nils-herrmann/clean_scirepeval huggingface dataset:
Charm: Facilitating Dynamic Analysis of Device Drivers of Mobile Systems

Mobile systems, such as smartphones and tablets, incorporate a diverse set of I/O devices, such as camera, audio devices, GPU, and sensors. This in turn results in a large number of diverse and customized device drivers running in the operating system kernel of mobile systems. These device drivers contain various bugs and vulnerabilities, making them a top target for kernel exploits [78]. Unfortunately, security analysts face important challenges in analyzing these device drivers in order to find, understand, and patch vulnerabilities. More specifically, using the state-of-the-art dynamic analysis techniques such as interactive debugging, fuzzing, and record-and-replay for analysis of these drivers is difficult, inefficient, or even completely inaccessible depending on the analysis. In this paper, we present Charm1, a system solution that facilitates dynamic analysis of device drivers of mobile systems. Charm’s key technique is remote device driver execution, which enables the device driver to execute in a virtual machine on a workstation. Charm makes this possible by using the actual mobile system only for servicing the low-level and infrequent I/O operations through a low-latency and customized USB channel. Charm does not require any specialized hardware and is immediately available to analysts. We show that it is feasible to apply Charm to various device drivers, including camera, audio, GPU, and IMU sensor drivers, in different mobile systems, including LG Nexus 5X, Huawei Nexus 6P, and Samsung Galaxy S7. In an extensive evaluation, we show that Charm enhances the usability of fuzzing of device drivers, enables record-andreplay of driver’s execution, and facilitates detailed vulnerability analysis. Altogether, these capabilities have enabled us to find 25 bugs in device drivers, analyze 3 existing ones, and even build an arbitrary-code-execution kernel exploit using one of them. 1Charm is open sourced: https://trusslab.github.io/charm/
Comparing gold nano-particle enhanced radiotherapy with protons, megavoltage photons and kilovoltage photons: a Monte Carlo simulation.

Gold nanoparticles (GNPs) have shown potential to be used as a radiosensitizer for radiation therapy. Despite extensive research activity to study GNP radiosensitization using photon beams, only a few studies have been carried out using proton beams. In this work Monte Carlo simulations were used to assess the dose enhancement of GNPs for proton therapy. The enhancement effect was compared between a clinical proton spectrum, a clinical 6 MV photon spectrum, and a kilovoltage photon source similar to those used in many radiobiology lab settings. We showed that the mechanism by which GNPs can lead to dose enhancements in radiation therapy differs when comparing photon and proton radiation. The GNP dose enhancement using protons can be up to 14 and is independent of proton energy, while the dose enhancement is highly dependent on the photon energy used. For the same amount of energy absorbed in the GNP, interactions with protons, kVp photons and MV photons produce similar doses within several nanometers of the GNP surface, and differences are below 15% for the first 10 nm. However, secondary electrons produced by kilovoltage photons have the longest range in water as compared to protons and MV photons, e.g. they cause a dose enhancement 20 times higher than the one caused by protons 10 μm away from the GNP surface. We conclude that GNPs have the potential to enhance radiation therapy depending on the type of radiation source. Proton therapy can be enhanced significantly only if the GNPs are in close proximity to the biological target.
Deriving Boolean structures from distributional vectors

Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up. We combine the advantages of the two views by inducing a mapping from distributional vectors of words (or sentences) into a Boolean structure of the kind in which natural language terms are assumed to denote. We evaluate this Boolean Distributional Semantic Model (BDSM) on recognizing entailment between words and sentences. The method achieves results comparable to a state-of-the-art SVM, degrades more gracefully when less training data are available and displays interesting qualitative properties.
Towards Semi-automatic Generation of R2R Mappings

Translating data from linked data sources to the vocabulary that is expected by a linked data application requires a large number of mappings and can require a lot of structural transformations as well as complex property value transformations. The R2R mapping language is a language based on SPARQL for publishing expressive mappings on the web. However, the specification of R2R mappings is not an easy task. This paper therefore proposes the use of mapping patterns to semi-automatically generate R2R mappings between RDF vocabularies. In this paper, we first specify a mapping language with a high level of abstraction to transform data from a source ontology to a target ontology vocabulary. Second, we introduce the proposed mapping patterns. Finally, we present a method to semi-automatically generate R2R mappings using the mapping
Active pebbles: parallel programming for data-driven applications

The scope of scientific computing continues to grow and now includes diverse application areas such as network analysis, combinatorialcomputing, and knowledge discovery, to name just a few. Large problems in these application areas require HPC resources, but they exhibit computation and communication patterns that are irregular, fine-grained, and non-local, making it difficult to apply traditional HPC approaches to achieve scalable solutions. In this paper we present Active Pebbles, a programming and execution model developed explicitly to enable the development of scalable software for these emerging application areas. Our approach relies on five main techniques--scalable addressing, active routing, message coalescing, message reduction, and termination detection--to separate algorithm expression from communication optimization. Using this approach, algorithms can be expressed in their natural forms, with their natural levels of granularity, while optimizations necessary for scalability can be applied automatically to match the characteristics of particular machines. We implement several example kernels using both Active Pebbles and existing programming models, evaluating both programmability and performance. Our experimental results demonstrate that the Active Pebbles model can succinctly and directly express irregular application kernels, while still achieving performance comparable to MPI-based implementations that are significantly more complex.
Intelligence, birth order, and family size.

The analysis of the National Child Development Study in the United Kingdom (n = 17,419) replicates some earlier findings and shows that genuine within-family data are not necessary to make the apparent birth-order effect on intelligence disappear. Birth order is not associated with intelligence in between-family data once the number of siblings is statistically controlled. The analyses support the admixture hypothesis, which avers that the apparent birth-order effect on intelligence is an artifact of family size, and cast doubt on the confluence and resource dilution models, both of which claim that birth order has a causal influence on children's cognitive development. The analyses suggest that birth order has no genuine causal effect on general intelligence.
Picturing Science: Design Patterns in Graphical Abstracts

A graphical abstract (GA) provides a concise visual summary of a scientific contribution. GAs are increasingly required by journals to help make scientific publications more accessible to readers. We characterize the design space of GAs through a qualitative analysis of 54 GAs from a range of disciplines, and descriptions of GA design principles from scientific publishers. We present a set of design dimensions, visual structures, and design templates that describe how GAs communicate via pictorial and symbolic elements. By reflecting on how GAs employ visual metaphors, representational genres, and text relative to prior characterizations of how diagrams communicate, our work sheds light on how and why GAs may be distinct. We outline steps for future work at the intersection of HCI, AI, and scientific communication aimed at the creation of GAs.
German Multicenter Study Investigating 177Lu-PSMA-617 Radioligand Therapy in Advanced Prostate Cancer Patients.

177Lu-labeled PSMA-617 is a promising new therapeutic agent for radioligand therapy (RLT) of patients with metastatic castration-resistant prostate cancer (mCRPC). Initiated by the German Society of Nuclear Medicine, a retrospective multicenter data analysis was started in 2015 to evaluate efficacy and safety of 177Lu-PSMA-617 in a large cohort of patients.



One hundred forty-five patients (median age, 73 y; range, 43-88 y) with mCRPC were treated with 177Lu-PSMA-617 in 12 therapy centers between February 2014 and July 2015 with 1-4 therapy cycles and an activity range of 2-8 GBq per cycle. Toxicity was categorized by the common toxicity criteria for adverse events (version 4.0) on the basis of serial blood tests and the attending physician's report. The primary endpoint for efficacy was biochemical response as defined by a prostate-specific antigen decline ≥ 50% from baseline to at least 2 wk after the start of RLT.



A total of 248 therapy cycles were performed in 145 patients. Data for biochemical response in 99 patients as well as data for physician-reported and laboratory-based toxicity in 145 and 121 patients, respectively, were available. The median follow-up was 16 wk (range, 2-30 wk). Nineteen patients died during the observation period. Grade 3-4 hematotoxicity occurred in 18 patients: 10%, 4%, and 3% of the patients experienced anemia, thrombocytopenia, and leukopenia, respectively. Xerostomia occurred in 8%. The overall biochemical response rate was 45% after all therapy cycles, whereas 40% of patients already responded after a single cycle. Elevated alkaline phosphatase and the presence of visceral metastases were negative predictors and the total number of therapy cycles positive predictors of biochemical response.



The present retrospective multicenter study of 177Lu-PSMA-617 RLT demonstrates favorable safety and high efficacy exceeding those of other third-line systemic therapies in mCRPC patients. Future phase II/III studies are warranted to elucidate the survival benefit of this new therapy in patients with mCRPC.
Rapid field assessment of RO desalination of brackish agricultural drainage water.

Rapid field evaluation of RO feed filtration requirements, selection of effective antiscalant type and dose, and estimation of suitable scale-free RO recovery level were demonstrated using a novel approach based on direct observation of mineral scaling and flux decline measurements, utilizing an automated Membrane Monitor (MeMo). The MeMo, operated in a stand-alone single-pass desalting mode, enabled rapid assessment of the adequacy of feed filtration by enabling direct observation of particulate deposition on the membrane surface. The diagnostic field study with RO feed water of high mineral scaling propensity revealed (via direct MeMo observation) that suspended particulates (even for feed water of turbidity <1 NTU) could serve as seeds for promoting surface crystal nucleation. With feed filtration optimized, a suitable maximum RO water recovery, with complete mineral scale suppression facilitated by an effective antiscalant dose, can be systematically and directly identified (via MeMo) in the field for a given feed water quality. Scale-free operating conditions, determined via standalone MeMo rapid diagnostic tests, were shown to be applicable to spiral-would RO system as validated via both flux decline measurements and ex-situ RO plant membrane scale monitoring. It was shown that the present approach is suitable for rapid field assessment of RO operability and it is particularly advantageous when evaluating water sources of composition that may vary both temporally and across the regions of interest.
Synapses and memory storage.

The synapse is the functional unit of the brain. During the last several decades we have acquired a great deal of information on its structure, molecular components, and physiological function. It is clear that synapses are morphologically and molecularly diverse and that this diversity is recruited to different functions. One of the most intriguing findings is that the size of the synaptic response in not invariant, but can be altered by a variety of homo- and heterosynaptic factors such as past patterns of use or modulatory neurotransmitters. Perhaps the most difficult challenge in neuroscience is to design experiments that reveal how these basic building blocks of the brain are put together and how they are regulated to mediate the information flow through neural circuits that is necessary to produce complex behaviors and store memories. In this review we will focus on studies that attempt to uncover the role of synaptic plasticity in the regulation of whole-animal behavior by learning and memory.
Preserving output-privacy in data stream classification

Privacy-preservation has emerged to be a major concern in devising a data mining system. But, protecting the privacy of data mining input does not guarantee a privacy-preserved output. This paper focuses on preserving the privacy of data mining output and particularly the output of classification task. Further, instead of static datasets, we consider the classification of continuously arriving data streams: a rapidly growing research area. Due to the challenges of data stream classification such as vast volume, a mixture of labeled and unlabeled instances throughout the stream and timely classifier publication, enforcing privacy-preservation techniques becomes even more challenging. In order to achieve this goal, we propose a systematic method for preserving output-privacy in data stream classification that addresses several applications like loan approval, credit card fraud detection, disease outbreak or biological attack detection. Specifically, we propose an algorithm named Diverse and k-Anonymized HOeffding Tree (DAHOT) that is an amalgamation of popular data stream classification algorithm Hoeffding tree and a variant of k-anonymity and l-diversity principles. The empirical results on real and synthetic data streams verify the effectiveness of DAHOT as compared to its bedrock Hoeffding tree and two other techniques, one that learns sanitized decision trees from sampled data stream and other technique that uses ensemble-based classification. DAHOT guarantees to preserve the private patterns while classifying the data streams accurately.
Eye-Blink Detection Using Facial Landmarks

A real-time algorithm to detect eye blinks in a video sequence from a standard camera is proposed. Recent landmark detectors, trained on in-the-wild datasets exhibit excellent robustness against face resolution, varying illumination and facial expressions. We show that the landmarks are detected precisely enough to reliably estimate the level of the eye openness. The proposed algorithm therefore estimates the facial landmark positions, extracts a single scalar quantity – eye aspect ratio (EAR) – characterizing the eye openness in each frame. Finally, blinks are detected either by an SVM classifier detecting eye blinks as a pattern of EAR values in a short temporal window or by hidden Markov model that estimates the eye states followed by a simple state machine recognizing the blinks according to the eye closure lengths. The proposed algorithm has comparable results with the state-of-the-art methods on three standard datasets.
Biomedical Applications of Biodegradable Polyesters

The focus in the field of biomedical engineering has shifted in recent years to biodegradable polymers and, in particular, polyesters. Dozens of polyester-based medical devices are commercially available, and every year more are introduced to the market. The mechanical performance and wide range of biodegradation properties of this class of polymers allow for high degrees of selectivity for targeted clinical applications. Recent research endeavors to expand the application of polymers have been driven by a need to target the general hydrophobic nature of polyesters and their limited cell motif sites. This review provides a comprehensive investigation into advanced strategies to modify polyesters and their clinical potential for future biomedical applications.
In-vitro platform to study ultrasound as source for wireless energy transfer and communication for implanted medical devices

A platform to study ultrasound as a source for wireless energy transfer and communication for implanted medical devices is described. A tank is used as a container for a pair of electroacoustic transducers, where a control unit is fixed to one wall of the tank and a transponder can be manually moved in three axes and rotate using a mechanical system. The tank is filled with water to allow acoustic energy and data transfer, and the system is optimized to avoid parasitic effects due to cables, reflection paths and cross talk problems. A printed circuit board is developed to test energy scavenging such that enough acoustic intensity is generated by the control unit to recharge a battery loaded to the transponder. In the same manner, a second printed circuit board is fabricated to study transmission of information through acoustic waves.
NATURAL CAPACITOR VOLTAGE BALANCE IN MULTILEVEL FLYING CAPACITOR CONVERTRS . A REVIEW OF RESEARCH ACHIEVEMENTS

The flying-capacitor (FC) topology is one of the more well-established ideas of multilevel conversion, typically applied as an inverter. One of the biggest advantages of the FC converter is the ability to naturally balance capacitor voltage. When natural balancing occurs neither measurements, nor additional control is needed to maintain required capacitors voltage sharing. However, in order to achieve natural voltage balancing suitable conditions must be achieved such as the topology, number of levels, modulation strategy as well as impedance of the output circuitry. Nevertheless this method is effectively applied in various classes of the converter such as inverters, multicell DC-DC, switch-mode DC-DC, AC-AC, as well as rectifiers. The next important issue related to the natural balancing process is its dynamics. Furthermore, in order to reinforce the balancing mechanism an auxiliary resonant balancing circuit is utilized in the converter which can also be critical in the AC-AC converters or switch mode DC-DC converters. This paper also presents an issue of choosing modulation strategy for the FC converter due to the fact that the natural balancing process is well-established for phase shifted PWM whilst other types of modulation can be more favorable for the power quality.
The immune response of Drosophila

Drosophila mounts a potent host defence when challenged by various microorganisms. Analysis of this defence by molecular genetics has now provided a global picture of the mechanisms by which this insect senses infection, discriminates between various classes of microorganisms and induces the production of effector molecules, among which antimicrobial peptides are prominent. An unexpected result of these studies was the discovery that most of the genes involved in the Drosophila host defence are homologous or very similar to genes implicated in mammalian innate immune defences. Recent progress in research on Drosophila immune defence provides evidence for similarities and differences between Drosophila immune responses and mammalian innate immunity.
Academic Search Engines: A Quantitative Outlook

El autor es José Luis Ortega, investigador del CSIC y miembro del Laboratorio de Cibermetría, que cuenta con una importante trayectoria investigadora con publicaciones nacionales e internacionales en el ámbito de la cibermetría, la visualización de información y el análisis de redes. La obra está escrita en un inglés claro y sencillo y su título refleja de forma precisa su contenido: los motores de búsqueda académicos.
IOTSim: A simulator for analysing IoT applications

A disruptive technology that is influencing not only computing paradigm but every other business is the rise of big data. Internet of Things (IoT) applications are considered to be a major source of big data. Such IoT applications are in general supported through clouds where data is stored and processed by big data processing systems. In order to improve the efficiency of cloud infrastructure so that they can efficiently support IoT big data applications, it is important to understand how these applications and the corresponding big data processing systems will perform in cloud computing environments. However, given the scalability and complex requirements of big data processing systems, an empirical evaluation on actual cloud infrastructure can hinder the development of timely and cost effective IoT solutions. Therefore, a simulator supporting IoT applications in cloud environment is highly demanded, but such work is still in its infancy. To fill this gap, we have designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. A real case study validates the efficacy of the simulator.
Customizing Low-Precision Deep Neural Networks for FPGAs

In this paper, we argue that instead of solely focusing on developing efficient architectures to accelerate well-known low-precision CNNs, we should also seek to modify the network to suit the FPGA. We develop a fully automative toolflow which focuses on modifying the network through filter pruning, such that it efficiently utilizes the FPGA hardware whilst satisfying a predefined accuracy threshold. Although fewer weights are re-moved in comparison to traditional pruning techniques designed for software implementations, the overall model complexity and feature map storage is greatly reduced. We implement the AlexNet and TinyYolo networks on the large-scale ImageNet and PascalVOC datasets, to demonstrate up to roughly 2× speedup in frames per second and 2× reduction in resource requirements over the original network, with equal or improved accuracy.
Direct Sparse Visual-Inertial Odometry Using Dynamic Marginalization

We present VI-DSO, a novel approach for visual-inertial odometry, which jointly estimates camera poses and sparse scene geometry by minimizing photometric and IMU measurement errors in a combined energy functional. The visual part of the system performs a bundle-adjustment like optimization on a sparse set of points, but unlike key-point based systems it directly minimizes a photometric error. This makes it possible for the system to track not only corners, but any pixels with large enough intensity gradients. IMU information is accumulated between several frames using measurement preintegration, and is inserted into the optimization as an additional constraint between keyframes. We explicitly include scale and gravity direction into our model and jointly optimize them together with other variables such as poses. As the scale is often not immediately observable using IMU data this allows us to initialize our visual-inertial system with an arbitrary scale instead of having to delay the initialization until everything is observable. We perform partial marginalization of old variables so that updates can be computed in a reasonable time. In order to keep the system consistent we propose a novel strategy which we call “dynamic marginalization”. This technique allows us to use partial marginalization even in cases where the initial scale estimate is far from the optimum. We evaluate our method on the challenging EuRoC dataset, showing that VI-DSO outperforms the state of the art.