nils-herrmann/clean_scirepeval huggingface dataset:
Iododerma following sitz bath with povidone-iodine.

A 50-year-old man developed numerous pustules and bullae on the trunk and limbs 15 days after anal fissure surgery. The clinicopathological diagnosis was iododerma induced by topical povidone-iodine sitz baths postoperatively. Complete resolution occurred within 3 weeks using systemic corticosteroids and forced diuresis.
Adaptive Adversarial Attack on Scene Text Recognition

Recent studies have shown that state-of-the-art deep learning models are vulnerable to the inputs with small perturbations (adversarial examples). We observe two critical obstacles in adversarial examples: (i) Strong adversarial attacks (e.g., C&W attack) require manually tuning hyper-parameters and take a long time to construct an adversarial example, making it impractical to attack real-time systems; (ii) Most of the studies focus on non-sequential tasks, such as image classification, yet only a few consider sequential tasks. In this work, we speed up adversarial attacks, especially on sequential learning tasks. By leveraging the uncertainty of each task, we directly learn the adaptive multi-task weightings, without manually searching hyper-parameters. A unified architecture is developed and evaluated for both nonsequential tasks and sequential ones. To validate the effectiveness, we take the scene text recognition task as a case study. To our best knowledge, our proposed method is the first attempt to adversarial attack for scene text recognition. Adaptive Attack achieves over 99.9% success rate with 3 ∼ 6× speedup compared to state-of-the-art adversarial attacks.
Social Media Image Analysis for Public Health

Several projects have shown the feasibility to use emph{textual} social media data to track public health concerns, such as temporal influenza patterns or geographical obesity patterns. In this paper, we look at whether geo-tagged emph{images} from Instagram also provide a viable data source. Especially for "lifestyle" diseases, such as obesity, drinking or smoking, images of social gatherings could provide information that is not necessarily shared in, say, tweets. In this study, we explore whether (i) tags provided by the users and (ii) annotations obtained via automatic image tagging are indeed valuable for studying public health. We find that both user-provided and machine-generated tags provide information that can be used to infer a county's health statistics. Whereas for most statistics user-provided tags are better features, for predicting excessive drinking machine-generated tags such as "liquid' and "glass' yield better models. This hints at the potential of using machine-generated tags to study substance abuse.
The Immune System Drugs in Fish: Immune Function, Immunoassay, Drugs

Fish is a heterogeneous group of different organisms which include the agnathans (hagfishes and lampreys), condryctians (sharks and rays) and teleosteans (bony fish). Like in all vertebrates, fish have cellular and humoral immune responses, and central organs whose the main function is involved in immune defence. Fish and mammals show some similarities and some differences regarding immune function (Cabezas, 2006; Nelson, 1994; Tort et al., 2003; Zapata et al., 1996). The fish defence system is basically similar to that described in mammals. For cellular defence systems in fish, teleosts have phagocytic cells similar to macrophages, neutrophils, and natural killer (NK) cells, as well as T and B lymphocytes. Teleosts also have various humoral defence components such as complement (classical and alternative pathways), lysozyme, natural hemolysin, transferrin and C-reactive protein (CRP). The existence of cytokines (such as interferon, interleukin 2 (IL-2), macrophage activating factors (MAF)) has also been reported (Secombes et al., 1996, Sakai, 1999). On the contrary, the morphology of the immune system is quite different between fish and mammals. Most obvious is the fact that fish lack bone marrow and lymph nodes. Instead, the head kidney serves as a major lymphoid organ, in addition to the thymus and spleen (Press & Evensen, 1999). Gut associated lymphoid tissues are also known lymphoid organs, and have been shown to function in eliciting immune responses in carp (Joosten et al., 1996). Some teleosts, such as plaice, have been shown to possess a lymphatic system that is differentiated from the blood vascular system, though the existence of such a system has been challenged in other species (Hølvold, 2007). Health of fish depends on the interrelationship of some major components of the fish and the environment in which they live . Tolerance of these various factors is dependent on the host and in many case the husbandry practices. The environment may be the most critical component of the fish health matrix because environmental quality influences the fish’s physiological well-being, species cultured, feeding regimes, rate of growth, and ability to maintain natural and acquired resistance and immunity. Overall physiological status of the fish host is determined by the husbandry practice, environmental quality, the fish’s nutritional well-being and the pathogen, all of which influence the natural resistance and acquired immunity of the host. It is common knowledge that fish stressed by one of these factors are more susceptible to infection (Magnadóttir, 2010; Plumb & Hanson, 2011).
Rate-Distortion Analysis of Directional Wavelets

The inefficiency of separable wavelets in representing smooth edges has led to a great interest in the study of new 2-D transformations. The most popular criterion for analyzing these transformations is the approximation power. Transformations with near-optimal approximation power are useful in many applications such as denoising and enhancement. However, they are not necessarily good for compression. Therefore, most of the nearly optimal transformations such as curvelets and contourlets have not found any application in image compression yet. One of the most promising schemes for image compression is the elegant idea of directional wavelets (DIWs). While these algorithms outperform the state-of-the-art image coders in practice, our theoretical understanding of them is very limited. In this paper, we adopt the notion of rate-distortion and calculate the performance of the DIW on a class of edge-like images. Our theoretical analysis shows that if the edges are not “sharp,” the DIW will compress them more efficiently than the separable wavelets. It also demonstrates the inefficiency of the quadtree partitioning that is often used with the DIW. To solve this issue, we propose a new partitioning scheme called megaquad partitioning. Our simulation results on real-world images confirm the benefits of the proposed partitioning algorithm, promised by our theoretical analysis.
Color sensors for indole vapors based on Ehrlich-type reactions.

A sensitive and simple color sensor for indole vapors has been developed based on the Ehrlich-type reaction in solid polymer film. Upon 60-min exposure of the film sensor to the air containing 5 - 100 ppb of indole vapors, pink or magenta color could be recognized by the naked eyes. Alternatively, a trial gas detector tube has been prepared by mixing the reagents with sea sand. When air (100 mL) was pumped through the detector tube, indole vapors above 20 ppb could be detected within 1 min. The sensing was selective to the vapors of indoles and pyrroles, and other VOCs or ambient moisture did not interfere.
Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins

A molecular test for Alzheimer's disease could lead to better treatment and therapies. We found 18 signaling proteins in blood plasma that can be used to classify blinded samples from Alzheimer's and control subjects with close to 90% accuracy and to identify patients who had mild cognitive impairment that progressed to Alzheimer's disease 2–6 years later. Biological analysis of the 18 proteins points to systemic dysregulation of hematopoiesis, immune responses, apoptosis and neuronal support in presymptomatic Alzheimer's disease.
ANTIMICROBIAL EFFECTS OF SPICES AND HERBS ESSENTIAL OILS

Spices and herbs have been used as food additives since ancient times, as flavouring agents but also as natural food preservatives. A number of spices shows antimicrobial activity against different types of microorganisms. This article gives a literature review of recent investigations considering antimicrobial activity of essential oils widely used spices and herbs, such as garlic, mustard, cinnamon, cumin, clove, bay, thyme, basil, oregano, pepper, ginger, sage, rosemary etc., against most common bacteria and fungi that contaminate food (Listeria spp., Staphylococcus spp., Salmonella spp., Escherichia spp., Pseudomonas spp., Aspergillus spp., Cladosporium spp. and many others). Antimicrobial activity depends on the type of spice or herb, type of food and microorganism, as well as on the chemical composition and content of extracts and essential oils. Summarizing results of different investigations, relative antimicrobial effectiveness can be made, and it shows that cinnamon, cloves and mustrad have very strong antimicrobial potential, cumin, oregano, sage, thyme and rosemary show medium inhibitory effect, and spices such as pepper and ginger have weak inhibitory effect.
Design and Implementation of Numerical Linear Algebra Algorithms on Fixed Point DSPs

Numerical linear algebra algorithms use the inherent elegance of matrix formulations and are usually implemented using C/C++ floating point representation. The system implementation is faced with practical constraints because these algorithms usually need to run in real time on fixed point digital signal processors (DSPs) to reduce total hardware costs. Converting the simulation model to fixed point arithmetic and then porting it to a target DSP device is a difficult and time-consuming process. In this paper, we analyze the conversion process. We transformed selected linear algebra algorithms from floating point to fixed point arithmetic, and compared real-time requirements and performance between the fixed point DSP and floating point DSP algorithm implementations. We also introduce an advanced code optimization and an implementation by DSP-specific, fixed point C code generation. By using the techniques described in the paper, speed can be increased by a factor of up to 10 compared to floating point emulation on fixed point hardware.
Optimized scale-and-stretch for image resizing

We present a "scale-and-stretch" warping method that allows resizing images into arbitrary aspect ratios while preserving visually prominent features. The method operates by iteratively computing optimal local scaling factors for each local region and updating a warped image that matches these scaling factors as closely as possible. The amount of deformation of the image content is guided by a significance map that characterizes the visual attractiveness of each pixel; this significance map is computed automatically using a novel combination of gradient and salience-based measures. Our technique allows diverting the distortion due to resizing to image regions with homogeneous content, such that the impact on perceptually important features is minimized. Unlike previous approaches, our method distributes the distortion in all spatial directions, even when the resizing operation is only applied horizontally or vertically, thus fully utilizing the available homogeneous regions to absorb the distortion. We develop an efficient formulation for the nonlinear optimization involved in the warping function computation, allowing interactive image resizing.
Biostatistics 101: data presentation.

Correspondence to: Y H Chan Tel: (65) 6317 2121 Fax: (65) 6317 2122 Email: chanyh@ cteru.gov.sg  Now we are at the last stage of the research process: Statistical Analysis & Reporting. In this article, we will discuss how to present the collected data and the forthcoming write-ups will highlight on the appropriate statistical tests to be applied. The terms Sample & Population; Parameter & Statistic; Descriptive & Inferential Statistics; Random variables; Sampling Distribution of the Mean; Central Limit Theorem could be read-up from the references indicated. To be able to correctly present descriptive (and inferential) statistics, we have to understand the two data types  that are usually encountered in any research study.
An Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bi-criteria TSP

The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the Ant Colony Optimization metaheuristic. In this contribution, the existing algorithms of this kind are reviewed and experimentally tested in several instances of the bi-objective traveling salesman problem, comparing their performance with that of two well-known multi-objective genetic algorithms.
Blind image deconvolution using constrained variance maximization

This paper describes an algorithm based on constrained variance maximization for the restoration of a blurred image. Blurring is a smoothing process by definition. Accordingly, the deblurring filter shall be able to perform as a high pass filter, which increases the variance. Therefore, we formulate a variance maximization object function for the deconvolution filter. Using principal component analysis (PCA), we find the filter maximizing the object function. PCA is more than just a high pass filter; by maximizing the variances, it is able to perform the decorrelation, by which the original image is extracted from the mixture (the blurred image). Our approach was experimentally compared with the adaptive Lucy-Richardson maximum likelihood (ML) algorithm. The comparative results on both synthesized and real blurred images are included.
Characterizing and Adapting the Consistency-Latency Tradeoff in Distributed Key-Value Stores

The CAP theorem is a fundamental result that applies to distributed storage systems. In this article, we first present and prove two CAP-like impossibility theorems. To state these theorems, we present probabilistic models to characterize the three important elements of the CAP theorem: consistency (C), availability or latency (A), and partition tolerance (P). The theorems show the un-achievable envelope, that is, which combinations of the parameters of the three models make them impossible to achieve together. Next, we present the design of a class of systems called Probabilistic CAP (PCAP) that perform close to the envelope described by our theorems. In addition, these systems allow applications running on a single data center to specify either a latency Service Level Agreement (SLA) or a consistency SLA. The PCAP systems automatically adapt, in real time and under changing network conditions, to meet the SLA while optimizing the other C/A metric. We incorporate PCAP into two popular key-value stores: Apache Cassandra and Riak. Our experiments with these two deployments, under realistic workloads, reveal that the PCAP systems satisfactorily meets SLAs and perform close to the achievable envelope. We also extend PCAP from a single data center to multiple geo-distributed data centers.
Adaptively Secure Threshold Cryptography: Introducing Concurrency, Removing Erasures

We put forward two new measures of security for threshold schemes secure in the adaptive adversary model: security under concurrent composition; and security without the assumption of reliable erasure. Using novel constructions and analytical tools, in both these settings, we exhibit efficient secure threshold protocols for a variety of cryptographic applications. In particular, based on the recent scheme by Cramer-Shoup, we construct adaptively secure threshold cryptosystems secure against adaptive chosen ciphertext attack under the DDH intractability assumption. Our techniques are also applicable to other cryptosystems and signature schemes, like RSA, DSS, and ElGamal. Our techniques include the first efficient implementation, for a wide but special class of protocols, of secure channels in erasure-free adaptive model. Of independent interest, we present the notion of a committed proof.
Database Architecture Optimized for the New Bottleneck: Memory Access

In the past decade, advances in speed of commodity CPUs have far out-paced advances in memory latency. Main-memory access is therefore increasingly a performance bottleneck for many computer applications, including database systems. In this article, we use a simple scan test to show the severe impact of this bottleneck. The insights gained are translated into guidelines for database architecture; in terms of both data structures and algorithms. We discuss how vertically fragmented data structures optimize cache performance on sequential data access. We then focus on equi-join, typically a random-access operation, and introduce radix algorithms for partitioned hash-join. The performance of these algorithms is quantified using a detailed analytical model that incorporates memory access cost. Experiments that validate this model were performed on the Monet database system. We obtained exact statistics on events like TLB misses, L1 and L2 cache misses, by using hardware performance counters found in modern CPUs. Using our cost model, we show how the carefully tuned memory access pattern of our radix algorithms make them perform well, which is confirmed by experimental results. This work was carried out when the author was at the University of Amsterdam, supported by SION grant 612-23-431 Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.
Electroneurographic evidence of polyneuropathy in chronic liver disease

An electroneurographic study performed on the peripheral nerves of 25 patients with severe cirrhosis following viral hepatitis showed slight slowing (P > 0.05) of motor conduction velocity (CV) and significant diminution (P < 0.001) of sensory CV and mixed sensorimotor-evoked potentials, associated with a significant decrease in the amplitude of sensory evoked potentials. The slowing was about equal in the distal (digital) and in the proximal segments of the same nerve. A mixed axonal degeneration and segmental demyelination is presumed to explain these findings. The CV measurements proved helpful for an early diagnosis of hepatic polyneuropathy showing subjective symptoms in the subclinical stage. Elektroneurographische Untersuchungen der peripheren Nerven bei 25 Patienten mit postviralen Leberzirrhosen ergaben folgendes: geringe Verminderung (P > 0.05) der motorischen Leitgeschwindigkeit (LG) und eine signifikant verlangsamte LG in sensiblen Fasern (P < 0.001), in beiden proximalen und distalen Fasern. Es wurde in den gemischten evozierten Potentialen eine Verlangsamung der LG festgestellt, zwischen den Werten der motorischen und sensiblen Fasern. Gleichzeitig wurde eine Minderung der Amplitude des NAP beobachtet. Diese Befunde sprechen für eine axonale Degeneration und eine Demyelinisierung in den meisten untersuchten peripheren Nerven. Elektroneurographische Untersuchungen erlaubten den funktionellen Zustand des peripheren Nervens abzuschätzen und bestimmte Veränderungen bereits im Initialstadium der Erkrankung aufzudecken, wenn der Patient noch keine klinischen Zeichen einer peripheren Neuropathie bietet.
Explaining Visual Changes in Web Interfaces

Web developers often want to repurpose interactive behaviors from third-party web pages, but struggle to locate the specific source code that implements the behavior. This task is challenging because developers must find and connect all of the non-local interactions between event-based JavaScript code, declarative CSS styles, and web page content that combine to express the behavior.
 The Scry tool embodies a new approach to locating the code that implements interactive behaviors. A developer selects a page element; whenever the element changes, Scry captures the rendering engine's inputs (DOM, CSS) and outputs (screenshot) for the element. For any two captured element states, Scry can compute how the states differ and which lines of JavaScript code were responsible. Using Scry, a developer can locate an interactive behavior's implementation by picking two output states; Scry indicates the JavaScript code directly responsible for their differences.
Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps

Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motivated research in applications of such models for medical image analysis. If CNN-based models are to be helpful in a medical context, they need to be precise, interpretable, and uncertainty in predictions must be well understood. In this paper, we develop and evaluate recent advances in uncertainty estimation and model interpretability in the context of semantic segmentation of polyps from colonoscopy images. We evaluate and enhance several architectures of Fully Convolutional Networks (FCNs) for semantic segmentation of colorectal polyps and provide a comparison between these models. Our highest performing model achieves a 76.06% mean IOU accuracy on the EndoScene dataset, a considerable improvement over the previous state-of-the-art.
A Theoretical Analysis of Camera Response Functions in Image Deblurring

Motion deblurring is a long standing problem in computer vision and image processing. In most previous approaches, the blurred image is modeled as the convolution of a latent intensity image with a blur kernel. However, for images captured by a real camera, the blur convolution should be applied to scene irradiance instead of image intensity and the blurred results need to be mapped back to image intensity via the camera’s response function (CRF). In this paper, we present a comprehensive study to analyze the effects of CRFs on motion deblurring. We prove that the intensity-based model closely approximates the irradiance model at low frequency regions. However, at high frequency regions such as edges, the intensity-based approximation introduces large errors and directly applying deconvolution on the intensity image will produce strong ringing artifacts even if the blur kernel is invertible. Based on the approximation error analysis, we further develop a dualimage based solution that captures a pair of sharp/blurred images for both CRF estimation and motion deblurring. Experiments on synthetic and real images validate our theories and demonstrate the robustness and accuracy of our approach.
OntoMetrics: Application of On-line Ontology Metric Calculation

A lot of work has been done regarding ontology evaluation in the recent years. Automatically calculated indicators are needed in order to assess ontology quality. Manual evaluation of ontologies would be very time consuming. While there is tool support for the detection modelling errors and the violation of ontology modelling guidelines, there is a lack of support for calculating ontology metrics. Many metrics have been proposed that correlate for example with ontology characteristics like Readability, Adaptability, and Reusability. However, no tools have been created or tools are no longer maintained and bound to certain ontology editors. OntoMetrics fills this gap by providing an on-line platform for ontology metric calculation.This paper presents the current status of OntoMetrics, use cases and planned future developments.