficiently. Bandwidth Calendaring (BWC) is a typical such example that exploits the knowledge of future to optimally pack the arising demands over the network. In this paper, we consider a generic BWC instance, where a carrier network operator has to accommodate at minimum cost demands of predetermined, but time-varying, bandwidth requirements. Some of the demands may be flexible, i.e., can be scheduled within a specific time window. We demonstrate that the resulting problem is NP-hard and we propose a scalable problem decomposition based on column generation. Our numerical results reveal that the proposed solution approach is near-optimal and outperforms state-of-the art methods based on relaxation and randomized rounding by more than 20% in terms of network cost.
Temporal-Kernel Recurrent Neural Networks

A Recurrent Neural Network (RNN) is a powerful connectionist model that can be applied to many challenging sequential problems, including problems that naturally arise in language and speech. However, RNNs are extremely hard to train on problems that have long-term dependencies, where it is necessary to remember events for many timesteps before using them to make a prediction. In this paper we consider the problem of training RNNs to predict sequences that exhibit significant long-term dependencies, focusing on a serial recall task where the RNN needs to remember a sequence of characters for a large number of steps before reconstructing it. We introduce the Temporal-Kernel Recurrent Neural Network (TKRNN), which is a variant of the RNN that can cope with long-term dependencies much more easily than a standard RNN, and show that the TKRNN develops short-term memory that successfully solves the serial recall task by representing the input string with a stable state of its hidden units.
Mixed Reality System for Virtual Interior Design

In this paper, a mixed reality system for virtual interior design is proposed. The mixed reality technology is a concept covering augmented reality that shows graphic objects integrated to a real environment. This paper describes design of a virtual interior design system that can change and display building inside interior design using mixed reality technologies in a ubiquitous computing environment. The virtual interior design system proposed in this study consists of a hardware platform that can receive and process data from various sensors and a virtual interior design application that displays interior design based on the processed information. The system proposed in this paper will develop into services to the extent which can be directly used in the field if subsequent studies are carried out
Interactions between a poly(3-hydroxybutyrate-co-3-hydroxyvalerate-co-3-hydroxyhexanoate) terpolyester and human keratinocytes.

A new member of polyhydroxyalkanoates (PHA) family, namely, a terpolyester abbreviated as PHBVHHx consisting of 3-hydroxybutyrate (HB), 3-hydroxyvalerate (HV) and 3-hydroxyhexanoate (HHx) that can be produced by recombinant microorganisms, was found to have proper thermo- and mechanical properties for possible skin tissue engineering, as demonstrated by its strong ability to support the growth of human keratinocyte cell line HaCaT. In this study, HaCaT cells showed the strongest viability and the highest growth activity on PHBVHHx film compared with PLA, PHB, PHBV, PHBHHx and P3HB4HB, even the tissue culture plates were grown with less HaCaT cells compared with that on PHBVHHx. To understand its superior biocompatibility, PHBVHHx nanoparticles ranging from 200 to 350nm were prepared. It was found that the nanoparticles could increase the cellular activities by stimulating a rapid increase of cytosolic calcium influx in HaCaT cells, leading to enhanced cell growth. At the same time, 3-hydroxybutyrate (HB), a degradation product and the main component of PHBVHHx, was also shown to promote HaCaT proliferation. Morphologically, under the same preparation conditions, PHBVHHx film showed the most obvious surface roughness under atomic force microscopy (AFM), accompanied by the lowest surface energy compared with all other well studied biopolymers tested above. These results explained the superior ability for PHBVHHx to grow skin HaCaT cells. Therefore, PHBVHHx possesses the suitability to be developed into a skin tissue-engineered material.
Universal Compact Model for Thin-Film Transistors and Circuit Simulation for Low-Cost Flexible Large Area Electronics

Thin-film transistors (TFT) in hydrogenated amorphoussilicon, amorphousmetal oxide, andsmallmolecule and polymer organic semiconductors would all hold promise as potential device candidates to large area flexible electronics applications. A universal compact dc model was developed with a proper balance between the physical and mathematical approaches for these thin-film transistors (TFTs). It can capture the common key parameters used for device performance benchmarking of the different TFTs while being applicable to a wide range of TFT technologies in different materials and device structures. Based on this model, a user-friendly tool was developed to provide an interactive way for convenient parameter extraction. The model is continuous from the off-state and subthreshold regimes to the above-threshold regime, avoiding the convergence problems when being used in SPICE circuit simulations. Finally, for verification, it was implemented into a SPICE circuit simulator using Verilog-A to simulate a TFT circuit examplewith the simulated results agreeing verywell with the experimental measurements.