nils-herrmann/clean_scirepeval huggingface dataset:
SMASH - A Cryptographic Hash Function

This paper presents a new hash function design, which is different from the popular designs of the MD4-family. Seen in the light of recent attacks on MD4, MD5, SHA-0, SHA-1, and on RIPEMD, there is a need to consider other hash function design strategies. The paper presents also a concrete hash function design named SMASH. One version has a hash code of 256 bits and appears to be at least as fast as SHA-256.
A Brand-level Ranking System with the Customized Attention-GRU Model

In e-commerce websites like Taobao, brand is playing a more important role in influencing users’ decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality. However, existing ranking systems are not specifically designed to satisfy this kind of demand. Some design tricks may partially alleviate this problem, but still cannot provide satisfactory results or may create additional interaction cost. In this paper, we design the first brand-level ranking system to address this problem. The key challenge of this system is how to sufficiently exploit users’ rich behavior in e-commerce websites to rank the brands. In our solution, we firstly conduct the feature engineering specifically tailored for the personalized brand ranking problem and then rank the brands by an adapted Attention-GRU model containing three important modifications. Note that our proposed modifications can also apply to many other machine learning models on various tasks. We conduct a series of experiments to evaluate the effectiveness of our proposed ranking model and test the response to the brand-level ranking system from real users on a large-scale e-commerce platform, i.e. Taobao.
Conformal UHF RFID tag antenna mountable on winebottle neck

Wine industry is hungry for item-level UHF RFID solutions. In this paper, a conformal UHF tag antenna directly mountable on the wine bottle neck is proposed. 360-degree readability is achieved by taking advantage of the antenna curving, and good impedance matching is realized by following a co-design approach. Simulation and measurement results demonstrate that the tag antenna provides a 10-dB bandwidth of 96 MHz, and a read range of 3~7 m around a full winebottle.
Hierarchical Item Response Models for Analyzing Public Opinion ∗

Opinion surveys usually employ multiple items to measure the respondent’s underlying value, belief, or attitude. To analyze such types of data, researchers have often followed a two-step approach by first constructing a composite measure and then using it in subsequent analysis. This paper presents a class of hierarchical item response models that help integrate measurement and analysis. In this approach, individual responses to multiple items stem from a latent preference, of which both the mean and variance may depend on observed covariates. Compared with the two-step approach, the hierarchical approach reduces bias, increases efficiency, and facilitates direct comparison across surveys covering different sets of items. Moreover, it enables us to investigate not only how preferences differ among groups, vary across regions, and evolve over time, but also levels, patterns, and trends of attitude polarization and ideological constraint. An open-source R package, hIRT, is available for fitting the proposed models. ∗Direct all correspondence to Xiang Zhou, Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138, USA; email: xiang zhou@fas.harvard.edu. The author thanks Kenneth Bollen, Bryce Corrigan, Ryan Enos, Max Goplerud, Gary King, Jonathan Kropko, Horacio Larreguy, Jie Lv, Christoph Mikulaschek, Barum Park, Pia Raffler, Yunkyu Sohn, Yu-Sung Su, Dustin Tingley, Yuhua Wang, Yu Xie, and Kazuo Yamaguchi for helpful comments on previous versions of this work.
SHOG - Spherical HOG Descriptors for Rotation Invariant 3D Object Detection

We present a method for densely computing local spherical histograms of oriented gradients (SHOG) in volumetric images. The descriptors are based on the continuous representation of the orientation histograms in the harmonic domain, which we compute very efficiently via spherical tensor products and the fast Fourier transformation. Building upon these local spherical histogram representations, we utilize the Harmonic Filter to create a generic rotation invariant object detection system that benefits from both the highly discriminative representation of local image patches in terms of histograms of oriented gradients and an adaptable trainable voting scheme that forms the filter. We exemplarily demonstrate the effectiveness of such dense spherical 3D descriptors in a detection task on biological 3D images. In a direct comparison to existing approaches, our new filter reveals superior performance.
A brief review on upper extremity robotic exoskeleton systems

Robotic exoskeleton systems are one of the highly active areas in recent robotic research. These systems have been developed significantly to be used for the human power augmentation, robotic rehabilitation, human power assist, and haptic interaction in virtual reality. Unlike the robots used in industry, the robotic exoskeleton systems should be designed with special consideration since they directly interact with human user. In the mechanical design of these systems, movable ranges, safety, comfort wearing, low inertia, and adaptability should be especially considered. Controllability, responsiveness, flexible and smooth motion generation, and safety should especially be considered in the controllers of exoskeleton systems. Furthermore, the controller should generate the motions in accordance with the human motion intention. This paper briefly reviews the upper extremity robotic exoskeleton systems. In the short review, it is focused to identify the brief history, basic concept, challenges, and future development of the robotic exoskeleton systems. Furthermore, key technologies of upper extremity exoskeleton systems are reviewed by taking state-of-the-art robot as examples.
Meningococcal purpura fulminans in children: I. Initial orthopedic management.

Purpura fulminans is a rare and extremely severe infection, mostly due to Neisseria meningitidis frequently causing early orthopedic lesions. Few studies have reported on the initial surgical management of acute purpura fulminans. The aim of this study is to look at the predictive factors in orthopedic outcome in light of the initial surgical management in children surviving initial resuscitation.



Nineteen patients referred to our institution between 1987 and 2005 were taken care of at the very beginning of the purpura fulminans. All cases were retrospectively reviewed so as to collect information on the total skin necrosis, vascular insufficiency, gangrene, and total duration of vasopressive treatment.



All patients had multiorgan failure; only one never developed any skin necrosis or ischemia. Eighteen patients lost tissue, leading to 22 skin grafts, including two total skin grafts. There was only one graft failure. Thirteen patients were concerned by an amputation, representing, in total, 54 fingers, 36 toes, two transmetatarsal, and ten transtibial below-knee amputations, with a mean delay of 4 weeks after onset of the disease. Necrosis seems to affect mainly the lower limbs, but there is no predictive factor that impacted on the orthopedic outcome. We did not perform any fasciotomy or compartment pressure measurement to avoid non-perfusion worsening; nonetheless, our outcome in this series is comparable to existing series in the literature. V.A.C.(®) therapy could be promising regarding the management of skin necrosis in this particular context. While suffering from general multiorgan failure, great care should be observed not to miss any additional osseous or articular infection, as some patients also develop local osteitis and osteomyelitis that are often not diagnosed.



We do not advocate very early surgery during the acute phase of purpura fulminans, as it does not change the orthopedic outcome in these children. By performing amputations and skin coverage some time after the acute phase, we obtained similar results to those found in the literature.
Train a 3D U-Net to segment cranial vasculature in CTA volume without manual annotation

Computed tomography angiography (CTA) is now applied as the gold standard in clinical diagnosis of cranial vascular diseases. Segmenting vasculature is a critical step of computer aided diagnosis. In this paper, we adopted a deep learning network architecture 3D U-Net to segment cranial vasculature from CTA images. Different from other traditional methods that require a large amount of manual annotation for network training, we adopted the incomplete vascular segmentation automatically obtained from time-of-flight magnetic resonance angiography (TOF-MRA) volume to train a segmentation network for CTA images. Our results showed that, by carefully tuning the network parameters, relatively complete cranial vascular segmentation can be achieved from CTA volume though the training truth is under-segmented. Our method does not require any human annotation.
Animation of trees in wind using sparse motion capture data

We present a novel approach to animate an entire tree from the motion of a few branches. Animating medium sized trees in wind presents an arduous situation because it is difficult to create the motion of flexible branches in turbulent wind flows. The animation of trees in wind is important in biology and ecology where understanding how trees move can inform us about the cultivation of trees and forests designed to withstand strong winds. Animation of trees in wind is also useful in the production of films and games. We use passive optical motion capture to record the motion of retro-reflective markers placed on some of the tree branch tips. These motion data are processed to remove noise, create motion traces, and add missing data. Given the processed motion data, we solve for wind velocity in a discrete formulation of aerodynamic drag and tree branch dynamics. This sparse collection of wind velocity samples is interpolated over the volume of the crown and enriched with a turbulence model to create a wind field that drives the animation of an entire tree crown. The wind field can also be used to drive the animation of other similar trees. In this process, 300 KB of data recorded from 10 s of tree motion can be processed and replayed in 16 s of computation time, not including rendering.
Design, Implementation and Practical Evaluation of an IoT Home Automation System for Fog Computing Applications Based on MQTT and ZigBee-WiFi Sensor Nodes

In recent years, the improvement of wireless protocols, the development of cloud services and the lower cost of hardware have started a new era for smart homes. One such enabling technologies is fog computing, which extends cloud computing to the edge of a network allowing for developing novel Internet of Things (IoT) applications and services. Under the IoT fog computing paradigm, IoT gateways are usually utilized to exchange messages with IoT nodes and a cloud. WiFi and ZigBee stand out as preferred communication technologies for smart homes. WiFi has become very popular, but it has a limited application due to its high energy consumption and the lack of standard mesh networking capabilities for low-power devices. For such reasons, ZigBee was selected by many manufacturers for developing wireless home automation devices. As a consequence, these technologies may coexist in the 2.4 GHz band, which leads to collisions, lower speed rates and increased communications latencies. This article presents ZiWi, a distributed fog computing Home Automation System (HAS) that allows for carrying out seamless communications among ZigBee and WiFi devices. This approach diverges from traditional home automation systems, which often rely on expensive central controllers. In addition, to ease the platform's building process, whenever possible, the system makes use of open-source software (all the code of the nodes is available on GitHub) and Commercial Off-The-Shelf (COTS) hardware. The initial results, which were obtained in a number of representative home scenarios, show that the developed fog services respond several times faster than the evaluated cloud services, and that cross-interference has to be taken seriously to prevent collisions. In addition, the current consumption of ZiWi's nodes was measured, showing the impact of encryption mechanisms.
Multiple Café au Lait Spots in a Group of Fair-Skinned Children without Signs or Symptoms of Neurofibromatosis Type 1.

The presence of six or more café au lait (CAL) spots is a criterion for the diagnosis of neurofibromatosis type 1 (NF-1). Children with multiple CAL spots are often referred to dermatologists for NF-1 screening. The objective of this case series is to characterize a subset of fair-complected children with red or blond hair and multiple feathery CAL spots who did not meet the criteria for NF-1 at the time of their last evaluation.



We conducted a chart review of eight patients seen in our pediatric dermatology clinic who were previously identified as having multiple CAL spots and no other signs or symptoms of NF-1.



We describe eight patients ages 2 to 9 years old with multiple, irregular CAL spots with feathery borders and no other signs or symptoms of NF-1. Most of these patients had red or blond hair and were fair complected. All patients were evaluated in our pediatric dermatology clinic, some with a geneticist. The number of CAL spots per patient ranged from 5 to 15 (mean 9.4, median 9).



A subset of children, many with fair complexions and red or blond hair, has an increased number of feathery CAL spots and appears unlikely to develop NF-1, although genetic testing was not conducted. It is important to recognize the benign nature of CAL spots in these patients so that appropriate screening and follow-up recommendations may be made.
Design of package cover for 60 GHz small antenna and effects of device box on radiation performance

This paper shows the simulated performance of the small antenna with the effects of the package cover and the device box at 60 GHz band. The total thickness of the package has been reduced from 2.6 mm to 1.4 mm by using the designed cover. The gain is enhanced and the reflection is suppressed lower than -15 dB by using different ways of packaging. On the other hand, the small antenna is put in a 120 mm × 75 mm × 30 mm polycarbonate box in simulation. The radiation performance due to the dielectric box case in practical device has also been simulated and discussed.
The role of gut microbiota in the gut-brain axis: current challenges and perspectives

Brain and the gastrointestinal (GI) tract are intimately connected to form a bidirectional neurohumoral communication system. The communication between gut and brain, knows as the gut-brain axis, is so well established that the functional status of gut is always related to the condition of brain. The researches on the gut-brain axis were traditionally focused on the psychological status affecting the function of the GI tract. However, recent evidences showed that gut microbiota communicates with the brain via the gut-brain axis to modulate brain development and behavioral phenotypes. These recent findings on the new role of gut microbiota in the gut-brain axis implicate that gut microbiota could associate with brain functions as well as neurological diseases via the gut-brain axis. To elucidate the role of gut microbiota in the gut-brain axis, precise identification of the composition of microbes constituting gut microbiota is an essential step. However, identification of microbes constituting gut microbiota has been the main technological challenge currently due to massive amount of intestinal microbes and the difficulties in culture of gut microbes. Current methods for identification of microbes constituting gut microbiota are dependent on omics analysis methods by using advanced high tech equipment. Here, we review the association of gut microbiota with the gut-brain axis, including the pros and cons of the current high throughput methods for identification of microbes constituting gut microbiota to elucidate the role of gut microbiota in the gut-brain axis.
Current understanding of neuroinflammation after traumatic brain injury and cell-based therapeutic opportunities

Traumatic brain injury (TBI) remains a major cause of death and disability worldwide. Increasing evidence indicates that TBI is an important risk factor for neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, and chronic traumatic encephalopathy. Despite improved supportive and rehabilitative care of TBI patients, unfortunately, all late phase clinical trials in TBI have yet to yield a safe and effective neuroprotective treatment. The disappointing clinical trials may be attributed to variability in treatment approaches and heterogeneity of the population of TBI patients as well as a race against time to prevent or reduce inexorable cell death. TBI is not just an acute event but a chronic disease. Among many mechanisms involved in secondary injury after TBI, emerging preclinical studies indicate that posttraumatic prolonged and progressive neuroinflammation is associated with neurodegeneration which may be treatable long after the initiating brain injury. This review provides an overview of recent understanding of neuroinflammation in TBI and preclinical cell-based therapies that target neuroinflammation and promote functional recovery after TBI.
Sarcasm Analysis Using Conversation Context

Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker’s sarcastic intent is not always apparent without additional context. Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection? (2) can we identify what part of conversation context triggered the sarcastic reply? and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic? To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn. We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network, outperform the LSTM model that reads only the current turn. As conversation context, we consider the prior turn, the succeeding turn, or both. Our computational models are tested on two types of social media platforms: Twitter and discussion forums. We discuss several differences between these data sets, ranging from their size to the nature of the gold-label annotations. To address the latter two issues, we present a qualitative analysis of the attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.
Design for Natural Breast Augmentation: The ICE Principle.

The authors' published studies have helped define breast beauty in outlining key parameters that contribute to breast attractiveness. The "ICE" principle puts design into practice. It is a simplified formula for inframammary fold incision planning as part of the process for determining implant selection and placement to reproduce the 45:55 ratio previously described as fundamental to natural breast appearance. The formula is as follows: implant dimensions (I) - capacity of the breast (C) = excess tissue required (E). The aim of this study was to test the accuracy of the ICE principle for producing consistent natural beautiful results in breast augmentation.



A prospective analysis of 50 consecutive women undergoing primary breast augmentation by means of an inframammary fold incision with anatomical or round implants was performed. The ICE principle was applied to all cases to determine implant selection, placement, and incision position. Changes in parameters between preoperative and postoperative digital clinical photographs were analyzed.



The mean upper pole-to-lower pole ratio changed from 52:48 preoperatively to 45:55 postoperatively (p < 0.0001). Mean nipple angulation was also statistically significantly elevated from 11 degrees to 19 degrees skyward (p ≤ 0.0005). Accuracy of incision placement in the fold was 99.7 percent on the right and 99.6 percent on the left, with a standard error of only 0.2 percent. There was a reduction in variability for all key parameters.



The authors have shown using the simple ICE principle for surgical planning in breast augmentation that attractive natural breasts may be achieved consistently and with precision.



Therapeutic, IV.
Unsupervised Learning-Based Fast Beamforming Design for Downlink MIMO

In the downlink transmission scenario, power allocation and beamforming design at the transmitter are essential when using multiple antenna arrays. This paper considers a multiple input–multiple output broadcast channel to maximize the weighted sum-rate under the total power constraint. The classical weighted minimum mean-square error (WMMSE) algorithm can obtain suboptimal solutions but involves high computational complexity. To reduce this complexity, we propose a fast beamforming design method using unsupervised learning, which trains the deep neural network (DNN) offline and provides real-time service online only with simple neural network operations. The training process is based on an end-to-end method without labeled samples avoiding the complicated process of obtaining labels. Moreover, we use the “APoZ”-based pruning algorithm to compress the network volume, which further reduces the computational complexity and volume of the DNN, making it more suitable for low computation-capacity devices. Finally, the experimental results demonstrate that the proposed method improves computational speed significantly with performance close to the WMMSE algorithm.
Adaptive Fuzzy Output Feedback Control of MIMO Nonlinear Systems With Unknown Dead-Zone Inputs

This paper is concerned with the problem of adaptive fuzzy tracking control for a class of multi-input and multi-output (MIMO) strict-feedback nonlinear systems with both unknown nonsymmetric dead-zone inputs and immeasurable states. In this research, fuzzy logic systems are utilized to evaluate the unknown nonlinear functions, and a fuzzy adaptive state observer is established to estimate the unmeasured states. Based on the information of the bounds of the dead-zone slopes as well as treating the time-varying inputs coefficients as a system uncertainty, a new adaptive fuzzy output feedback control approach is developed via the backstepping recursive design technique. It is shown that the proposed control approach can assure that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded. It is also shown that the observer and tracking errors converge to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.