nils-herrmann/clean_scirepeval huggingface dataset:
Analysis and Optimization of Graph Decompositions by Lifted Multicuts

We study the set of all decompositions (clusterings) of a graph through its characterization as a set of lifted multicuts. This leads us to practically relevant insights related to the definition of classes of decompositions by must-join and must-cut constraints and related to the comparison of clusterings by metrics. To find optimal decompositions defined by minimum cost lifted multicuts, we establish some properties of some facets of lifted multicut polytopes, define efficient separation procedures and apply these in a branchand-cut algorithm.
A model of non-elemental olfactory learning in Drosophila

The pathways for olfactory learning in the fruitfly Drosophila have been extensively investigated, with mounting evidence that that the mushroom body is the site of the olfactory associative memory trace (Heisenberg,  2003; Gerber et al.,  2004). Heisenberg’s description of the mushroom body as an associative learning device is a testable hypothesis that relates the mushroom body’s function to its neural structure and input and output pathways. Here, we formalise a relatively complete computational model of the network interactions in the neural circuitry of the insect antennal lobe and mushroom body, to investigate their role in olfactory learning, and specifically, how this might support learning of complex (non-elemental; Giurfa,  2003) discriminations involving compound stimuli. We find that the circuit is able to learn all tested non-elemental paradigms. This does not crucially depend on the number of Kenyon cells but rather on the connection strength of projection neurons to Kenyon cells, such that the Kenyon cells require a certain number of coincident inputs to fire. As a consequence, the encoding in the mushroom body resembles a unique cue or configural representation of compound stimuli (Pearce, Psychol Rev 101:587–607, 1994). Learning of some conditions, particularly negative patterning, is strongly affected by the assumption of normalisation effects occurring at the level of the antennal lobe. Surprisingly, the learning capacity of this circuit, which is a simplification of the actual circuitry in the fly, seems to be greater than the capacity expressed by the fly in shock-odour association experiments (Young et al. 2010).
Omega-3 from Flaxseed Oil Protects Obese Mice Against Diabetic Retinopathy Through GPR120 Receptor

The chronic and low-grade inflammation induced by obesity seem to be the “first hit” to retinopathy associated to diabetes type 2. Herein, we hypothesized that omega-3 fatty acids from flaxseed oil enriched diet disrupt the pro-inflammatory status in the retina, protecting against retinopathy development. For eight weeks under a high-fat diet (HF), several physiological parameters were monitored to follow the metabolic homeostasis disruption. After this period, mice were treated with a HF substituted in part of lard by flaxseed oil (FS) for another eight weeks. Food behavior, weight gain, glucose and insulin sensitivity, electroretinography, RT-qPCR and western blots were carried out. The HF was able to induce a pro-inflammatory background in the retina, changing IL1β and TNFα. VEGF, a master piece of retinopathy, had early onset increased also induced by HF. The FS-diet was able to decrease inflammation and retinopathy and improved retinal electro stimuli compared to HF group. GPR120 and GPR40 (G Protein-Coupled Receptors 120 and 40), an omega-3 fatty acid receptors, were detected in the retina for the first time. FS-diet modulated the gene expression and protein content of these receptors. Thus, unsaturated fatty acids protect the retina from diabetes type 2 mice model from disease progression.
Robust planning with incomplete domain models

Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In this paper, we study planning problems with incomplete domain models where the annotations specify possible preconditions and effects of actions. We show that the problem of assessing the quality of a plan, or its plan robustness, is #P -complete, establishing its equivalence with the weighted model counting problems. We present two approaches to synthesizing robust plans. While the method based on the compilation to conformant probabilistic planning is much intuitive, its performance appears to be limited to only small problem instances. Our second approach based on stochastic heuristic search works well for much larger problems. It aims to use the robustness measure directly for estimating heuristic distance, which is then used to guide the search. Our planning system, PISA, outperforms a state-of-the-art planner handling incomplete domain models in most of the tested domains, both in terms of plan quality and planning time. Finally, we also present an extension of PISA called CPISA that is able to exploit the available of past successful plan traces to both improve the robustness of the synthesized plans and reduce the domain modeling burden.
Effect of retinal defocus on basketball free throw shooting performance.

Vision plays a critical role in athletic performance; however, previous studies have demonstrated that a variety of simulated athletic sensorimotor tasks can be surprisingly resilient to retinal defocus (blurred vision). The purpose of the present study was to extend this work to determine the effect of retinal defocus on overall basketball free throw performance, as well as for the factors gender, refractive error and experience.



Forty-four young adult participants of both genders were recruited. They had a range of refractive errors and basketball experience. Each performed 20 standard basketball free throws under five lens defocus conditions in a randomised manner: plano, +1.50 D, +3.00 D, +4.50 D and +10.00 D.



Overall, free throw performance was significantly reduced under the +10.00 D lens defocus condition only. Previous experience, but neither refractive error nor gender, yielded a statistically significant difference in performance.



Consistent with previous studies of complex sensorimotor tasks, basketball free throw performance was resilient to low and moderate levels of retinal defocus. Thus, for a relatively non-dynamic motor task at a fixed far distance, such as the basketball free throw, precise visual clarity was not critical. Other factors such as motor memory may be important. However, in the dynamic athletic competitive environment it is likely that visual clarity plays a more critical role in one's performance level, at least for specific task demands.
Approaches for teaching computational thinking strategies in an educational game: A position paper

Computer science is expanding into K12 education and numerous educational games and systems have been created to teach programming skills, including many block-based programming environments. Teaching computational thinking has received particular attention, and more research is needed on using educational games to directly teach computational thinking skills. We propose to investigate this using Dragon Architect, an educational block-based programming game we are developing. Specifically, we wish to study ways of directly teaching computational thinking strategies such as divide and conquer in an educational game, as well as ways to evaluate our approaches.
A simulation study of patient flow for day of surgery admission

In this paper the patient flow and perioperative processes involved in day of surgery admissions are considered for a hospital that is undergoing a staged redesign of its operating room. In particular, the day of surgery admission area where patients are prepared for surgery is being relocated and some additional functions for the new unit are being considered. The goal of the simulation study is to map the patient flows and functions of the current area into the newly designed space, to measure potential changes in productivity, and to determine opportunities for future improvements.
Internet of Things (IoT) for Precision Agriculture Application

Internet is experiencing a very explosive growth nowadays with the amount of the devices connecting to it. Earlier we had only personal computers (pCs) and Mobile handset connected to internet but now with Internet of Things i.e. IoT concept of connecting things with internet, millions of device are connecting with it. This development of IoT leads to the idea of machine to machine communication which means that two machines can communicate to each other and also all the data which was previously with private server can now is available on internet so the user can access it remotely. Application of IoT is feasible in almost all industries particularly where speed of communication is not an issue. This paper proposes the application of cloud based IoT in the agriculture domain. Precision agriculture is basically a concept which insists to provide right amount of resources at and for exact duration of time. These resources can be any things such as water, light, pesticides etc. To implement precision agriculture the benefits of IOT has been utilized in the proposed paper. The fundamental idea is to sense all the required parameter from the agriculture field and take required decision to control the actuator. These agriculture parameters are Soil Moisture, Temperature & Relative Humidity around plant, Light intensity. Based on the reading sensed by the sensor suitable action is taken i.e. irrigation valve is actuated based on soil moisture readings, valve for fogger (for spraying water droplet) is actuated based on the Relative humidity(RH) readings etc. This paper proposed the development of the sensor node capable of measuring all these parameter and creating the actuation signal for all the actuator. On top of that sensor nodes are also capable of sending this data to cloud. An Android application is also developed in order to access all these agricultural parameter.
A Task Allocation Schema Based on Response Time Optimization in Cloud Computing

Cloud computing is a newly emerging distributed computing which is evolved from Grid computing. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes so that the tasks can get a balanced allocation or each task’s execution cost decreases to the minimum or the overall system performance is optimal. Unlike the previous task slices’ sequential execution of an independent task in the model of which the target is processing time, we build a model that targets at the response time, in which the task slices are executed in parallel. Then we give its solution with a method based on an improved adjusting entropy function. At last, we design a new task scheduling algorithm. Experimental results show that the response time of our proposed algorithm is much lower than the game-theoretic algorithm and balanced scheduling algorithm and compared with the balanced scheduling algorithm, game-theoretic algorithm is not necessarily superior in parallel although its objective function value is better.
Performance metrics in supply chain management

This survey paper starts with a critical analysis of various performance metrics for supply chain management (SCM), used by a specific manufacturing company. Then it summarizes how economic theory treats multiple performance metrics. Actually, the paper proposes to deal with multiple metrics in SCM via the balanced scorecard — which measures customers, internal processes, innovations, and finance. To forecast how the values of these metrics will change — once a supply chain is redesigned — simulation may be used. This paper distinguishes four simulation types for SCM: (i) spreadsheet simulation, (ii) system dynamics, (iii) discrete-event simulation, and (iv) business games. These simulation types may explain the bullwhip effect, predict fill rate values, and educate and train users. Validation of simulation models requires sensitivity analysis; a statistical methodology is proposed. The paper concludes with suggestions for a possible research agenda in SCM. A list with 50 references for further study is included.  (2003) 00,
Vision through other senses: Practical use of Sensory Substitution devices as assistive technology for visual rehabilitation

Visual-to-auditory Sensory Substitution Devices (SSDs) are non-invasive sensory aids that provide visual information to the blind via their functioning senses, such as audition. For years SSDs have been confined to laboratory settings, but we believe the time has come to use them also for their original purpose of real-world practical visual rehabilitation. Here we demonstrate this potential by presenting for the first time new features of the EyeMusic SSD, which gives the user whole-scene shape, location & color information. These features include higher resolution and attempts to overcome previous stumbling blocks by being freely available to download and run from a smartphone platform. We demonstrate with use the EyeMusic the potential of SSDs in noisy real-world scenarios for tasks such as identifying and manipulating objects. We then discuss the neural basis of using SSDs, and conclude by discussing other steps-in-progress on the path to making their practical use more widespread.
Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowledge graphs (KGs). We introduce a regularized attention mechanism to GCNNs that not only improves performance on clean datasets, but also favorably accommodates noise in KGs, a pervasive issue in realworld applications. Further, we explore new visualization methods for interpretable modelling and to illustrate how the learned representation can be exploited to automate dataset denoising. The results are demonstrated on a synthetic dataset, the common benchmark dataset FB15k-237, and a large biomedical knowledge graph derived from a combination of noisy and clean data sources. Using these improvements, we visualize a learned model’s representation of the disease cystic fibrosis and demonstrate how to interrogate a neural network to show the potential of PPARG as a candidate therapeutic target for rheumatoid arthritis.
Concept and Design of A Fully Autonomous Sewer Pipe Inspection Mobile Robot "KANTARO"

In current conventional method, the sewer pipe inspection is undertaken using a cable-tethered robot with an on-board video camera system, completely, tele-operated by human operator. All commercial sewer inspection robots have platforms with poor mobility functions so that those robots are only capable to move into the straight pipes. Inspecting the sewage pipes using the state of the arts inspection methods by the current robots is costly, mostly human cost, and not fast enough to check and inspect the amount of sewage pipes will grow stronger than it has actually happened, specially in Japan. In order to realize inexpensive and effective inspection system, an autonomous pipe inspection method should be introduced to improve the inspection efficiency by reducing the time and manpower in the inspection process. The development of a fully autonomous pipe inspection system, requires design and development of an un-tethered mobile robot equipped with the required sensors using for autonomous pipe assessment and damage detection, and capability of navigating, completely, autonomously inside of sewer networks including different types of pipe-bends such as curves and junctions. KANTARO, presented in this paper, is the prototype of a passive-active intelligent, fully autonomous, un-tethered robot which has an intelligent modular architecture in its sensor and mechanism. KANTARO prototype robot, including a novel passive-active intelligent moving mechanism, can move into the straight pipe and pass various kinds of pipe bends without need to any intelligence of the controller or sensor reading. In order to realize a fully autonomous inspection robot, we also developed a small and intelligent 2D laser scanner for detecting of the navigational landmarks, independently with the main computer system, and fusion with a fish eye camera to assess the pipe state and fault detection.
E-commerce quality and adoptive elements of e-ticketing for entertainment and sporting events

The present study is an attempt to discover some of the statistically significant outlines motivations and factors that influence the quality in e-ticketing, which affects customers’ perceptions, preferences, and intentions. Consumers, especially business professions – the subjects of this study – are constantly demanding higher quality e-commerce services. All three hypotheses were found to be highly significant and positively related to promoting the perceived value of e-ticketing technologies, especially for sport-related events. Based on technology adoption models, e-ticketing does provide significant levels of perceived value and its linkage to customer satisfaction are important factors as well as operational costs. It seems obvious that box office staffs will become smaller in size as more e-ticketing devices and acceptance increases. Technological applications should continue to grow, and eventual acceptance of ticket kiosks, wireless ticket purchases, will undoubtedly change from being an industry rarity to an industry standard.
Critical Success Factors for the Implementation of Integrated Healthcare Information Systems Projects: An Organizational Fit Perspective

Many healthcare reforms are to digitalize and integrate healthcare information systems. However, the disparity of business benefits in having an integrated healthcare information system (IHIS) varies with organizational fit factors. Critical success factors (CSFs) exist for hospitals to implement an IHIS successfully. This study investigated the relationship between the organizational fit and the system success. In addition, we examined the moderating effect of five CSFs -information systems adjustment, business process adjustment, organizational resistance, top management support, and the capability of key team members – in an IHIS implementation. Fifty-three hospitals that have successfully undertaken IHIS projects participated in this study. We used regression analysis to assess the relationships. The findings of this study provide a roadmap for hospitals to capitalize on the organizational fit and the five critical success factors in order to implement successful IHIS projects. Shin-Yuan Hung, Charlie Chen, Kuan-Hsiuang Wang (2014) "Critical Success Factors For The Implementation Of Integrated Healthcare Information Systems Projects: An Organizational Fit Perspective" Communication of the Association for Information Systems volume 34 Article 39 Version of record Available @ www.aisel.aisnet.org
Where do you know what you know? The representation of semantic knowledge in the human brain

Mr M, a patient with semantic dementia — a neurodegenerative disease that is characterized by the gradual deterioration of semantic memory — was being driven through the countryside to visit a friend and was able to remind his wife where to turn along the not-recently-travelled route. Then, pointing at the sheep in the field, he asked her “What are those things?” Prior to the onset of symptoms in his late 40s, this man had normal semantic memory. What has gone wrong in his brain to produce this dramatic and selective erosion of conceptual knowledge?
LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications

We present the implementation of a largevocabulary continuous speech recognition (LVCSR) system on NVIDIA’s Tegra K1 hyprid GPU-CPU embedded platform. The system is trained on a standard 1000hour corpus, LibriSpeech, features a trigram WFST-based language model, and achieves state-of-the-art recognition accuracy. The fact that the system is realtime-able and consumes less than 7.5 watts peak makes the system perfectly suitable for fast, but precise, offline spoken dialog applications, such as in robotics, portable gaming devices, or in-car systems.
Biomechanical study on pullout strength of thoracic extrapedicular screw fixation.

To identify the biomechanical feasibility of the thoracic extrapedicular approach to the placement of screws.



Five fresh adult cadaveric thoracic spine from T1 to T8 were harvested. The screw was inserted either by pedicular approach or extrapedicular approach. The result was observed and the pullout strength by pedicular screw approach and extrapedicular screw approach via sagittal axis of the vertebrale was measured and compared statistically.



In thoracic pedicular approach, the pullout strength of pedicle screw was 1001.23 N+/-220 N (288.2-1561.7 N)ls and that of thoracic extrapedicular screw approach was 827.01 N+/-260 N when screw was inserted into the vertebrae through transverse process, and 954.25 N+/-254 N when screw was inserted into the vertebrae through the lateral cortex of the pedicle. Compared with pedicular group, the pullout strength in extrapedicular group was decreased by 4.7% inserted through transverse process (P larger than 0.05) and by 17.3% inserted through the lateral cortex (P less than 0.05). The mean pullout strength by extrapedicular approach was decreased by 11.04% as compared with pedicular approach (P less than 0.05).



It is feasible biomechanically to use extrapedicular screw technique to insert pedicular screws in the thoracic spine when it is hard to insert by pedicular approach.
Advanced policy options to regulate sugar-sweetened beverages to support public health.

Consumption of sugar-sweetened beverages (SSBs) has increased worldwide. As public health studies expose the detrimental impact of SSBs, consumer protection and public health advocates have called for increased government control. A major focus has been on restricting marketing of SSBs to children, but many innovative policy options--legally defensible ways to regulate SSBs and support public health--are largely unexplored. We describe the public health, economic, and retail marketing research related to SSBs (including energy drinks). We review policy options available to governments, including mandatory factual disclosures, earmarked taxation, and regulating sales, including placement within retail and food service establishments, and schools. Our review describes recent international initiatives and classifies options available in the United States by jurisdiction (federal, state, and local) based on legal viability.
Pessimistic Uplift Modeling

Uplift modeling is a machine learning technique that aims to model treatment effects heterogeneity. It has been used in business and health sectors to predict the effect of a specific action on a given individual. Despite its advantages, uplift models show high sensitivity to noise and disturbance, which leads to unreliable results. In this paper we show different approaches to address the problem of uplift modeling, we demonstrate how disturbance in data can affect uplift measurement. We propose a new approach, we call it Pessimistic Uplift Modeling, that minimizes disturbance effects. We compared our approach with the existing uplift methods, on simulated and real datasets. The experiments show that our approach outperforms the existing approaches, especially in the case of high noise data environment.