nils-herrmann/clean_scirepeval huggingface dataset:
A wide-range and fast-locking all-digital cycle-controlled delay-locked loop

An all-digital cycle-controlled delay-locked loop (DLL) is presented to achieve wide range operation, fast lock and process immunity. Utilizing the cycle-controlled delay unit, the proposed DLL reuses the delay units to enlarge the operating frequency range rather than cascade a huge number of delay units. Adopting binary search scheme, the two-step successive-approximation-register (SAR) controller ensures the proposed DLL to lock the input clock within 32 clock cycles regardless of input frequencies. The DLL operates in open-loop fashion once lock occurs in order to achieve low jitter operation with small area and low power dissipation. Since the DLL will not track temperature or supply variations once it is in lock, it is best suited for burst mode operation. Given a supplied reference input with 50% duty cycle, the DLL generates an output clock with the duty cycle of nearly 50% over the entire operating frequency range. Fabricated in a 0.18-/spl mu/m CMOS one-poly six-metal (1P6M) technology, the experimental prototype exhibits a wide locking range from 2 to 700 MHz while consuming a maximum power of 23 mW. When the operating frequency is 700 MHz, the measured peak-to-peak jitter and rms jitter is 17.6 ps and 2.0 ps, respectively.
Bus as a sensor: A mobile sensor nodes network for the air quality monitoring

Air pollution is an important issue due to its direct impact on human health. To cope with this problem, in urban areas a “monitor and react” approach, based on measurements provided use of static monitoring stations, is commonly adopted introducing constrains to the amount of areas that can be monitored within the city. With the rise of the Internet of Things paradigm, new air monitoring models based on mobile sensors networks have been proposed. In this paper, we present the concept of Bus as a Sensor (BaaS): an air quality monitoring system based on mobile sensor nodes placed upon buses. This work aims to provide a high resolution air quality map to report pollutants measurements and facilitate the analysis to support the government decisions in order to reduce the pollution. The proposed paper describes the system architecture and the requirements of sensor nodes to depict the implementation concepts and lay the foundations for future experiments on field. A preliminary end-to-end prototype of the BaaS solution has been already developed at the TIM Joint Open Lab based in Catania and some tests have been successfully carried out within the University campus.
Programmatically Interpretable Reinforcement Learning

We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent policies. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods. We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maximal reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural “oracle”. We evaluate NDPS on the task of learning to drive a simulated car in the TORCS carracing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some significant performance bars. We also show that PIRL policies can have smoother trajectories, and can be more easily transferred to environments not encountered during training, than corresponding policies discovered by DRL.
A clutched parallel elastic actuator concept: Towards energy efficient powered legs in prosthetics and robotics

Parallel passive-elastic elements can reduce the energy consumption and torque requirements for motors in powered legged systems. However, the hardware design for such combined actuators is challenged by the need to engage and disengage the parallel elasticity depending on the gait phase. Although clutches in the drive train are often proposed, compact and low cost solutions of clutched parallel elastic actuators have so far not been established. Here we present the design and control of an initial prototype for a parallel elastic actuator. The actuator combines a DC motor with a parallel spring that is engaged and disengaged by a commercially available, compact and low-cost electric clutch. In experiments that mimic the torque and motion patterns of knee extensor muscles in human rebounding tasks we find that the parallel spring in the prototype reduces the energy consumption of the actuator by about 80% and the peak torque requirement for the DC motor by about 66%. In addition, we find that a simple trigger-based control can reliably engage and disengage the electric clutch during the motion, allowing the spring to support the motor in rebound, to remove stored energy from the system as necessary for stopping, and to virtually disappear at the actuator output level. On the other hand, the hardware experiments also reveal that our initial design limits the precision in the torque control, and we propose specific improvements to overcome these limitations.
Partially overlapping neural networks for real and imagined hand movements.

Neuroimagery findings have shown similar cerebral networks associated with imagination and execution of a movement. On the other hand, neuropsychological studies of parietal-lesioned patients suggest that these networks may be at least partly distinct. In the present study, normal subjects were asked to either imagine or execute auditory-cued hand movements. Compared with rest, imagination and execution showed overlapping networks, including bilateral premotor and parietal areas, basal ganglia and cerebellum. However, direct comparison between the two experimental conditions showed that specific cortico-subcortical areas were more engaged in mental simulation, including bilateral premotor, prefrontal, supplementary motor and left posterior parietal areas, and the caudate nuclei. These results suggest that a specific neuronal substrate is involved in the processing of hand motor representations.
Hadoop Oriented Smart Cities Architecture

A smart city implies a consistent use of technology for the benefit of the community. As the city develops over time, components and subsystems such as smart grids, smart water management, smart traffic and transportation systems, smart waste management systems, smart security systems, or e-governance are added. These components ingest and generate a multitude of structured, semi-structured or unstructured data that may be processed using a variety of algorithms in batches, micro batches or in real-time. The ICT architecture must be able to handle the increased storage and processing needs. When vertical scaling is no longer a viable solution, Hadoop can offer efficient linear horizontal scaling, solving storage, processing, and data analyses problems in many ways. This enables architects and developers to choose a stack according to their needs and skill-levels. In this paper, we propose a Hadoop-based architectural stack that can provide the ICT backbone for efficiently managing a smart city. On the one hand, Hadoop, together with Spark and the plethora of NoSQL databases and accompanying Apache projects, is a mature ecosystem. This is one of the reasons why it is an attractive option for a Smart City architecture. On the other hand, it is also very dynamic; things can change very quickly, and many new frameworks, products and options continue to emerge as others decline. To construct an optimized, modern architecture, we discuss and compare various products and engines based on a process that takes into consideration how the products perform and scale, as well as the reusability of the code, innovations, features, and support and interest in online communities.
DeepXplore: Automated Whitebox Testing of Deep Learning Systems

Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains including self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner case inputs are of great importance. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs.
 We design, implement, and evaluate DeepXplore, the first whitebox framework for systematically testing real-world DL systems. First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking. Finally, we demonstrate how finding inputs for DL systems that both trigger many differential behaviors and achieve high neuron coverage can be represented as a joint optimization problem and solved efficiently using gradient-based search techniques.
 DeepXplore efficiently finds thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on five popular datasets including ImageNet and Udacity self-driving challenge data. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running only on a commodity laptop. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve the model's accuracy by up to 3%.
Spinal Instability Neoplastic Score (SINS): Reliability Among Spine Fellows and Resident Physicians in Orthopedic Surgery and Neurosurgery

Reliability analysis.



The Spinal Instability Neoplastic Score (SINS) was developed for assessing patients with spinal neoplasia. It identifies patients who may benefit from surgical consultation or intervention. It also acts as a prognostic tool for surgical decision making. Reliability of SINS has been established for spine surgeons, radiologists, and radiation oncologists, but not yet among spine surgery trainees. The purpose of our study is to determine the reliability of SINS among spine residents and fellows, and its role as an educational tool.



Twenty-three residents and 2 spine fellows independently scored 30 de-identified spine tumor cases on 2 occasions, at least 6 weeks apart. Intraclass correlation coefficient (ICC) measured interobserver and intraobserver agreement for total SINS scores. Fleiss's kappa and Cohen's kappa analysis evaluated interobserver and intraobserver agreement of 6 component subscores (location, pain, bone lesion quality, spinal alignment, vertebral body collapse, and posterolateral involvement of spinal elements).



Total SINS scores showed near perfect interobserver (0.990) and intraobserver (0.907) agreement. Fleiss's kappa statistics revealed near perfect agreement for location; substantial for pain; moderate for alignment, vertebral body collapse, and posterolateral involvement; and fair for bone quality (0.948, 0.739, 0.427, 0.550, 0.435, and 0.382). Cohen's kappa statistics revealed near perfect agreement for location and pain, substantial for alignment and vertebral body collapse, and moderate for bone quality and posterolateral involvement (0.954, 0.814, 0.610, 0.671, 0.576, and 0.561, respectively).



The SINS is a reliable and valuable educational tool for spine fellows and residents learning to judge spinal instability.
Modified Wilkinson Power Dividers for Millimeter-Wave Integrated Circuits

A modification of the Wilkinson power divider is presented that eases planar implementation while maintaining performance. By adding transmission lines between the resistor and the quarter-wave transformers of the traditional design, a range of valid solutions exists that meet the conditions of being reciprocal, isolated between the output ports, and matched at all ports. The proposed design is particularly useful at millimeter-wave frequencies where reduced physical dimensions make a circuit configuration suitable for low-cost package-level implementation difficult using traditional methods. Two frequency bands are demonstrated. At V-band, the circuit gives 0.3-dB excess insertion loss, 19-dB isolation, and 50% bandwidth. At the W-band, the circuit gives 0.75-dB excess insertion loss, 24-dB isolation, and 39% bandwidth.
The Impact of Internal Social Media Usage on Organizational Socialization and Commitment

Socializing new employees into the organization and building higher levels of organizational commitment poses a challenge to organizations of different sizes. Social media technologies present an opportunity for organizations to create value by facilitating interactions among employees. The purpose of this study is to investigate an internal social media tool that was designed and implemented as part of a new hire program. An online survey was administered to IT new hires in a military financial services organization. While work related social media use did not prove to have a significant relationship with socialization or commitment, this study suggests that social related social media use is associated with knowledge of organizational culture, social acceptance, and affective commitment. In this study, two types of social media use, four indicators of socialization and three indicators of commitment were analyzed. Our paper concludes with a discussion and the implications concerning internal social media use.
Retrieval Profiling Framework for Thematic Interpretation of Al-Quran

A retrieval profiling is to capturing the metadata of search process and the details of retrieval results. According to the theory of information retrieval (IR) that relevance judgment should be justified by user's needs, the aim of this research is to propose a retrieval profiling framework (RPF) for pooling relevant interpretations of al-Quran based on thematic search. A thematic interpretation of al-Quran is called tafseer al-maudu'iy (urdu language sentence). The domain tafseer al-maudu'iy is sensitive in IR due to its interrelation of Quranic interpretation within its verse(s). On account of this concern, this paper has proposed a RPF to develop a standard procedure for themes/topics of al-Quran in order to keep the interrelation in search. It has undertaken mix methodology with a comparative study on two thematic search engines of al-Quran and two sources of thematic interpretation of al-Quran to accomplish the procedure. With a counting number of themes and experts' justification, it encountered a pilot test to justify the validity of the RPF and found effective. Despite the limitation of data and experts, it is assumed that this RPF can contribute to users in thematic search of al-Quran as well as it can practically guide the IR system developers for generating a good retrieval platform based on thematic search of Quranic interpretation.
How Much Is Enough? Choosing ε for Differential Privacy

Differential privacy is a recent notion, and while it is nice conceptually it has been difficult to apply in practice. The parameters of differential privacy have an intuitive theoretical interpretation, but the implications and impacts on the risk of disclosure in practice have not yet been studied, and choosing appropriate values for them is non-trivial. Although the privacy parameter in differential privacy is used to quantify the privacy risk posed by releasing statistics computed on sensitive data, is not an absolute measure of privacy but rather a relative measure. In effect, even for the same value of , the privacy guarantees enforced by differential privacy are different based on the domain of attribute in question and the query supported. We consider the probability of identifying any particular individual as being in the database, and demonstrate the challenge of setting the proper value of given the goal of protecting individuals in the database with some fixed probability.
Solar cell parameters extraction using particle swarm optimization algorithm

This paper presents an application of particle swarm optimization (PSO) technique for extracting the parameters of single diode solar cell model. The proposed technique is used to estimate five different model parameters; namely, generated photocurrent, saturation current, series resistance, shunt resistance and ideality factor that govern the current-voltage relationship of a solar cell. A measurement data of 57 mm diameter commercial (R.T.C. France) silicon solar cell is used to test and verify the consistency of accurately estimating various parameters. The effectiveness of the proposed method is compared with the results found by the other parameter estimation techniques.
Temporal Pattern Attention for Multivariate Time Series Forecasting

Forecasting of multivariate time series data, for instance the prediction of electricity consumption, solar power production, and polyphonic piano pieces, has numerous valuable applications. However, complex and non-linear interdependencies between time steps and series complicate this task. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved by recurrent neural networks (RNNs) with an attention mechanism. The typical attention mechanism reviews the information at each previous time step and selects relevant information to help generate the outputs; however, it fails to capture temporal patterns across multiple time steps. In this paper, we propose using a set of filters to extract time-invariant temporal patterns, similar to transforming time series data into its “frequency domain”. Then we propose a novel attention mechanism to select relevant time series, and use its frequency domain information for multivariate forecasting. We apply the proposed model on several real-world tasks and achieve state-of-the-art performance in all of these with a single exception.  indicates equal contribution. This work was financially supported by the Ministry of Science and Technology of Taiwan.  Shih National Taiwan University E-mail: shunyaoshih@gmail.com Fan-Keng Sun National Taiwan University E-mail: b03901056@ntu.edu.tw Hung-yi Lee National Taiwan University E-mail: hungyilee@ntu.edu.tw ar X iv :1 80 9. 04 20 6v 2 [ cs .L G ] 2 7 N ov 2 01 8 2 Shun-Yao Shih* et al. Fig. 1 Historical prices of crude oil, gasoline, and lumber. Units are omitted and scales are normalized for simplicity.
Data Mining of Causal Relations from Text: Analysing Maritime Accident Investigation Reports

Text mining is a process of extracting information of interest from text. Such a method includes techniques from various areas such as Information Retrieval (IR), Natural Language Processing (NLP), and Information Extraction (IE). In this study, text mining methods are applied to extract causal relations from maritime accident investigation reports collected from the Marine Accident Investigation Branch (MAIB). These causal relations provide information on various mechanisms behind accidents, including human and organizational factors relating to the accident. The objective of this study is to facilitate the analysis of the maritime accident investigation reports, by means of extracting contributory causes with more feasibility. A careful investigation of contributory causes from the reports provide opportunity to improve safety in future. Two methods have been employed in this study to extract the causal relations. They are 1) Pattern classification method and 2) Connectives method. The earlier one uses naı̈ve Bayes and Support Vector Machines (SVM) as classifiers. The latter simply searches for the words connecting cause and effect in sentences. The causal patterns extracted using these two methods are compared to the manual (human expert) extraction. The pattern classification method showed a fair and sensible performance with F-measure(average) = 65% when compared to connectives method with F-measure(average) = 58%. This study is an evidence, that text mining methods could be employed in extracting causal relations from marine accident investigation reports.
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

We revisit the problem of visual depth estimation in the context of autonomous vehicles. Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large-a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving. We argue that the challenges of removing this gap are significant, owing to fundamental limitations of monocular vision. As a result, we focus our efforts on depth estimation by stereo. We propose a novel semi-supervised learning approach to training a deep stereo neural network, along with a novel architecture containing a machine-learned argmax layer and a custom runtime that enables a smaller version of our stereo DNN to run on an embedded GPU. Competitive results are shown on the KITTI 2015 stereo dataset. We also evaluate the recent progress of stereo algorithms by measuring the impact upon accuracy of various design
An Optimum Ultra-Wideband (UWB) Bandpass Filter with Spurious Response Suppression

A high selectivity ultra-wideband (UWB) bandpass filter (BPF) with a fractional bandwidth (FBW) of about 110% is presented using a microstrip structure. The filter is based on a circuit model for an optimum distributed highpass filter consisting of only 5 short-circuited stubs separated by non-redundant connecting lines. As a result, the filter in a compact structure exhibited a selective filtering characteristic equivalent to a 9-pole Chebyshev filter. In addition, an optimum bandstop filter was added in order to suppress the second spurious harmonic. The design was successfully realized in theory and verified by full-wave EM simulation of the full layout and the experiment. The proposed filter had low insertion loss, sharp rejection, and a group delay of about 0.2 ns over the most center band. The filter also demonstrated an UWB reject band from 11.4 GHz to more than 20 GHz at -30 dB
Hard Mixtures of Experts for Large Scale Weakly Supervised Vision

Training convolutional networks (CNNs) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large networks that do not fit in the memory of a few GPU cards, or for parallelizing CNN training. In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks. Mixture of experts models are not new [7, 3], but in the past, researchers have had to devise sophisticated methods to deal with data fragmentation. We show empirically that modern weakly supervised data sets are large enough to support naive partitioning schemes where each data point is assigned to a single expert. Because the experts are independent, training them in parallel is easy, and evaluation is cheap for the size of the model. Furthermore, we show that we can use a single decoding layer for all the experts, allowing a unified feature embedding space. We demonstrate that it is feasible (and in fact relatively painless) to train far larger models than could be practically trained with standard CNN architectures, and that the extra capacity can be well used on current datasets.