nils-herrmann/clean_scirepeval huggingface dataset:
NLP for answer extraction in technical domains

In this paper we argue that questionanswering (QA) over technical domains is distinctly different from TREC-based QA or Web-based QA and it cannot benefit from data-intensive approaches. Technical questions arise in situations where concrete problems require specific answers and explanations. Finding a justification of the answer in the context of the document is essential if we have to solve a real-world problem. We show that NLP techniques can be used successfully in technical domains for high-precision access to information stored in documents. We present ExtrAns, an answer extraction system over technical domains, its architecture, its use of logical forms for answer extractions and how terminology extraction becomes an important part of the system.
Accelerating Braided B+ Tree Searches on a GPU with CUDA

Previous work has shown that using the GPU as a brute force method for SELECT statements on a SQLite database table yields significant speedups. However, this requires that the entire table be selected and transformed from the B-Tree to row-column format. This paper investigates possible speedups by traversing B+ Trees in parallel on the GPU, avoiding the overhead of selecting the entire table to transform it into row-column format and leveraging the logarithmic nature of tree searches. We experiment with different input sizes, different orders of the B+ Tree, and batch multiple queries together to find optimal speedups for SELECT statements with single search parameters as well as range searches. We additionally make a comparison to a simple GPU brute force algorithm on a row-column version of the B+ Tree.
Improving Agility and Elasticity in Bare-metal Clouds

Bare-metal clouds are an emerging infrastructure-as-a-service (IaaS) that leases physical machines (bare-metal instances) rather than virtual machines, allowing resource-intensive applications to have exclusive access to physical hardware. Unfortunately, bare-metal instances require time-consuming or OS-specific tasks for deployment due to the lack of virtualization layers, thereby sacrificing several beneficial features of traditional IaaS clouds such as agility, elasticity, and OS transparency. We present BMcast, an OS deployment system with a special-purpose de-virtualizable virtual machine monitor (VMM) that supports quick and OS-transparent startup of bare-metal instances. BMcast performs streaming OS deployment while allowing direct access to physical hardware from the guest OS, and then disappears after completing the deployment. Quick startup of instances improves agility and elasticity significantly, and OS transparency greatly simplifies management tasks for cloud customers. Experimental results have confirmed that BMcast initiated a bare-metal instance 8.6 times faster than image copying, and database performance on BMcast during streaming OS deployment was comparable to that on a state-of-the-art VMM without performing deployment. BMcast incurred zero overhead after de-virtualization.
Smile analysis and design in the digital era.

key elements of orthodontic diagnosis and treatment planning over the last decade.1-3 Recent advances in technology now permit the clinician to measure dynamic lip-tooth relationships and incorporate that information into the orthodontic problem list and biomechanical plan. Digital videography is particularly useful in both smile analysis and in doctor/patient communication. Smile design is a multifactorial process, with clinical success determined by an understanding of the patient’s soft-tissue treatment limitations and the extent to which orthodontics or multidisciplinary treatment can satisfy the patient’s and orthodontist’s esthetic goals.
Stereo odometry based on careful feature selection and tracking

In this paper we present a novel algorithm for fast and robust stereo visual odometry based on feature selection and tracking (SOFT). The reduction of drift is based on careful selection of a subset of stable features and their tracking through the frames. Rotation and translation between two consecutive poses are estimated separately. The five point method is used for rotation estimation, whereas the three point method is used for estimating translation. Experimental results show that the proposed algorithm has an average pose error of 1.03% with processing speed above 10 Hz. According to publicly available KITTI leaderboard, SOFT outperforms all other validated methods. We also present a modified IMU-aided version of the algorithm, fast and suitable for embedded systems. This algorithm employs an IMU for outlier rejection and Kalman filter for rotation refinement. Experiments show that the IMU based system runs at 20 Hz on an ODROID U3 ARM-based embedded computer without any hardware acceleration. Integration of all components is described and experimental results are presented.
Reference Hijacking: Patching, Protecting and Analyzing on Unmodified and Non-rooted Android Devices

Many efforts have been paid to enhance the security of Android. However, less attention has been given to how to practically adopt the enhancements on off-the-shelf devices. In particular, securing Android devices often requires modifying their write-protected underlying system component files (especially the system libraries) by flashing or rooting devices, which is unacceptable in many realistic cases. In this paper, a novel technique, called reference hijacking, is presented to address the problem. By introducing a specially designed reset procedure, a new execution environment is constructed for the target application, in which the reference to the underlying system libraries will be redirected to the security-enhanced alternatives. The technique can be applicable to both the Dalvik and Android Runtime (ART) environments and to almost all mainstream Android versions (2.x to 5.x). To demonstrate the capability of reference hijacking, we develop three prototype systems, PatchMan, ControlMan, and TaintMan, to enforce specific security enhancements, involving patching vulnerabilities, protecting inter-component communications, and performing dynamic taint analysis for the target application. These three prototypes have been successfully deployed on a number of popular Android devices from different manufacturers, without modifying the underlying system. The evaluation results show that they are effective and do not introduce noticeable overhead. They strongly support that reference hijacking can substantially improve the practicability of many security enhancement efforts for Android.
Variational Bayesian Inference with Stochastic Search

Mean-field variational inference is a method for approximate Bayesian posterior inference. It approximates a full posterior distribution with a factorized set of distributions by maximizing a lower bound on the marginal likelihood. This requires the ability to integrate a sum of terms in the log joint likelihood using this factorized distribution. Often not all integrals are in closed form, which is typically handled by using a lower bound. We present an alternative algorithm based on stochastic optimization that allows for direct optimization of the variational lower bound. This method uses control variates to reduce the variance of the stochastic search gradient, in which existing lower bounds can play an important role. We demonstrate the approach on two non-conjugate models: logistic regression and an approximation to the HDP.
Learning To Route with Deep RL

We investigate a novel and important application domain for deep RL: network routing. The question of whether/when traditional network protocol design, which relies on the application of algorithmic insights by human experts, can be replaced by a data-driven approach has received much attention recently. We explore this question in the context of the, arguably, most fundamental networking task: routing. Can ideas and techniques from machine learning be leveraged to automatically generate “good” routing configurations? We observe that the routing domain poses significant challenges for data-driven network protocol design and report on preliminary results regarding the power of data-driven routing. Our results suggest that applying deep reinforcement learning to this context yields high performance and is thus a promising direction for further research. We outline a research agenda for data-driven routing.
Improving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings

It has been shown that learning distributed word representations is highly useful for Twitter sentiment classification. Most existing models rely on a single distributed representation for each word. This is problematic for sentiment classification because words are often polysemous and each word can contain different sentiment polarities under different topics. We address this issue by learning topic-enriched multi-prototype word embeddings (TMWE). In particular, we develop two neural networks which 1) learn word embeddings that better capture tweet context by incorporating topic information, and 2) learn topic-enriched multiple prototype embeddings for each word. Experiments on Twitter sentiment benchmark datasets in SemEval 2013 show that TMWE outperforms the top system with hand-crafted features, and the current best neural network model.
Detecting objective-C malware through memory forensics

Memory forensics is increasingly used to detect and analyze sophisticated malware. In the last decade, major advances in memory forensics have made analysis of kernel-level mal-ware straightforward. Kernel-level malware has been favored by attackers because it essentially provides complete control over a machine. This has changed recently as operating systems vendors now routinely enforce driving signing and strategies for protecting kernel data, such as Patch Guard, have made userland attacks much more attractive to malware authors. In this thesis, new techniques for detecting userland malware written in Objective-C on Mac OS X are presented. As the thesis illustrates, C provides a rich set of APIs that malware uses to manipulate and steal data and to perform other malicious activities. The novel memory forensics techniques presented in this thesis deeply examine the state of the Objective-C runtime, identifying a number of suspicious activities, from keystroke logging to pointer swizzling.
Motion capture with constrained inverse kinematics for real-time hand tracking

Articulated hand tracking systems have been commonly used in virtual reality applications, including systems with human-computer interaction or interaction with game consoles. However, building an effective real-time hand pose tracker remains challenging. In this paper, we present a simple and efficient methodology for tracking and reconstructing 3d hand poses using a markered optical motion capture system. Markers were positioned at strategic points, and an inverse kinematics solver was incorporated to fit the rest of the joints to the hand model. The model is highly constrained with rotational and orientational constraints, allowing motion only within a feasible set. The method is real-time implementable and the results are promising, even with a low frame rate.
Ruptured Corpus Luteal Cyst: CT Findings

To evaluate the CT findings of ruptured corpus luteal cysts.



Six patients with a surgically proven ruptured corpus luteal cyst were included in this series. The prospective CT findings were retrospectively analyzed in terms of the size and shape of the cyst, the thickness and enhancement pattern of its wall, the attenuation of its contents, and peritoneal fluid.



The mean diameter of the cysts was 2.8 (range, 1.5-4.8) cm; three were round and three were oval. The mean thickness of the cyst wall was 4.7 (range, 1-10) mm; in all six cases it showed strong enhancement, and in three was discontinuous. In five of six cases, the cystic contents showed high attenuation. Peritoneal fluid was present in all cases, and its attenuation was higher, especially around the uterus and adnexa, than that of urine present in the bladder.



In a woman in whom CT reveals the presence of an ovarian cyst with an enhancing rim and highly attenuated contents, as well as highly attenuated peritoneal fluid, a ruptured corpus luteal cyst should be suspected. Other possible evidence of this is focal interruption of the cyst wall and the presence of peritoneal fluid around the adnexa.
Design of Novel Wideband Circularly Polarized Antenna Based on Vivaldi Antenna Structure

This letter presents a novel compact wideband circularly polarized antenna by utilizing Vivaldi antenna structure as the design concept. The proposed antenna consists of four exponentially tapered patches, a circular metal wall and a feed network. Four tapered patches are sequentially rotated and vertically located in the circular metal wall which acts as a vertical ground plane. Slot-coupled feed technique is applied to feed the antenna. Feeding by a corporate feed network, wide impedance bandwidth and axial ratio bandwidth are achieved. Parametric studies are made to optimize the antenna. The experimental results show that the antenna exhibits a 3-dB AR bandwidth of 41.4% (3.45-5.25 GHz) and a 10-dB impedance bandwidth of 57.3% (3.06-5.52 GHz).
Development of Academic Attendence Monitoring System Using Fingerprint Identification

In this paper we propose a system that automates the whole process of taking attendance and maintaining its records in an academic institute. Managing people is a difficult task for most of the organizations, and maintaining the attendance record is an important factor in people management. When considering academic institutes, taking the attendance of students on daily basis and maintaining the records is a major task. Manually taking the attendance and maintaining it for a long time adds to the difficulty of this task as well as wastes a lot of time. For this reason an efficient system is designed. This system takes attendance electronically with the help of a fingerprint sensor and all the records are saved on a computer server. Fingerprint sensors and LCD screens are placed at the entrance of each room. In order to mark the attendance, student has to place his/her finger on the fingerprint sensor. On identification student’s attendance record is updated in the database and he/she is notified through LCD screen. No need of all the stationary material and special personal for keeping the records. Furthermore an automated system replaces the manual system.
A course on geometric group theory

These notes are based on a series of lectures I gave at the Tokyo Institute of Technology from April to July 2005. They constituted a course entitled “An introduction to geometric group theory” totalling about 20 hours. The audience consisted of fourth year students, graduate students as well as several staff members. I therefore tried to present a logically coherent introduction to the subject, tailored to the background of the students, as well as including a number of diversions into more sophisticated applications of these ideas. There are many statements left as exercises. I believe that those essential to the logical developments will be fairly routine. Those related to examples or diversions may be more challenging. The notes assume a basic knowledge of group theory, and metric and topological spaces. We describe some of the fundamental notions of geometric group theory, such as quasi-isometries, and aim for a basic overview of hyperbolic groups. We describe group presentations from first principles. We give an outline description of fundamental groups and covering spaces, sufficient to allow us to illustrate various results with more explicit examples. We also give a crash course on hyperbolic geometry. Again the presentation is rather informal, and aimed at providing a source of examples of hyperbolic groups. This is not logically essential to most of what follows. In principle, the basic theory of hyperbolic groups can be developed with no reference to hyperbolic geometry, but interesting examples would be rather sparse. In order not to interupt the exposition, I have not given references in the main text. We give sources and background material as notes in the final section. I am very grateful for the generous support offered by the Tokyo Insititute of Technology, which allowed me to complete these notes, as well as giving me the freedom to pursue my own research interests. I am indebted to Sadayoshi Kojima for his invitation to spend six months there, and for many interesting conversations. I thank Toshiko Higashi for her constant help in making my stay a very comfortable and enjoyable one. My then PhD student Ken Shackleton accompanied me on my visit, and provided some tutorial assistance. Shigeru Mizushima and Hiroshi Ooyama helped with some matters of translatation etc.
Effective Comparison and Evaluation of DES and Rijndael Algorithm ( AES )

This paper discusses the effective coding of Rijndael algorithm, Advanced Encryption Standard (AES) in Hardware Description Language, Verilog. In this work we analyze the structure and design of new AES, following three criteria: a) resistance against all known attacks; b) speed and code compactness on a wide range of platforms; and c) design simplicity; as well as its similarities and dissimilarities with other symmetric ciphers. On the other side, the principal advantages of new AES with respect to DES, as well as its limitations, are investigated. Thus, for example, the fact that the new cipher and its inverse use different components, which practically eliminates the possibility for weak and semi-weak keys, as existing for DES, and the non-linearity of the key expansion, which practically eliminates the possibility of equivalent keys, are two of the principal advantages of new cipher. Finally, the implementation aspects of Rijndael cipher and its inverse are treated. Thus, although Rijndael is well suited to be implemented efficiently on a wide range of processors and in dedicated hardware, we have concentrated our study on 8-bit processors, typical for current Smart Cards and on 32-bit processors, typical for PCs.
Automatic lane-level map generation for advanced driver assistance systems using low-cost sensors

Lane-level digital maps can simplify driving tasks for robotic cars as well as enhance performance and reliability for advanced driver assistance systems (ADAS) by providing strong priors about the driving environment. In this paper, we present a system for automatic generation of precise lane-level maps by using conventional low-cost sensors installed in most of current commercial cars. It mainly consists of two modules, i.e. road orthographic image generation and lane graph construction. First, we divide the global map into fixed local segments based on the road network topology. According to the local map segments, we accumulate the bird's eye view images of the road surface by fusing GPS, INS and visual odometry, and subsequently integrate them into synthetic orthographic images with the reference of the local map segments. Furthermore, the information of the driving lanes is extracted from the orthographic images and a large amount of vehicle trajectories, which is used to construct the lane graph of the map based on the lane models we proposed. Such a system can offer increased value as well as promote the automation level for today's commercial cars without being supplemented additional sensors. Experiments show promising results of the automatic map generation of the real-world roads, which substantiated the effectiveness of the proposed approach.
The brainweb: Phase synchronization and large-scale integration

The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.
Identification of power distribution network topology via voltage correlation analysis

We consider the problem of reconstructing the topology of a portion of the power distribution network, given a dataset of voltage measurements. By using an approximate model for the grid voltage magnitudes, we show that these signals exhibit some specific correlation properties, that can be described via a sparse Markov random field. By specializing the tools available for the identification of graphical models, we propose an algorithm for the reconstruction of the grid topology. Via simulations, we show how the algorithm performs well also when an exact nonlinear model of the grid voltages is adopted, when realistic power demand profiles are considered, and when the voltage measurements are affected by measurement noise.
Accelerating 5G QoE via public-private spectrum sharing

Fifth generation wireless systems (5G) must achieve high user Quality of Experience (QoE) in order to compete for market share. Each candidate 5G wireless radio frequency (RF) band offers advantages such as longer range or higher data rate than 2G, 3G, and 4G, but no single band or air interface standard by itself fully achieves ubiquitous levels of QoE for the complete range of wireless access devices. Spectrum clearing cannot keep pace with user demand, so public-private spectrum sharing is emerging as an affordable, near-term method of increasing radio access network (RAN) capacities for content delivery. This paper presents a fresh look at QoE; spectrum scarcity; public uses that underutilize candidate 5G radio frequency (RF) spectrum; and emerging public-private radio interference management frameworks that enable near-term spectrum sharing, with positive consequences for 5G price, performance, and total user QoE.
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning ∗

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of meta-algorithms that can take advantage of any machine learning or regression method to estimate the conditional average treatment effect (CATE) function. Meta-algorithms build on base algorithms—such as OLS, the Nadaraya-Watson estimator, Random Forests (RF), Bayesian Average Regression Trees (BART) or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a new meta-algorithm, the X–learner, that is provably efficient when the number of units in one treatment group is much larger than another, and it can exploit structural properties of the CATE function. For example, if the CATE function is parametrically linear and the response functions in treatment and control are Lipschitz continuous, the X–learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X–learner that use RF and BART as base learners. In our extensive simulation studies, the X–learner performs favorably, although none of the meta-learners is uniformly the best. We also analyze two real data applications, and provide a software package that implements our methods.  Causal Inference | Random Forests | Minimax Optimality