nils-herrmann/clean_scirepeval huggingface dataset:
Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection

Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving. Recently, many deep learning methods spring up for this task, because they can extract high-level local features to find road regions from raw RGB data, such as convolutional neural networks and fully convolutional networks (FCNs). However, how to detect the boundary of road accurately is still an intractable problem. In this paper, we propose siamesed FCNs (named “s-FCN-loc”), which is able to consider RGB-channel images, semantic contours, and location priors simultaneously to segment the road region elaborately. To be specific, the s-FCN-loc has two streams to process the original RGB images and contour maps, respectively. At the same time, the location prior is directly appended to the siamesed FCN to promote the final detection performance. Our contributions are threefold: 1) An s-FCN-loc is proposed that learns more discriminative features of road boundaries than the original FCN to detect more accurate road regions. 2) Location prior is viewed as a type of feature map and directly appended to the final feature map in s-FCN-loc to promote the detection performance effectively, which is easier than other traditional methods, namely, different priors for different inputs (image patches). 3) The convergent speed of training s-FCN-loc model is 30% faster than the original FCN because of the guidance of highly structured contours. The proposed approach is evaluated on the KITTI road detection benchmark and one-class road detection data set, and achieves a competitive result with the state of the arts.
Identifying and assessing anxiety in pre-operative patients.

control. McCleane and Cooper (1990) suggested that anxiety begins as soon as the surgical procedure is planned and increases to maximum intensity on admission to hospital. Some researchers have speculated about the reasons why patients experience anxiety (Koscielniak-Nielsen et al 2002, Matthey et al 2004, Jakobsen and Fagermoen 2005, Mitchell 2009). Anxiety is caused by a variety of factors, including concerns about the surgical procedure and the anaesthetic, and the perceived consequences of surgery (Box 1). The patient’s previous experiences of surgery and the experiences of other family members or friends are also important considerations. Anxiety causes a wide range of responses. Physiological responses include tachycardia, hypertension, elevated temperature, sweating, nausea and a heightened sense of touch, smell or hearing (Pritchard 2009). Psychological responses include changes in behaviour such as increased tension, apprehension, nervousness and aggression (Markland and Hardy 1993). Anxiety is a subjective emotion. It may be influenced by age, gender, the extent and type of surgery, previous hospital experiences, and susceptibility to and ability to cope with stressful experiences (Boker et al 2002). Studies have reported on the efficacy of a variety of interventions to reduce anxiety, such as how much information should be given to patients and what format to use (Andrewes et al 1999, Montgomery and Bovbjerg 2004, Osborn and Sandler 2004). However, questions remain about how patients grade their levels of anxiety and how anxiety relates to physiology. Anxiety is a genuine response to undergoing surgery. The provision of appropriate information is essential in reducing anxiety and distress among pre-operative patients. Mitchell (2009) stated that: Identifying and assessing anxiety in pre-operative patients
Hyperglycemia and antipsychotic medications.

Type 2 diabetes mellitus and impaired glucose tolerance are associated with antipsychotic treatment. Risk factors for type 2 diabetes and impaired glucose tolerance include abdominal adiposity, age, ethnic status, and certain neuropsychiatric conditions. While impaired glucose metabolism was first described in psychotic patients prior to the introduction of antipsychotic medications, treatment with antipsychotic medications is associated with impaired glucose metabolism, exacerbation of existing type 1 and 2 diabetes, new-onset type 2 diabetes mellitus, and diabetic ketoacidosis, a severe and potentially fatal metabolic complication. The strength of the association between antipsychotics and diabetes varies across individual medications, with the largest number of reports for chlorpromazine, clozapine, and olanzapine. Recent controlled studies suggest that antipsychotics can impair glucose regulation by decreasing insulin action, although effects on insulin secretion are not ruled out. Antipsychotic medications induce weight gain, and the potential for weight gain varies across individual agents with larger effects observed again for agents like chlorpromazine, clozapine, and olanzapine. Increased abdominal adiposity may explain some treatment-related changes in glucose metabolism. However, case reports and recent controlled studies suggest that clozapine and olanzapine treatment may also be associated with adverse effects on glucose metabolism independent of adiposity. Dyslipidemia is a feature of type 2 diabetes, and antipsychotics such as clozapine and olanzapine have also been associated with hypertriglyceridemia, with agents such as haloperidol, risperidone, and ziprasidone associated with reductions in plasma triglycerides. Diabetes mellitus is associated with increased morbidity and mortality due to both acute (e.g., diabetic ketoacidosis) and long-term (e.g., cardiovascular disease) complications. A progressive relationship between plasma glucose levels and cardiovascular risk (e.g., myocardial infarction, stroke) begins at glucose levels that are well below diabetic or "impaired" thresholds. Increased adiposity and dyslipidemia are additional, independent risk factors for cardiovascular morbidity and mortality. Patients with schizophrenia suffer increased mortality due to cardiovascular disease, with presumed contributions from a number of modifiable risk factors (e.g., smoking, sedentary lifestyle, poor diet, obesity, hyperglycemia, and dyslipidemia). Patients taking antipsychotic medications should undergo regular monitoring of weight and plasma glucose and lipid levels, so that clinicians can individualize treatment decisions and reduce iatrogenic contributions to morbidity and mortality.
Conditional Random Field Autoencoders for Unsupervised Structured Prediction

We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input’s latent representation is predicted conditional on the observed data using a feature-rich conditional random field (CRF). Then a reconstruction of the input is (re)generated, conditional on the latent structure, using a generative model which factorizes similarly to the CRF. The autoencoder formulation enables efficient exact inference without resorting to unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate connections to traditional autoencoders, posterior regularization, and multi-view learning. We then show competitive results with instantiations of the framework for two canonical tasks in natural language processing: part-of-speech induction and bitext word alignment, and show that training the proposed model can be substantially more efficient than a comparable feature-rich baseline.
Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face

The human face is a primary medium of human communication and a prominent source of information used to infer various attributes. In this paper, we study a fully automated system that can infer the perceived traits of a person from his face -- social dimensions, such as "intelligence," "honesty," and "competence" -- and how those traits can be used to predict the outcomes of real-world social events that involve long-term commitments, such as political elections, job hires, and marriage engagements. To this end, we propose a hierarchical model for enduring traits inferred from faces, incorporating high-level perceptions and intermediate-level attributes. We show that our trained model can successfully classify the outcomes of two important political events, only using the photographs of politicians' faces. Firstly, it classifies the winners of a series of recent U. S. elections with the accuracy of 67.9% (Governors) and 65.5% (Senators). We also reveal that the different political offices require different types of preferred traits. Secondly, our model can categorize the political party affiliations of politicians, i.e., Democrats vs. Republicans, with the accuracy of 62.6% (male) and 60.1% (female). To the best of our knowledge, our paper is the first to use automated visual trait analysis to predict the outcomes of real-world social events. This approach is more scalable and objective than the prior behavioral studies, and opens for a range of new applications.
SRAM for Error-Tolerant Applications With Dynamic Energy-Quality Management in 28 nm CMOS

In this paper, a voltage-scaled SRAM for both error-free and error-tolerant applications is presented that dynamically manages the energy/quality trade-off based on application need. Two variation-resilient techniques, write assist and Error Correcting Code, are selectively applied to bit positions having larger impact on the overall quality, while jointly performing voltage scaling to improve overall energy efficiency. The impact of process variations, voltage and temperature on the energy-quality tradeoff is investigated. A 28 nm CMOS 32 kb SRAM shows 35% energy savings at iso-quality and operates at a supply 220 mV below a baseline voltage-scaled SRAM, at the cost of 1.5% area penalty. The impact of the SRAM quality at the system level is evaluated by adopting a H.264 video decoder as case study.
MAINSTREAMING GENDER EQUALITY IN EUROPE. A FRAME ANALYSIS APPROACH

Beijing 1995, gender mainstreaming has heralded the beginning of a renewed effort to address what is seen as one of the roots of gender inequality: the genderedness of systems, procedures and organizations. In the definition of the Council of Europe, gender mainstreaming is the (re)organisation, improvement, development and evaluation of policy processes, so that a gender equality perspective is incorporated in all policies at all levels and at all stages, by the actors normally involved in policymaking. All member states and some candidate states of the European Union have started to implement gender mainstreaming. The 1997 Treaty of Amsterdam places equality between women and men among the explicit tasks of the European Union and obliges the EU to promote gender equality in all its tasks and activities. The Gender Mainstreaming approach that has been legitimated by this Treaty is backed by legislation and by positive action in favour of women (or the “under-represented sex”). Gender equality policies have not only been part and parcel of modernising action in the European Union, but can be expected to continue to be so (Rossili 2000). With regard to gender inequality, the EU has both a formal EU problem definition at the present time, and a formalised set of EU strategies. Problems in the implementation of gender equality policies abound, at both national and EU level. To give just one example, it took the Netherlands – usually very supportive of the EU –14 years to implement article 119 on Equal Pay (Van der Vleuten 2001). Moreover, it has been documented that overall EU action has run counter to its goal of gender equality. Overall EU action has weakened women’s social rights more seriously than men’s (Rossili 2000). The introduction of Gender Mainstreaming, the incorporation of gender and women’s concerns in all regular policymaking is meant to address precisely this problem of a contradiction between specific gender policies and regular EU policies. Yet, in the case of the Structural Funds, for instance, Gender Mainstreaming has been used to further reduce existing funds and incentives for gender equality (Rossili 2000). Against this backdrop, this paper will present an approach at studying divergences in policy frames around gender equality as one of the elements connected to implementation problems: the MAGEEQ project.
A new cell counter based attack against tor

Various low-latency anonymous communication systems such as Tor and Anoymizer have been designed to provide anonymity service for users. In order to hide the communication of users, many anonymity systems pack the application data into equal-sized cells (e.g., 512 bytes for Tor, a known real-world, circuit-based low-latency anonymous communication network). In this paper, we investigate a new cell counter based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the counter of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g. using our hopping-based signal embedding).
Attention Networks for Aspect-Level Sentiment Classification

Aspect-level sentiment classification aims at identifying the sentiment polarity of specific target in its context. Previous approaches have realized the importance of targets in sentiment classification and developed various methods with the goal of precisely modeling their contexts via generating target-specific representations. However, these studies always ignore the separate modeling of targets. In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning. Then, we propose the interactive attention networks (IAN) to interactively learn attentions in the contexts and targets, and generate the representations for targets and contexts separately. With this design, the IAN model can well represent a target and its collocative context, which is helpful to sentiment classification. Experimental results on SemEval 2014 Datasets demonstrate the effectiveness of our model.
System-Level Virtualization for High Performance Computing

System-level virtualization has been a research topic since the 70's but regained popularity during the past few years because of the availability of efficient solution such as Xen and the implementation of hardware support in commodity processors (e.g. Intel-VT, AMD-V). However, a majority of system-level virtualization projects is guided by the server consolidation market. As a result, current virtualization solutions appear to not be suitable for high performance computing (HPC) which is typically based on large-scale systems. On another hand there is significant interest in exploiting virtual machines (VMs) within HPC for a number of other reasons. By visualizing the machine, one is able to run a variety of operating systems and environments as needed by the applications. Virtualization allows users to isolate workloads, improving security and reliability. It is also possible to support non-native environments and/or legacy operating environments through virtualization. In addition, it is possible to balance work loads, use migration techniques to relocate applications from failing machines, and isolate fault systems for repair. This document presents the challenges for the implementation of a system-level virtualization solution for HPC. It also presents a brief survey of the different approaches and techniques to address these challenges.
Solving the subset-sum problem with a light-based device

We propose an optical computational device which uses light rays for solving the subset-sum problem. The device has a graph-like representation and the light is traversing it by following the routes given by the connections between nodes. The nodes are connected by arcs in a special way which lets us to generate all possible subsets of the given set. To each arc we assign either a number from the given set or a predefined constant. When the light is passing through an arc it is delayed by the amount of time indicated by the number placed in that arc. At the destination node we will check if there is a ray whose total delay is equal to the target value of the subset sum problem (plus some constants). The proposed optical solution solves a NP-complete problem in time proportional with the target sum, but requires an exponential amount of energy.
Selection of the Forecasting Model in Health Care

O management in service industry, expecially in Health Care is so crucial. There is no sector that the importance of planning could be underestimated, hospital management is one of them. It is the one that effects human life, for that reason forecasting should be done carefully. Forecasting is one of the first steps in planning, the success of the plans depends on the accuracy of the forecasts. In the service industries like the hospitals, there are many plans that depends on the forecasts, from capacity planning to aggregate planning, from layout decisions to the daily schedules. In this paper, many forecasting methods are studied and the accuracy of the forecasts are determined by the error indicators.
Inkjet-printed optoelectronics.

Inkjet printing is a powerful and cost-effective technique for deposition of liquid inks with high accuracy, which is not only of great significance for graphic applications but also has enormous potential for the direct printing of optoelectronic devices. This review highlights a comprehensive overview of the progress that has been made in optoelectronics fabrication by the inkjet printing technique. The first part briefly covers the droplet-generation process in the nozzles of printheads and the physical properties affecting droplet formation and the profiles of the printed patterns. The second section outlines the recent activities related to applications of inkjet printing in optoelectronics fabrication including solar cells, light-emitting diodes, photodetectors and transparent electrodes. In each application field, the challenges with the inkjet printing process and the possible solutions are discussed before a few remarks. In the last section, a brief summary on the progress of inkjet printing fabrication of optoelectronics and an outlook for future research effort are presented.
Doctrine for Cybersecurity

A succession of doctrines for enhancing cybersecurity has been advocated in the past, including prevention, risk management, and deterrence through accountability. None has proved effective. Proposals that are now being made view cybersecurity as a public good and adopt mechanisms inspired by those used for public health. This essay discusses the failings of previous doctrines and surveys the landscape of cybersecurity through the lens that a new doctrine, public cybersecurity, provides.
CS 0.5: a better approach to introductory computer science for majors

There are often problems when students enter a course with widely different experience levels with key course topics. If the material is covered too slowly, those with greater experience get bored and lose interest. If the material is covered too quickly, those with less experience get lost and feel incompetent. This problem with incoming students of our Computer Science Major led us to create CS 0.5: an introductory Computer Science course to target those CS majors who have little or no background with programming. Our goal is to provide these students with an engaging curriculum and prepare them to keep pace in future courses with those students who enter with a stronger background.
 Following the lead of Mark Guzdial's work on using media computation for non-majors at Georgia Tech, we use media computation as the tool to provide this engaging curriculum. We report here on our experience to date using the CS 0.5 approach with a media computation course.
A 40-Gb/s PAM-4 Transmitter Based on a Ring-Resonator Optical DAC in 45-nm SOI CMOS

The next generations of large-scale data-centers and supercomputers demand optical interconnects to migrate to 400G and beyond. Microring modulators in silicon-photonics VLSI chips are promising devices to meet this demand due to their energy efficiency and compatibility with dense wavelength division multiplexed chip-to-chip optical I/O. Higher order pulse amplitude modulation (PAM) schemes can be exploited to mitigate their fundamental energy–bandwidth tradeoff at the system level for high data rates. In this paper, we propose an optical digital-to-analog converter based on a segmented microring resonator, capable of operating at 20 GS/s with improved linearity over conventional optical multi-level generators that can be used in a variety of applications such as optical arbitrary waveform generators and PAM transmitters. Using this technique, we demonstrate a PAM-4 transmitter that directly converts the digital data into optical levels in a commercially available 45-nm SOI CMOS process. We achieved 40-Gb/s PAM-4 transmission at 42-fJ/b modulator and driver energies, and 685-fJ/b total transmitter energy efficiency with an area bandwidth density of 0.67 Tb/s/mm2. The transmitter incorporates a thermal tuning feedback loop to address the thermal and process variations of microrings’ resonance wavelength. This scheme is suitable for system-on-chip applications with a large number of I/O links, such as switches and general-purpose and specialized processors in large-scale computing and storage systems.
A Novel Robotic Platform for Aerial Manipulation Using Quadrotors as Rotating Thrust Generators

We propose a novel robotic platform for aerial operation and manipulation, a spherically connected multiquadrotor (SmQ) platform, which consists of a rigid frame and multiple quadrotors that are connected to the frame via passive spherical joints and act as distributed rotating thrust generators to collectively propel the frame by adjusting their attitude and thrust force. Depending on the number of quadrotors and their configuration, this SmQ platform can fully (or partially) overcome the issues of underactuation of the standard multirotor drones for aerial operation/manipulation (e.g., body-tilting with sideway gust/force, dynamic interaction hard to attain, complicated arm–drone integration, etc.). We present the dynamics modeling of this SmQ platform system and establish the condition for its full actuation in SE(3). We also show how to address limited range of spherical joints and rotor saturations as a constrained optimization problem by noticing the similarity with the multifingered grasping problem under the friction-cone constraint. We then design and analyze feedback control laws for the S3Q and S2Q systems as a combination of high-level Lyapunov control design and low-level constrained optimization and show that the (fully actuated) S3Q system can assume any trajectory in SE(3), whereas the S2Q system in  $\Re^3\, \times$ S $^2$ with its unactuated dynamics is still internally stable. Experiments are also performed to show the efficacy of the theory.