nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive rendering with non-local means filtering

We propose a novel approach for image space adaptive sampling and filtering in Monte Carlo rendering. We use an iterative scheme composed of three steps. First, we adaptively distribute samples in the image plane. Second, we denoise the image using a non-linear filter. Third, we estimate the residual per-pixel error of the filtered rendering, and the error estimate guides the sample distribution in the next iteration. The effectiveness of our approach hinges on the use of a state of the art image denoising technique, which we extend to an adaptive rendering framework. A key idea is to split the Monte Carlo samples into two buffers. This improves denoising performance and facilitates variance and error estimation. Our method relies only on the Monte Carlo samples, allowing us to handle arbitrary light transport and lens effects. In addition, it is robust to high noise levels and complex image content. We compare our approach to a state of the art adaptive rendering technique based on adaptive bandwidth selection and demonstrate substantial improvements in terms of both numerical error and visual quality. Our framework is easy to implement on top of standard Monte Carlo renderers and it incurs little computational overhead.
The Cambridge Face Memory Test: Results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants

The two standardized tests of face recognition that are widely used suffer from serious shortcomings [Duchaine, B. & Weidenfeld, A. (2003). An evaluation of two commonly used tests of unfamiliar face recognition. Neuropsychologia, 41, 713-720; Duchaine, B. & Nakayama, K. (2004). Developmental prosopagnosia and the Benton Facial Recognition Test. Neurology, 62, 1219-1220]. Images in the Warrington Recognition Memory for Faces test include substantial non-facial information, and the simultaneous presentation of faces in the Benton Facial Recognition Test allows feature matching. Here, we present results from a new test, the Cambridge Face Memory Test, which builds on the strengths of the previous tests. In the test, participants are introduced to six target faces, and then they are tested with forced choice items consisting of three faces, one of which is a target. For each target face, three test items contain views identical to those studied in the introduction, five present novel views, and four present novel views with noise. There are a total of 72 items, and 50 controls averaged 58. To determine whether the test requires the special mechanisms used to recognize upright faces, we conducted two experiments. We predicted that controls would perform much more poorly when the face images are inverted, and as predicted, inverted performance was much worse with a mean of 42. Next we assessed whether eight prosopagnosics would perform poorly on the upright version. The prosopagnosic mean was 37, and six prosopagnosics scored outside the normal range. In contrast, the Warrington test and the Benton test failed to classify a majority of the prosopagnosics as impaired. These results indicate that the new test effectively assesses face recognition across a wide range of abilities.
08 67 0 v 1 [ cs . C V ] 2 3 M ar 2 01 8 Object Detection for Comics using Manga 109 Annotations

With the growth of digitized comics, image understanding techniques are becoming important. In this paper, we focus on object detection, which is a fundamental task of image understanding. Although convolutional neural networks (CNN)-based methods archived good performance in object detection for naturalistic images, there are two problems in applying these methods to the comic object detection task. First, there is no large-scale annotated comics dataset. The CNN-based methods require large-scale annotations for training. Secondly, the objects in comics are highly overlapped compared to naturalistic images. This overlap causes the assignment problem in the existing CNN-based methods. To solve these problems, we proposed a new annotation dataset and a new CNN model. We annotated an existing image dataset of comics and created the largest annotation dataset, named Manga109-annotations. For the assignment problem, we proposed a new CNN-based detector, SSD300-fork. We compared SSD300-fork with other detection methods using Manga109-annotations and confirmed that our model outperformed them based on the mAP score.
Qualitative research in health care. Assessing quality in qualitative research.

In the past decade, qualitative methods have become more commonplace in areas such as health services research and health technology assessment, and there has been a corresponding rise in the reporting of qualitative research studies in medical and related journals. Interest in these methods and their wider exposure in health research has led to necessary scrutiny of qualitative research. Researchers from other traditions are increasingly concerned to understand qualitative methods and, most importantly, to examine the claims researchers make about the findings obtained from these methods. The status of all forms of research depends on the quality of the methods used. In qualitative research, concern about assessing quality has manifested itself recently in the proliferation of guidelines for doing and judging qualitative work. Users and funders of research have had an important role in developing these guidelines as they become increasingly familiar with qualitative methods, but require some means of assessing their quality and of distinguishing “good” and “poor” quality research. However, the issue of “quality” in qualitative research is part of a much larger and contested debate about the nature of the knowledge produced by qualitative research, whether its quality can legitimately be judged, and, if so, how. This paper cannot do full justice to this wider epistemological debate. Rather it outlines two views of how qualitative methods might be judged and argues that qualitative research can be assessed according to two broad criteria: validity and relevance.
Class-Conditional Superresolution with GANs

● Super resolution enhances the resolution of a low-res image. ● Many deep learning models today work fairly well with an upscaling factor of 4x but use only the downscaled image as input. ● We demonstrate two class-conditional GAN models that outperform state-of-the-art GANs for superresolution: ○ Explicitly passes in a known class parameter as input ○ Uses a separately trained classifier to inform a ResNet about character attributes ● Both improve the quality of the super-resolved output.
Sensitivity enhancement of MEMS fluid flow rate and flow direction sensor

MEMS based flow rate sensor and flow direction sensor is a crucial to the determination of exact fluid flow path. Conventional rectangular cantilever beam detects based on the surface strain of the beam with respect to the mass flow. Different structured beam will greatly affect the sensitivity. In this paper different geometries of cantilever beam such as rectangular, inverted trapezoidal, triangular are designed and simulated using the finite element software INTELLISUITE 8.7. The simulation results show that the inverted trapezoidal geometries having the more deflection at same stress of the conventional beam and thus increase the sensitivity by approx. 1.45 times.
Forecasting the Saudi Arabia stock prices based on artificial neural networks model

In this paper, we have proposed artificial neural network for the prediction of Saudi stock market. The proposed predictions model, with its high degree of accuracy, could be used as investment advisor for the investors and traders in the Saudi stock market. The proposed model is based mainly on Saudi Stock market historical data covering a large span of time. Achieving reasonable accuracy rate of predication models will surely facilitate an increased confidence in the investment in the Saudi stock market. We have only used the closing price of the stock as the stock variable considered for input to the system. The number of windows gap to determine the numbers of previous days to be used in predicting the next day closing price data has been choosing based on experimental simulation carried out to determine the best possible value. Our results indicated that the proposed ANN model predicts the next day closing price stock market value with a very low RMSE down to 1.8174, very low MAD down to 18.2835, very low MAPE of down to 1.6476 and very high correlation coefficient of up to 99.9% for the test set, which is an indication that the model adequately mimics the trend of the market in its prediction. This performance is really encouraging and thus the proposed system will impact positively on the analysis and prediction of Saudi stock market in general.
Supporting Private Data on Hyperledger Fabric with Secure Multiparty Computation

Hyperledger Fabric is a "permissioned" blockchain architecture, providing a consistent distributed ledger, shared by a set of "peers." As with every blockchain architecture, the core principle of Hyperledger Fabric is that all the peers must have the same view of the shared ledger, making it challenging to support private data for the different peers. Extending Hyperledger Fabric to support private data (that can influence transactions) would open the door to many exciting new applications, in areas from healthcare to commerce, insurance, finance, and more. In this work we explored adding private-data support to Hyperledger Fabric using secure multiparty computation (MPC). Specifically, in our solution the peers store on the chain encryption of their private data, and use secure MPC whenever such private data is needed in a transaction. This solution is very general, allowing in principle to base transactions on any combination of public and private data. We created a demo of our solution over Hyperledger Fabric v1.0, implementing a bidding system where sellers can list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price itself. We implemented a smart contract (aka "chaincode") that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. The chaincode itself was written in Go, and we used the SWIG library to make it possible to call our protocol implementation in C++. We identified two basic services that should be added to Hyperledger Fabric to support our solution, and are now working on implementing them.
HMAC: Keyed-Hashing for Message Authentication

This memo provides information for the Internet community. This memo does not specify an Internet standard of any kind. Distribution of this memo is unlimited.  This document describes HMAC, a mechanism for message authentication using cryptographic hash functions. HMAC can be used with any iterative cryptographic hash function, e.g., MD5, SHA-1, in combination with a secret shared key. The cryptographic strength of HMAC depends on the properties of the underlying hash function.
MPLS based hybridization in SDN

The new paradigm of Software Defined Networking (SDN) although has great potential to address the complex problems presented by enterprise networks, it has its own deployment and scalability issues. Further, a full SDN deployment has its own business and economic challenges. A smooth transition from legacy networks to SDN (disruption free, accommodating budget constraints, with progressive improvement in network management) requires a hybrid networking model as an inevitable intermediate step; that allows heterogeneous paradigms to function together while the full transition is realized in phases. Therefore, the need of the hour is to develop an incremental deployment strategy that caters to the needs of the organization. We present here a class-based hybrid SDN model for Multi Protocol Label Switching (MPLS) networks. We discuss the model, design, components, their interactions, advantages and drawbacks. We also present a n implementation and evaluation of a prototype. In legacy networks, MPLS architecture closely resembles SDN paradigm in terms of separation of control and data planes, flow-abstraction etc. Moreover, ISPs have preferred MPLS over the years due to benefits of virtual private networks and traffic engineering. The central idea is to partition traffic using forwarding equivalence classes at the ingress router, the rules of which can be updated via a centralized controller using OpenFlow. Therefore, we aim to use the standard MPLS data-plane together with a control-plane based on OpenFlow to come up with a systematic incremental deployment methodology as well as a hybrid operation model
Analysis of modulation schemes for Bluetooth-LE module for Internet-of-Things (IoT) applications

Bluetooth transceivers have been in the active area of recent research being the key component of physical layer in the Bluetooth technology. The low power consumption of Bluetooth low energy (LE) devices compared to the conventional Bluetooth devices has enhanced its importance in Internet-of-Things (IoT) applications. Therefore, Bluetooth low energy device based solution needs expansion in the IoT network infrastructure. The transceivers in the literature and modulation schemes are compared and summarized. Energy consumption of modulation schemes in Bluetooth communication are analyzed and compared using the model presented in this work. In this approach considering both circuit and signal power consumption, optimum modulation order for minimum energy consumption has been found using numerical calculation and relation between signal to noise ratio (SNR) and channel capacity. Battery life for IoT sensors using Bluetooth LE technology as a wireless link has been analyzed considering multiple transaction times for transmitters having different power consumption. MFSK and more bandwidth-efficient GFSK are identified as low energy solution for all smart devices.
Measuring Semantic Similarity in the Taxonomy of WordNet

This paper presents a new model to measure semantic similarity in the taxonomy of WordNet, using edgecounting techniques. We weigh up our model against a benchmark set by human similarity judgment, and achieve a much improved result compared with other methods: the correlation with average human judgment on a standard 28 word pair dataset is 0.921, which is better than anything reported in the literature and also significantly better than average individual human judgments. As this set has been effectively used for algorithm selection and tuning, we also cross-validate an independent 37 word pair test set (0.876) and present results for the full 65 word pair superset (0.897).
Marginal structural models for estimating effect modification.

The use of marginal structural models (MSMs) to adjust for measured confounding factors is becoming increasingly common in observational studies. Here, we propose MSMs for estimating effect modification in observational cohort and case-control studies.



MSMs for estimating effect modification were derived by the use of the potential outcome model. The proposed methods were applied to a cohort study and a case-control study.



In cohort studies, effect modification can be estimated by the application of a logistic MSM to individuals who experienced the event in question. In case-control studies, effect modification can be estimated by the ratio between the estimate from the model applied to case data and that applied to control data. The application of the model to real data from a cohort study indicated that the estimate from the proposed method was close to that from standard regression analysis. In a case-control study, the estimate from the proposed method may be biased.



Epidemiological researchers can use MSMs to estimate effect modification. In case-control studies, it should be determined whether the estimated effect modification is biased by applying a logistic MSM of control data.
A Component-Based Dynamic Link Support for Safety-Critical Embedded Systems

Safety-critical embedded systems have to undergo rigorous development process in order to ensure that their function will not compromise humans or environment where they operate. Therefore, they rely on simple and proven-in-use design. However, with growing software complexity, maintenance becomes very important aspect in safety domain. Recent approaches for managing maintenance allow to perform changes on software at design-time, which implies that the whole system has to be rebuilt when the application software changes. In this paper, we describe more flexible solution for updating the application software. We apply the component-based paradigm to construct the application software, i.e. we define a model of a software function that can be dynamically linked with the entire operating system (OS). In order to avoid the usage of the OS-provided support for dynamic linking, we design software functions as position-independent and relocation-free binaries with well-defined interfaces. With the help of component-based paradigm we show how to simplify the link support and make it suitable for safety domain.
World ocean simulation system (WOSS): a simulation tool for underwater networks with realistic propagation modeling

Network simulators are a fundamental tool for the performance evaluation of protocols and applications in complex scenarios, which would be too expensive or infeasible to realize in practice.
 With the aim to provide a shared environment for the simulation of underwater networks we have adapted the ns2 network simulator to provide a detailed reproduction of the propagation of sound in water (i.e., by means of ray tracing instead of empirical relations). This has been tied to formerly available simulation frameworks (such as the MIRACLE extensions to ns2) to provide a completely customizable tool, including acoustic propagation, physical layer modeling, and cross-layer specification of networking protocols. In this paper, we describe our tool, and use it for a case study involving the comparison of three MAC protocols for underwater networks over different kinds of physical layers. Our results compare the transmission coordination approach chosen by each protocol, and show when it is better to rely on random access, as opposed to loose or tight coordination.
Image Captioning with Object Detection and Localization

Automatically generating a natural language description of an image is a task close to the heart of image understanding. In this paper, we present a multi-model neural network method closely related to the human visual system that automatically learns to describe the content of images. Our model consists of two sub-models: an object detection and localization model, which extract the information of objects and their spatial relationship in images respectively; Besides, a deep recurrent neural network (RNN) based on long short-term memory (LSTM) units with attention mechanism for sentences generation. Each word of the description will be automatically aligned to different objects of the input image when it is generated. This is similar to the attention mechanism of the human visual system. Experimental results on the COCO dataset showcase the merit of the proposed method, which outperform previous benchmark models.
Operator Splitting Methods in Compressive Sensing and Sparse Approximation

Compressive sensing and sparse approximation have many emerging applications, and are a relatively new driving force for development of splitting methods in optimization. Many sparse coding problems are well described by variational models with `1-norm penalties and constraints that are designed to promote sparsity. Successful algorithms need to take advantage of the separable structure of potentially complicated objectives by “splitting” them into simpler pieces and iteratively solving a sequence of simpler convex minimization problems. In particular, isolating `1 terms from the rest of the objective leads to simple soft thresholding updates or `1 ball projections. A few basic splitting techniques can be used to design a huge variety of relevant algorithms. This chapter will focus on operator splitting strategies that are based on proximal operators, duality, and alternating direction methods. These will be explained in the context of basis pursuit variants and through compressive sensing applications.
Evolution of RNA- and DNA-guided antivirus defense systems in prokaryotes and eukaryotes: common ancestry vs convergence

Complementarity between nucleic acid molecules is central to biological information transfer processes. Apart from the basal processes of replication, transcription and translation, complementarity is also employed by multiple defense and regulatory systems. All cellular life forms possess defense systems against viruses and mobile genetic elements, and in most of them some of the defense mechanisms involve small guide RNAs or DNAs that recognize parasite genomes and trigger their inactivation. The nucleic acid-guided defense systems include prokaryotic Argonaute (pAgo)-centered innate immunity and CRISPR-Cas adaptive immunity as well as diverse branches of RNA interference (RNAi) in eukaryotes. The archaeal pAgo machinery is the direct ancestor of eukaryotic RNAi that, however, acquired additional components, such as Dicer, and enormously diversified through multiple duplications. In contrast, eukaryotes lack any heritage of the CRISPR-Cas systems, conceivably, due to the cellular toxicity of some Cas proteins that would get activated as a result of operon disruption in eukaryotes. The adaptive immunity function in eukaryotes is taken over partly by the PIWI RNA branch of RNAi and partly by protein-based immunity. In this review, I briefly discuss the interplay between homology and analogy in the evolution of RNA- and DNA-guided immunity, and attempt to formulate some general evolutionary principles for this ancient class of defense systems. This article was reviewed by Mikhail Gelfand and Bojan Zagrovic.
Power Analysis Resistant SRAM

The power consumption of a standard CMOS SRAM during read/write operations is dependent on the address applied, the data accessed, and the type of access (read/write). The power analysis resistant SRAM structure presented in this work reduces the dependency of power consumption on data and address 10 times compared to standard SRAM at the expense of higher power and silicon area consumption. Modifications on the primitive SRAM cell, address decoding logic and write operation were made to reduce the dependency.
Path Planning and Controlled Crash Landing of a Quadcopter in case of a Rotor Failure

This paper presents a framework for controlled emergency landing of a quadcopter, experiencing a rotor failure, away from sensitive areas. A complete mathematical model capturing the dynamics of the system is presented that takes the asymmetrical aerodynamic load on the propellers into account. An equilibrium state of the system is calculated around which a linear time-invariant control strategy is developed to stabilize the system. By utilizing the proposed model, a specific configuration for a quadcopter is introduced that leads to the minimum power consumption during a yaw-rateresolved hovering after a rotor failure. Furthermore, given a 3D representation of the environment, an optimal flight trajectory towards a safe crash landing spot, while avoiding collision with obstacles, is developed using an RRT* approach. The cost function for determining the best landing spot consists of: (i) finding the safest landing spot with the largest clearance from the obstacles; and (ii) finding the most energy-efficient trajectory towards the landing spot. The performance of the proposed framework is tested via simulations.