nils-herrmann/clean_scirepeval huggingface dataset:
ON DATA BANKS AND PRIVACY HOMOMORPHISMS

Encryption is a well—known technique for preserving the privacy of sensitive information. One of the basic, apparently inherent, limitations of this technique is that an information system working with encrypted data can at most store or retrieve the data for the user; any more complicated operations seem to require that the data be decrypted before being operated on. This limitation follows from the choice of encryption functions used, however, and although there are some truly inherent limitations on what can be accomplished, we shall see that it appears likely that there exist encryption functions which permit encrypted data to be operated on without preliminary decryption of the operands, for many sets of interesting operations. These special encryption functions we call “privacy homomorphisms”; they form an interesting subset of arbitrary encryption schemes (called “privacy transformations”).
Discrete Dynamic Shortest Path Problems in Transportation Applications: Complexity and Algorithms with Optimal Run Time

This paper solves what appears to be a 30 years old problem dealing with the discovery of most effi algorithms possible to compute all-to-one shortest paths in discrete dynamic networks. This problem lies at th of efficient solution approaches to dynamic network models that arise in dynamic transportation systems, suc Intelligent Transportation Systems (ITS) applications. While the main objective of this paper is the study of the one dynamic shortest paths problem, one-to-all fastest paths problems are studied as well. Early results are and new properties are established. We establish the exact complexity of these problems and develop optima run time sense, solution algorithms. A new and simple solution algorithm is proposed for all-to-one all departur intervals shortest path problems. It is proved, theoretically, that the new solution algorithm has an optimal run complexity that equals the complexity of the problem. Computer implementations and experimental evaluatio various solution algorithms support the theoretical findings and demonstrate the efficiency of the proposed so algorithm. We expect our findings to be of major benefit to research and development activities in the field of dynamic, in particular real-time, management and control of large-scale Intelligent Transportation Systems (I
Understanding Latency Variation in Modern DRAM Chips: Experimental Characterization, Analysis, and Optimization

Long DRAM latency is a critical performance bottleneck in current systems. DRAM access latency is defined by three fundamental operations that take place within the DRAM cell array: (i) activation of a memory row, which opens the row to perform accesses; (ii) precharge, which prepares the cell array for the next memory access; and (iii) restoration of the row, which restores the values of cells in the row that were destroyed due to activation. There is significant latency variation for each of these operations across the cells of a single DRAM chip due to irregularity in the manufacturing process. As a result, some cells are inherently faster to access, while others are inherently slower. Unfortunately, existing systems do not exploit this variation. 
 The goal of this work is to (i) experimentally characterize and understand the latency variation across cells within a DRAM chip for these three fundamental DRAM operations, and (ii) develop new mechanisms that exploit our understanding of the latency variation to reliably improve performance. To this end, we comprehensively characterize 240 DRAM chips from three major vendors, and make several new observations about latency variation within DRAM. We find that (i) there is large latency variation across the cells for each of the three operations; (ii) variation characteristics exhibit significant spatial locality: slower cells are clustered in certain regions of a DRAM chip; and (iii) the three fundamental operations exhibit different reliability characteristics when the latency of each operation is reduced. 
 Based on our observations, we propose Flexible-LatencY DRAM (FLY-DRAM), a mechanism that exploits latency variation across DRAM cells within a DRAM chip to improve system performance. The key idea of FLY-DRAM is to exploit the spatial locality of slower cells within DRAM, and access the faster DRAM regions with reduced latencies for the fundamental operations. Our evaluations show that FLY-DRAM improves the performance of a wide range of applications by 13.3%, 17.6%, and 19.5%, on average, for each of the three different vendors' real DRAM chips, in a simulated 8-core system. We conclude that the experimental characterization and analysis of latency variation within modern DRAM, provided by this work, can lead to new techniques that improve DRAM and system performance.
BiGS: BioTac Grasp Stability Dataset

Autonomous grasping of unknown objects is a fundamental requirement for robots performing manipulation tasks in real world environments. Even though there has been a lot of progress in the area of grasping, it is still considered an open challenge and even the state-of-the-art grasping methods may result in failures . A reliable prediction of grasp stability helps to avoid such failures and provides an option to re-grasp the object safely. Since the majority of grasping failures happen at the contact points, which are occluded for vision systems, tactile feedback plays a major role for predicting grasp stability. The human-inspired biomimmetic tactile sensor (BioTac)  is equipped with a 19-electrode array and a hydroacoustic sensor surrounded by silicon skin inflated with incompressible and conductive liquid. This design provides rich tactile feedback similar to the slowly-adapting and fastadapting afferents present in the human skin . Latest developments in classification algorithms  allow us to explore the potential of large amounts of data from these sensors. Our goal is to provide a publicly accessible grasp-stability dataset collected using the BioTacs and, thus, enable further development of algorithms capable of reliable grasp stability prediction.
RCS reduction of Antipodal Vivaldi Antenna

A novel Antipodal Vivaldi Antenna with low radar cross section (RCS) is proposed in this paper. By using flat corrugated slotline to replace exponential gradient curve on both sides of the antenna, the RCS in the endfire direction can be reduced in the operating band of 4.3 GHz-12 GHz when the incident wave is perpendicular to the antenna plane. Mean while, the lowest operating frequency of the antenna is reduced from 4.3 GHz to 3.8 GHz, and radiation performance keeps stably.
Generating Descriptions with Grounded and Co-referenced People

Learning how to generate descriptions of images or videos received major interest both in the Computer Vision and Natural Language Processing communities. While a few works have proposed to learn a grounding during the generation process in an unsupervised way (via an attention mechanism), it remains unclear how good the quality of the grounding is and whether it benefits the description quality. In this work we propose a movie description model which learns to generate description and jointly ground (localize) the mentioned characters as well as do visual co-reference resolution between pairs of consecutive sentences/clips. We also propose to use weak localization supervision through character mentions provided in movie descriptions to learn the character grounding. At training time, we first learn how to localize characters by relating their visual appearance to mentions in the descriptions via a semi-supervised approach. We then provide this (noisy) supervision into our description model which greatly improves its performance. Our proposed description model improves over prior work w.r.t. generated description quality and additionally provides grounding and local co-reference resolution. We evaluate it on the MPII Movie Description dataset using automatic and human evaluation measures and using our newly collected grounding and co-reference data for characters.
Digital Calibration Techniques for Pipelined ADC ’ s

A skip and fill algorithm is developed to digitally self-calibrate pipelined analog-to-digital converters (ADC’s) in real time. The proposed digital calibration technique is applicable to capacitor-ratioed multiplying digital-to-analog converters (MDAC’s) commonly used in multi-step or pipelined ADC’s. This background calibration process can replace, in effect, a trimming procedure usually done in the factory with a hidden electronic calibration. Unlike other self-calibration techniques working in the foreground, the proposed technique is based on the concept of skipping conversion cycles randomly but filling in data later by nonlinear interpolation. This opens up the feasibility of digitally implementing calibration hardware and simplifying the task of self-calibrating multi-step or pipelined ADC’s. The proposed method improves the performance of the inherently fast ADC’s by maintaining simple system architectures. To measure errors resulting from capacitor mismatch, op amp dc gain, offset, and switch feedthrough in real time, the calibration test signal is injected in place of the input signal using a split-reference injection technique. Ultimately, the missing signal within 2/3 of the Nyquist bandwidth is recovered with 16-bit accuracy using a 44-th order polynomial interpolation, behaving essentially as an FIR filter. This work was supported by the National Science Foundation under Grant MIP-9312671.
A clinical study of Noonan syndrome.

Clinical details are presented on 151 individuals with Noonan syndrome (83 males and 68 females, mean age 12.6 years). Polyhydramnios complicated 33% of affected pregnancies. The commonest cardiac lesions were pulmonary stenosis (62%), and hypertrophic cardiomyopathy (20%), with a normal echocardiogram present in only 12.5% of all cases. Significant feeding difficulties during infancy were present in 76% of the group. Although the children were short (50% with a height less than 3rd centile), and underweight (43% with a weight less than 3rd centile), the mean head circumference of the group was on the 50th centile. Motor milestone delay was usual, the cohort having a mean age of sitting unsupported of 10 months and walking of 21 months. Abnormal vision (94%) and hearing (40%) were frequent findings, but 89% of the group were attending normal primary or secondary schools. Other associations included undescended testicles (77%), hepatosplenomegaly (50%), and evidence of abnormal bleeding (56%). The mean age at diagnosis of Noonan syndrome in this group was 9.0 years. Earlier diagnosis of this common condition would aid both clinical management and genetic counselling.
Data transmission via GSM voice channel for end to end security

Global System for Mobile Communications (GSM) technology still plays a key role because of its availability, reliability and robustness. Recently, additional consumer applications are proposed in which GSM is used as a backup or data transmission service. Unfortunately sending data via GSM channel is a challenging task since it is speech sensitive and suppresses other forms of signals. In this paper, a systematic method is proposed to develop a modem that transmits data over GSM voice channel (DoGSMV) using speech like (SL) symbols. Unlike the previous approaches an artificial search space is produced to find best SL symbols and analyses by synthesis (AbyS) method is introduced for parameter decoding. As a result 1.6 kbps simulation data rate is achieved when wireless communication errors are ignored.
Characterization of evoked tactile sensation in forearm amputees with transcutaneous electrical nerve stimulation.

The goal of this study is to characterize the phenomenon of evoked tactile sensation (ETS) on the stump skin of forearm amputees using transcutaneous electrical nerve stimulation (TENS).



We identified the projected finger map (PFM) of ETS on the stump skin in 11 forearm amputees, and compared perceptual attributes of the ETS in nine forearm amputees and eight able-bodied subjects using TENS. The profile of perceptual thresholds at the most sensitive points (MSPs) in each finger-projected area was obtained by modulating current amplitude, pulse width, and frequency of the biphasic, rectangular current stimulus. The long-term stability of the PFM and the perceptual threshold of the ETS were monitored in five forearm amputees for a period of 11 months.



Five finger-specific projection areas can be independently identified on the stump skin of forearm amputees with a relatively long residual stump length. The shape of the PFM was progressively similar to that of the hand with more distal amputation. Similar sensory modalities of touch, pressure, buzz, vibration, and numb below pain sensation could be evoked both in the PFM of the stump skin of amputees and in the normal skin of able-bodied subjects. Sensory thresholds in the normal skin of able-bodied subjects were generally lower than those in the stump skin of forearm amputees, however, both were linearly modulated by current amplitude and pulse width. The variation of the MSPs in the PFM was confined to a small elliptical area with 95% confidence. The perceptual thresholds of thumb-projected areas were found to vary less than 0.99 × 10(-2) mA cm(-2).



The stable PFM and sensory thresholds of ETS are desirable for a non-invasive neural interface that can feed back finger-specific tactile information from the prosthetic hand to forearm amputees.
Engineering design review of stance-control knee-ankle-foot orthoses.

Persons with quadriceps muscle weakness are often prescribed a knee-ankle-foot orthosis that locks the knee in full extension during both stance and swing phases of gait. Locking the knee results in abnormal gait patterns characterized by hip hiking and leg circumduction during swing. The stance-control knee-ankle-foot orthosis (SCKAFO), a new type of orthosis, has emerged that permits free knee motion during swing while resisting knee flexion during stance, thereby supporting the limb during weight-bearing. This article examines various SCKAFO designs, discuss the existing design limitations, and identify remaining design challenges. Several commercial SCKAFOs have been released that incorporate different locking mechanisms. Preliminary gait studies have shown some devices to be promising; however, an important functional limitation in some SCKAFOs is dependence on specific joint angles to switch between stance and swing modes. Important design factors such as size, weight, and noise must be considered in new orthosis designs to ensure wide consumer acceptance.
Mortality and cardiovascular events in online haemodiafiltration (OL-HDF) compared with high-flux dialysis: results from the Turkish OL-HDF Study.

Online haemodiafiltration (OL-HDF) is considered to confer clinical benefits over haemodialysis (HD) in terms of solute removal in patients undergoing maintenance HD. The aim of this study was to compare postdilution OL-HDF and high-flux HD in terms of morbidity and mortality.



In this prospective, randomized, controlled trial, we enrolled 782 patients undergoing thrice-weekly HD and randomly assigned them in a 1:1 ratio to either postdilution OL-HDF or high-flux HD. The mean age of patients was 56.5 ± 13.9 years, time on HD 57.9 ± 44.6 months with a diabetes incidence of 34.7%. The follow-up period was 2 years, with the mean follow-up of 22.7 ± 10.9 months. The primary outcome was a composite of death from any cause and nonfatal cardiovascular events. The major secondary outcomes were cardiovascular and overall mortality, intradialytic complications, hospitalization rate, changes in several laboratory parameters and medications used.



The filtration volume in OL-HDF was 17.2 ± 1.3 L. Primary outcome was not different between the groups (event-free survival of 77.6% in OL-HDF versus 74.8% in the high-flux group, P = 0.28), as well as cardiovascular and overall survival, hospitalization rate and number of hypotensive episodes. In a post hoc analysis, the subgroup of OL-HDF patients treated with a median substitution volume >17.4 L per session (high-efficiency OL-HDF, n = 195) had better cardiovascular (P = 0.002) and overall survival (P = 0.03) compared with the high-flux HD group. In adjusted Cox-regression analysis, treatment with high-efficiency OL-HDF was associated with a 46% risk reduction for overall mortality {RR = 0.54 [95% confidence interval (95% CI) 0.31-0.93], P = 0.02} and a 71% risk reduction for cardiovascular mortality [RR = 0.29 (95% CI 0.12-0.65), P = 0.003] compared with high-flux HD.



The composite of all-cause mortality and nonfatal cardiovascular event rate was not different in the OL-HDF and in the high-flux HD groups. In a post hoc analysis, OL-HDF treatment with substitution volumes over 17.4 L was associated with better cardiovascular and overall survival.
Dynamic scheduling of network updates (Extended version)

We present Dionysus, a system for fast, consistent network updates in software-defined networks. Dionysus encodes as a graph the consistency-related dependencies among updates at individual switches, and it then dynamically schedules these updates based on runtime differences in the update speeds of different switches. This dynamic scheduling is the key to its speed; prior update methods are slow because they pre-determine a schedule, which does not adapt to runtime conditions. Testbed experiments and data-driven simulations show that Dionysus improves the median update speed by 53–88% in both wide area and data center networks compared to prior methods.
Show Me Your Evidence - an Automatic Method for Context Dependent Evidence Detection

Engaging in a debate with oneself or others to take decisions is an integral part of our day-today life. A debate on a topic (say, use of performance enhancing drugs) typically proceeds by one party making an assertion/claim (say, PEDs are bad for health) and then providing an evidence to support the claim (say, a 2006 study shows that PEDs have psychiatric side effects). In this work, we propose the task of automatically detecting such evidences from unstructured text that support a given claim. This task has many practical applications in decision support and persuasion enhancement in a wide range of domains. We first introduce an extensive benchmark data set tailored for this task, which allows training statistical models and assessing their performance. Then, we suggest a system architecture based on supervised learning to address the evidence detection task. Finally, promising experimental results are reported.
Variational mesh segmentation via quadric surface fitting

Wepresent a new variationalmethod formesh segmentation by fitting quadric surfaces. Each component of the resulting segmentation is represented by a general quadric surface (including plane as a special case). A novel energy function is defined to evaluate the quality of the segmentation, which combines both L2 and L2,1 metrics from a triangle to a quadric surface. The Lloyd iteration is used to minimize the energy function, which repeatedly interleaves between mesh partition and quadric surface fitting. We also integrate feature-based and simplification-based techniques in the segmentation framework, which greatly improve the performance. The advantages of our algorithm are demonstrated by comparing with the state-of-the-art methods.
Plug-in hybrid electric vehicles and smart grid investigations based on a microsimulation

Introduction of Plug-in Hybrid Electric Vehicles (PHEVs) c ould potentially trigger a stepwise electrification of the whole transportation sector. But the impact on the electric grid by electrical vehicl e harging is still not fully known. This paper investigates several PHEV charg ing schemes, including smart charging, using a novel iterative approach . An agent based traffic demand model is used for modeling the electrical dema nd of PHEVs over the day. For modeling the different parts of the electri c g d, an approach based on interconnected multiple energy carrier sys tems is used. For a given charging scheme the power system simulation give s back a price signal indicating whether grid constraints, such as m xi um power output at hub transformators, have been violated. This lead s to a corrective step in the iterative process, until a charging pattern is found, which does not violate grid constraints. The proposed system allo ws t investigate existing electric grids, whether they are capable of me eting increased electricity demand by certain future PHEV penetration. Fur thermore, in the future, different types of smart charging schemes can be add d into the system for comparison.
To buy or not to buy online: adopters and non-adopters of online shopping in Singapore

The Internet, as a dynamic virtual medium for selling and buying information, services and products, is gaining increasing attention from researchers and practitioners. In this study, we examine the perceptions of adopters and non-adopters of online shopping in terms of demographic profile, consumer expectations of online stores, advantages and problems of online shopping and transaction cost. In addition, we also examine the types of products purchased, frequency of online purchase and the extent of communication with e-commerce vendors. The findings are useful in explaining consumers’ buying behaviour in the electronic marketplace. Implications of the results are discussed.
Quantum Nearest-Neighbor Algorithms for Machine Learning

We present several quantum algorithms for performing nearest-neighbor learning. At the core of our algorithms are fast and coherent quantum methods for computing distance metrics such as the inner product and Euclidean distance. We prove upper bounds on the number of queries to the input data required to compute these metrics. In the worst case, our quantum algorithms lead to polynomial reductions in query complexity relative to the corresponding classical algorithm. In certain cases, we show exponential or even super-exponential reductions over the classical analog. We study the performance of our quantum nearest-neighbor algorithms on several real-world binary classification tasks and find that the classification accuracy is competitive with classical methods.
Time is of the essence: improving recency ranking using Twitter data

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.