nils-herrmann/clean_scirepeval huggingface dataset:
Real-Time Cloth Rendering with Fiber-Level Detail

Modeling cloth with fiber-level geometry can produce highly realistic details. However, rendering fiber-level cloth models not only has a high memory cost but it also has a high computation cost even for offline rendering applications. In this paper we present a real-time fiber-level cloth rendering method for current GPUs. Our method procedurally generates fiber-level geometric details on-the-fly using yarn-level control points for minimizing the data transfer to the GPU. We also reduce the rasterization operations by collectively representing the fibers near the center of each ply that form the yarn structure. Moreover, we employ a level-of-detail strategy to minimize or completely eliminate the generation of fiber-level geometry that would have little or no impact on the final rendered image. Furthermore, we introduce a simple self-shadow computation method that allows lighting with self-shadows using relatively low-resolution shadow maps. We also provide a simple distance-based ambient occlusion approximation as well as an ambient illumination precomputation approach, both of which account for fiber-level self-occlusion of yarn. Finally, we discuss how to use a physical-based shading model with our fiber-level cloth rendering method and how to handle cloth animations with temporal coherency. We demonstrate the effectiveness of our approach by comparing our simplified fiber geometry to procedurally generated references and display knitwear containing more than a hundred million individual fiber curves at real-time frame rates with shadows and ambient occlusion.
Structured Light in Sunlight

Strong ambient illumination severely degrades the performance of structured light based techniques. This is especially true in outdoor scenarios, where the structured light sources have to compete with sunlight, whose power is often 2-5 orders of magnitude larger than the projected light. In this paper, we propose the concept of light concentration to overcome strong ambient illumination. Our key observation is that given a fixed light (power) budget, it is always better to allocate it sequentially in several portions of the scene, as compared to spreading it over the entire scene at once. For a desired level of accuracy, we show that by distributing light appropriately, the proposed approach requires 1-2 orders lower acquisition time than existing approaches. Our approach is illumination-adaptive as the optimal light distribution is determined based on a measurement of the ambient illumination level. Since current light sources have a fixed light distribution, we have built a prototype light source that supports flexible light distribution by controlling the scanning speed of a laser scanner. We show several high quality 3D scanning results in a wide range of outdoor scenarios. The proposed approach will benefit 3D vision systems that need to operate outdoors under extreme ambient illumination levels on a limited time and power budget.
Synthesis of Secure FPGA Implementations

This paper describes the synthesis of Dynamic Differential Logic to increase the resistance of FPGA implementations against Differential Power Analysis. The synthesis procedure is developed and a detailed description is given of how EDA tools should be used appropriately to implement a secure digital design flow. Compared with an existing technique to implement Dynamic Differential Logic on FPGA, the technique saves a factor 2 in slice utilization. Experimental results also indicate that a secure version of the AES encryption algorithm can now be implemented with a mere 50% increase in time delay and 90% increase in slice utilization when compared with a normal non-secure single ended implementation.
Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking

Dialogue topic tracking is a sequential labelling problem of recognizing the topic state at each time step in given dialogue sequences. This paper presents various artificial neural network models for dialogue topic tracking, including convolutional neural networks to account for semantics at each individual utterance, and recurrent neural networks to account for conversational contexts along multiple turns in the dialogue history. The experimental results demonstrate that our proposed models can significantly improve the tracking performances in human-human conversations.
Streaming video over HTTP with consistent quality

In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.
Ethical challenges in nursing homes--staff's opinions and experiences with systematic ethics meetings with participation of residents' relatives.

Many ethical problems exist in nursing homes. These include, for example, decision-making in end-of-life care, use of restraints and a lack of resources.



The aim of the present study was to investigate nursing home staffs' opinions and experiences with ethical challenges and to find out which types of ethical challenges and dilemmas occur and are being discussed in nursing homes.



The study used a two-tiered approach, using a questionnaire on ethical challenges and systematic ethics work, given to all employees of a Norwegian nursing home including nonmedical personnel, and a registration of systematic ethics discussions from an Austrian model of good clinical practice.



Ninety-one per cent of the nursing home staff described ethical problems as a burden. Ninety per cent experienced ethical problems in their daily work. The top three ethical challenges reported by the nursing home staff were as follows: lack of resources (79%), end-of-life issues (39%) and coercion (33%). To improve systematic ethics work, most employees suggested ethics education (86%) and time for ethics discussion (82%). Of 33 documented ethics meetings from Austria during a 1-year period, 29 were prospective resident ethics meetings where decisions for a resident had to be made. Agreement about a solution was reached in all 29 cases, and this consensus was put into practice in all cases. Residents did not participate in the meetings, while relatives participated in a majority of case discussions. In many cases, the main topic was end-of-life care and life-prolonging treatment.



Lack of resources, end-of-life issues and coercion were ethical challenges most often reported by nursing home staff. The staff would appreciate systematic ethics work to aid decision-making. Resident ethics meetings can help to reach consensus in decision-making for nursing home patients. In the future, residents' participation should be encouraged whenever possible.
A double-blind, placebo-controlled study of quetiapine and paroxetine as monotherapy in adults with bipolar depression (EMBOLDEN II).

The aim of this study was to evaluate the efficacy and tolerability of quetiapine and paroxetine monotherapy for major depression in bipolar disorder.



740 patients (478 bipolar I, 262 bipolar II) with major depressive episodes (DSM-IV) were randomly assigned to quetiapine 300 mg/d (n = 245), quetiapine 600 mg/d (n = 247), paroxetine 20 mg/d (n = 122), or placebo (n = 126) for 8 weeks. The primary end point was the change from baseline in Montgomery-Asberg Depression Rating Scale (MADRS) total score. The study was conducted from May 2005 to May 2007.



Mean MADRS score change from baseline at 8 weeks was -16.19 for quetiapine 300 mg, -16.31 for quetiapine 600 mg, -13.76 for paroxetine, and -12.60 for placebo (P < .001 for both quetiapine doses, P = .313 for paroxetine, vs placebo). Quetiapine-treated (both doses), but not paroxetine-treated, patients showed significantly greater improvements (P < or = .05) in most secondary outcomes measures at week 8 versus the placebo group. Paroxetine significantly improved Hamilton Anxiety Rating Scale scores versus placebo (P < .05) but not MADRS or Hamilton Depression Rating Scale (HDRS) scores. Both quetiapine doses were associated with greater improvements than paroxetine for MADRS and HDRS scores. The most common adverse events were dry mouth, somnolence, sedation, and dizziness with quetiapine (both doses) and dry mouth, sedation, headache, insomnia, and nausea with paroxetine. The incidence of treatment-emergent mania/hypomania was lower with quetiapine compared with paroxetine and placebo.



Quetiapine (300 or 600 mg/d), but not paroxetine, was more effective than placebo for treating acute depressive episodes in bipolar I and II disorder. Quetiapine treatment was generally well tolerated.
Experience Design: Technology for All the Right Reasons

In his In the blink of an eye,Walter Murch, the Oscar-awarded editor of the English Patient, Apocalypse Now, and many other outstanding movies, devises the Rule of Six—six criteria for what makes a good cut. On top of his list is "to be true to the emotion of the moment," a quality more important than advancing the story or being rhythmically interesting. The cut has to deliver a meaningful, compelling, and emotion-rich "experience" to the audience. Because, "what they finally remember is not the editing, not the camerawork, not the performances, not even the story—it’s how they felt." Technology for all the right reasons applies this insight to the design of interactive products and technologies—the domain of Human-Computer Interaction,Usability Engineering,and Interaction Design. It takes an experiential approach, putting experience before functionality and leaving behind oversimplified calls for ease, efficiency, and automation or shallow beautification. Instead, it explores what really matters to humans and what it needs to make technology more meaningful. The book clarifies what experience is, and highlights five crucial aspects and their implications for the design of interactive products. It provides reasons why we should bother with an experiential approach, and presents a detailed working model of experience useful for practitioners and academics alike. It closes with the particular challenges of an experiential approach for design. The book presents its view as a comprehensive, yet entertaining blend of scientific findings, design examples, and personal anecdotes.
Systemic resilience model

It has been realized that resilience as a concept involves several contradictory definitions, both for instance resilience as agile adjustment and as robust resistance to situations. Our analysis of resilience concepts and models suggest that beyond simplistic definitions, it is possible to draw up a systemic resilience model (SyRes) that maintains these opposing characteristics without contradiction. We outline six functions in a systemic model, drawing primarily on resilience engineering, and disaster response: anticipation, monitoring, response, recovery, learning, and self-monitoring. The model consists of four areas: Event-based constraints, Functional Dependencies, Adaptive Capacity and Strategy. The paper describes dependencies between constraints, functions and strategies. We argue that models such as SyRes should be useful both for envisioning new resilience methods and metrics, as well as for engineering and evaluating resilient systems.
How Does Business Analytics Contribute to Business Value?

This paper presents a model, synthesized from the literature, of factors that explain how business analytics contributes to business value. It also reports results from a preliminary test of that model. The model consists of two parts: a process and a variance model. The process model depicts the analyze-insight-decision-action process through which use of an organization’s business-analytic capabilities create business value. The variance model proposes that the five factors in Davenport et al.’s (2010) DELTA model of BA success factors, six from Watson and Wixom (2007), and three from Seddon et al.’s (2010) model of organizational benefits from enterprise systems, assist a firm to gain business value from business analytics. A preliminary test of the model was conducted using data from 100 customer-success stories from vendors such as IBM, SAP, and Teradata. Our conclusion is that the model is likely to be a useful basis for future research.
Incremental Maintenance of Regression Models over Joins

This paper introduces a principled incremental view maintenance (IVM) mechanism for in-database computation described by rings. We exemplify our approach by introducing the covariance matrix ring that we use for learning linear regression models over arbitrary equi-join queries. Our approach is a higher-order IVM algorithm that exploits the factorized structure of joins and aggregates to avoid redundant computation and improve performance. We implemented it in DBToaster, which uses program synthesis to generate high-performance maintenance code. We experimentally show that it can outperform first-order and fully recursive higher-order IVM as well as recomputation by orders of magnitude while using less memory.
Multiview Deep Learning for Predicting Twitter Users' Location

The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing. Twitter user geolocation is a difficult and active research topic with a vast literature. Most of the proposed methods follow either a content-based or a network-based approach. The former exploits user-generated content while the latter utilizes the connection or interaction between Twitter users. In this paper, we introduce a novel method combining the strength of both approaches. Concretely, we propose a multi-entry neural network architecture named MENET leveraging the advances in deep learning and multiview learning. The generalizability of MENET enables the integration of multiple data representations. In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features. Considering the natural distribution of Twitter users across the concerned geographical area, we subdivide the surface of the earth into multi-scale cells and train MENET with the labels of the cells. We show that our method outperforms the state of the art by a large margin on three benchmark datasets.
Treatment of peri-implantitis around TiUnite-surface implants using Er:YAG laser microexplosions.

Implant therapy can lead to peri-implantitis, and none of the methods used to treat this inflammatory response have been predictably effective. It is nearly impossible to treat infected surfaces such as TiUnite (a titanium oxide layer) that promote osteoinduction, but finding an effective way to do so is essential. Experiments were conducted to determine the optimum irradiation power for stripping away the contaminated titanium oxide layer with Er:YAG laser irradiation, the degree of implant heating as a result of Er:YAG laser irradiation, and whether osseointegration was possible after Er:YAG laser microexplosions were used to strip a layer from the surface of implants placed in beagle dogs. The Er:YAG laser was effective at removing an even layer of titanium oxide, and the use of water spray limited heating of the irradiated implant, thus protecting the surrounding bone tissue from heat damage.
A novel approach for graph isomorphism: Handling large graphs

Graph Isomorphism has been proved as most crucial and very difficult process in Pattern Matching. The Graph Isomorphism problem is to check if two graphs are similar or not based on different properties like degree, vertex, edges etc. Two graphs are Isomorphic if they satisfy above properties. A Novel Approach is proposed for Graph Isomorphism Detection Problem (GIDP) based on two different methods. First method is to match an Input Graph with a Model Graph and second method is to match an input graph to the set of Model Graphs (Database of Model Graphs). This Novel Approach is used to solve Isomorphic Problems in an efficient way. Numbers of experiments are performed on large graphs and compared its performance with well-established algorithms like Ullman and VF2.
Asynchronous indoor positioning system based on visible light communications

Indoor positioning has become an attractive research topic within the past two decades. However, no satisfying solution has been found with consideration of both accuracy and system complexity. Recently, research on visible light communications (VLC) offer new opportunities in realizing accurate indoor positioning with relatively simple system configuration. An indoor positioning system based on VLC technology is introduced, with no synchronization requirement on the transmitters. Simulation results show that, with over 95% confidence, the target receiver can be located with an accuracy of 5.9 cm, assuming indirect sunlight exposure and proper installation of light-emitting diode bulbs.
Application of osteopathic manipulative technique in the treatment of back pain during pregnancy.

Changes in body posture, musculoskeletal disorders and somatic dysfunctions are frequently observed during pregnancy especially ligament, joint and myofascial impairment. The aim of the paper is to present the use of osteopathic manipulative treatment (OMT) for back and pelvic pain in pregnancy on the basis of a review of the available literature. MEDLINE and Cochrane Library were searched in January 2014 for relevant reports, randomized controlled trials, clinical and case studies of OMT use in pregnant women. Each eligible source was verified and analyzed by two independent reviewers. OMT procedures appear to be effective and safe for pelvic and spinal pain management in the lumbosacral area in pregnant women.
Capturing the metabolomic diversity of KRAS mutants in non-small-cell lung cancer cells

In non-small-cell lung cancer (NSCLC), one-fifth of patients have KRAS mutations, which are considered a negative predictive factor to first-line therapy. Evidence is emerging that not all KRAS mutations have the same biological activities and possible remodeling of cell metabolism by KRAS activation might complicate the scenario. An open question is whether different KRAS mutations at codon-12 affect cellular metabolism differently with possible implications for different responses to cancer treatments. We applied an explorative mass spectrometry-based untargeted metabolomics strategy to characterize the largest possible number of metabolites that might distinguish isogenic NSCLC cells overexpressing mutated forms of KRAS at codon-12 (G12C, G12D, G12V) and the wild-type. The glutamine deprivation assay and real-time PCR were used to confirm the involvement of some of the metabolic pathways highlighted. Cell clones indicated distinct metabolomic profiles in KRAS wild-type and mutants. Clones harboring different KRAS mutations at codon-12 also had different metabolic remodeling, such as a different redox buffering system and different glutamine-dependency not driven by the transcriptional state of enzymes involved in glutaminolysis. These findings indicate that KRAS mutations at codon-12 are associated with different metabolomic profiles that might affect the responses to cancer treatments.
Nostalgia Conceptual Issues and Existential Functions CONSTANTINE SEDIKIDES TIM WILDSCHUT

Approximately 2,800 years ago, a blind poet wandered from city to city in Greece telling a tall tale—that of a nobleman, war hero, and daring adventurer who did not catch sight of his homeland for 20 long years. The poet was Homer and the larger-than-life character was Odysseus. Homer sang the epic adventures of cunning Odysseus who fought the Trojan War for 10 years and labored for another 10 on a die-hard mission to return to his homeland, the island of Ithaca, and reunite with his loyal wife, Penelope, and their son, Telemachus. Three of the 10 return years were spent on sea, facing the wrath of Gods, monsters, and assorted evil-doers. The other 7 were spent on the island of Ogygia, in the seducing arms of a nymph, the beautiful and possessive Calypso. Yet, despite this dolce vita, Odysseus never took his mind off Ithaca, refusing Calypso’s offer to make him immortal. On the edge of ungratefulness, he confided to his mistress, “Full well I acknowledge Prudent Penelope cannot compare with your stature or beauty, for she is only a mortal, and you are immortal and ageless. Nevertheless it is she whom I daily desire and pine for. Therefore I long for my home and to see the day of returning” (Homer, The Odyssey, trans. 1921, Book V, pp. 78–79). 1 Return was continually on Odysseus’ mind, and the Greek word for it is nostos. His burning wish for nostos afflicted unbearable suffering on Odysseus, and the Greek word for it is algos. Nostalgia, then, is the psychological suffering caused by unrelenting yearning to
When Money Learns to Fly: Towards Sensing as a Service Applications Using Bitcoin

Sensing-as-a-Service (S2aaS) is an emerging Internet of Things (IOT) business model pattern. To be technically feasible and to effectively allow for broad adoption, S2aaS implementations have to overcome manifold systemic hurdles, specifically regarding payment and sensor identification. In an effort to overcome these hurdles, we propose Bitcoin as protocol for S2aaS networks. To lay the groundwork and start the conversation about disruptive changes that Bitcoin technology could bring to S2aaS concepts and IOT in general, we identify and discuss the core characteristics that could drive those changes. We present a conceptual example and describe the basic process of exchanging data for cash using Bitcoin.