nils-herrmann/clean_scirepeval huggingface dataset:
PCPs and the Hardness of Generating Private Synthetic Data

Assuming the existence of one-way functions, we show that there is no polynomial-time, differentially private algorithm A that takes a database D ∈ ({0, 1}d)n and outputs a “synthetic database” ̂ D all of whose two-way marginals are approximately equal to those of D. (A two-way marginal is the fraction of database rows x ∈ {0, 1}d with a given pair of values in a given pair of columns). This answers a question of Barak et al. (PODS ‘07), who gave an algorithm running in time poly(n, 2). Our proof combines a construction of hard-to-sanitize databases based on digital signatures (by Dwork et al., STOC ‘09) with encodings based on probabilistically checkable proofs. We also present both negative and positive results for generating “relaxed” synthetic data, where the fraction of rows in D satisfying a predicate c are estimated by applying c to each row of ̂ D and aggregating the results in some way.
High speed, Low power Approximate Multipliers

Adders and multipliers form the significant components in all kind of digital applications and has always been a thought-provoking topic for the researchers. Multiplication process will initially find out the set of partial products and then groups the partial products according to their respective bit positions and takes the summation. Among the phases of multiplication, partial product reduction tree consumes the maximum power and area as it is done using several adders. In this paper, a High Speed, Low Power Approximate Multiplier is proposed in which a new 4–2 compressor is being projected and is made use while performing vector merge addition after transforming the partial product terms to lead varying probability terms. Simulation results shows that the proposed Approximate Multiplier achieved 3.19% less Area, 53.74% less Delay, 3.79% less Power compared to Approximate Multiplier [6] and 31% less Area, 81.35% less Delay and 59.83% less Power compared to that of an Exact Multiplier. When we make use of Approximate Multipliers, there are likelihoods of error occurring in some cases. Hence approximate multipliers are suggested only for Error Resilient applications. Video compression, Digital Image processing and Test Data compression can be categorized as such applications. The proposed approximate multiplier takes less Power, Area and Time and also can be paralleled to the existing approximate multiplier algorithms. Hence for Error resilient application, it is always sagacious to use less power consuming constituents since power is a crucial one to save in the current scenario.
A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks

We present the soft exponential activation function for artificial neural networks that continuously interpolates between logarithmic, linear, and exponential functions. This activation function is simple, differentiable, and parameterized so that it can be trained as the rest of the network is trained. We hypothesize that soft exponential has the potential to improve neural network learning, as it can exactly calculate many natural operations that typical neural networks can only approximate, including addition, multiplication, inner product, distance, and sinusoids.
RTL Hardware IP Protection Using Key-Based Control and Data Flow Obfuscation

Recent trends of hardware intellectual property (IP) piracy and reverse engineering pose major business and security concerns to an IP-based system-on-chip (SoC) design flow. In this paper, we propose a Register Transfer  The basic idea is to transform the RTL core into control and data flow graph (CDFG) and then integrate a well-obfuscated finite state machine (FSM) of special structure, referred as “Mode-Control FSM”, into the CDFG in a manner that normal functional behavior is enabled only after application of a specific input sequence. We provide formal analysis of the effectiveness of the proposed approach and present a simple metric to quantify the level of obfuscation. We also present an integrated design flow that implements the proposed obfuscation at low computational overhead. Simulation results for two open-source IP cores show that high levels of security is achievable at nominal area and power overheads under delay constraint.
The information bottleneck method

We define the relevant information in a signal x ∈ X as being the information that this signal provides about another signal y ∈ Y . Examples include the information that face images provide about the names of the people portrayed, or the information that speech sounds provide about the words spoken. Understanding the signal x requires more than just predicting y, it also requires specifying which features of X play a role in the prediction. We formalize this problem as that of finding a short code for X that preserves the maximum information about Y . That is, we squeeze the information that X provides about Y through a ‘bottleneck’ formed by a limited set of codewords X̃. This constrained optimization problem can be seen as a generalization of rate distortion theory in which the distortion measure d(x, x̃) emerges from the joint statistics of X and Y . This approach yields an exact set of self consistent equations for the coding rules X → X̃ and X̃ → Y . Solutions to these equations can be found by a convergent re–estimation method that generalizes the Blahut–Arimoto algorithm. Our variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere.
2D-3D face recognition via Restricted Boltzmann Machines

This paper proposes a new scheme for the 2D-3D face recognition problem. Our proposed framework mainly consists of Restricted Boltzmann Machines (RBMs) and a correlation learning model. In the framework, a single-layer network based on RBMs is adopted to extract latent features over two different modalities. Furthermore, the latent hidden layer features of different models are projected to formulate a shared space based on correlation learning. Then several different correlation learning schemes are evaluated against the proposed scheme. We evaluate the advocated approach on a popular face dataset-FRGCV2.0. Experimental results demonstrate that the latent features extracted using RBMs are effective in improving the performance of correlation mapping for 2D-3D face recognition.
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro

The main contribution of this paper is a simple semisupervised pipeline that only uses the original training set without collecting extra data. It is challenging in 1) how to obtain more training data only from the training set and 2) how to use the newly generated data. In this work, the generative adversarial network (GAN) is used to generate unlabeled samples. We propose the label smoothing regularization for outliers (LSRO). This method assigns a uniform label distribution to the unlabeled images, which regularizes the supervised model and improves the baseline. We verify the proposed method on a practical problem: person re-identification (re-ID). This task aims to retrieve a query person from other cameras. We adopt the deep convolutional generative adversarial network (DCGAN) for sample generation, and a baseline convolutional neural network (CNN) for representation learning. Experiments show that adding the GAN-generated data effectively improves the discriminative ability of learned CNN embeddings. On three large-scale datasets, Market- 1501, CUHK03 and DukeMTMC-reID, we obtain +4.37%, +1.6% and +2.46% improvement in rank-1 precision over the baseline CNN, respectively. We additionally apply the proposed method to fine-grained bird recognition and achieve a +0.6% improvement over a strong baseline. The code is available at https://github.com/layumi/ Person-reID_GAN.
Enriching textbooks with images

Textbooks have a direct bearing on the quality of education imparted to the students. Therefore, it is of paramount importance that the educational content of textbooks should provide rich learning experience to the students. Recent studies on understanding learning behavior suggest that the incorporation of digital visual material can greatly enhance learning. However, textbooks used in many developing regions are largely text-oriented and lack good visual material. We propose techniques for finding images from the web that are most relevant for augmenting a section of the textbook, while respecting the constraint that the same image is not repeated in different sections of the same chapter. We devise a rigorous formulation of the image assignment problem and present a polynomial time algorithm for solving the problem optimally. We also present two image mining algorithms that utilize orthogonal signals and hence obtain different sets of relevant images. Finally, we provide an ensembling algorithm for combining the assignments. To empirically evaluate our techniques, we use a corpus of high school textbooks in use in India. Our user study utilizing the Amazon Mechanical Turk platform indicates that the proposed techniques are able to obtain images that can help increase the understanding of the textbook material.
Learning to Log: Helping Developers Make Informed Logging Decisions

Logging is a common programming practice of practical importance to collect system runtime information for postmortem analysis. Strategic logging placement is desired to cover necessary runtime information without incurring unintended consequences (e.g., performance overhead, trivial logs). However, in current practice, there is a lack of rigorous specifications for developers to govern their logging behaviours. Logging has become an important yet tough decision which mostly depends on the domain knowledge of developers. To reduce the effort on making logging decisions, in this paper, we propose a "learning to log" framework, which aims to provide informative guidance on logging during development. As a proof of concept, we provide the design and implementation of a logging suggestion tool, LogAdvisor, which automatically learns the common logging practices on where to log from existing logging instances and further leverages them for actionable suggestions to developers. Specifically, we identify the important factors for determining where to log and extract them as structural features, textual features, and syntactic features. Then, by applying machine learning techniques (e.g., feature selection and classifier learning) and noise handling techniques, we achieve high accuracy of logging suggestions. We evaluate LogAdvisor on two industrial software systems from Microsoft and two open-source software systems from GitHub (totally 19.1M LOC and 100.6K logging statements). The encouraging experimental results, as well as a user study, demonstrate the feasibility and effectiveness of our logging suggestion tool. We believe our work can serve as an important first step towards the goal of "learning to log".
Diagnosing the root-causes of failures from cluster log files

System event logs are often the primary source of information for diagnosing (and predicting) the causes of failures for cluster systems. Due to interactions among the system hardware and software components, the system event logs for large cluster systems are comprised of streams of interleaved events, and only a small fraction of the events over a small time span are relevant to the diagnosis of a given failure. Furthermore, the process of troubleshooting the causes of failures is largely manual and ad-hoc. In this paper, we present a systematic methodology for reconstructing event order and establishing correlations among events which indicate the root-causes of a given failure from very large syslogs. We developed a diagnostics tool, FDiag, to extract the log entries as structured message templates and uses statistical correlation analysis to establish probable cause and effect relationships for the fault being analyzed. We applied FDiag to analyze failures due to br eakdowns in interactions between the Lustre file system and its clients on the Ranger supercomputer at the Texas Advanced Computing Center (TACC). The results are positive. FDiag is able to identify the dates and the time periods that contain the significant events which eventually led to the occurrence of compute node soft lockups.
Structuring Space with Image Schemata: Wayfinding in Airports as a Case Study

Wayfinding is a basic activity people do throughout their entire lives as they navigate from one place to another. In order to create different spaces in such a way that they facilit ate people’s wayfinding it is necessary to integrate principles of human spatial cognition into the design process. This paper presents a methodology to structure space based on experiental patterns, called image schemata. It integrates cognitive and engineering aspects in three steps: (1) interviewing people about their spatial experiences as they perform a wayfinding task in the application space, (2) extracting the image schemata from these interviews and formulating a sequence of subtasks, and (3) structuring the application space (i.e., the wayfinding task) with the extracted image schemata. We use wayfinding in airports as a case study to demonstrate the methodology. Our observations show that most often image schemata are correlated with other image schemata in the form of image-schematic blocks and rarely occur in isolation. Such image-schematic blocks serve as a knowledge-representation scheme for wayfinding tasks.
Blue Screen Matting

A classical problem of imaging—the matting problem—is separation of a non-rectangular foreground image from a (usually) rectangular background image—for example, in a film frame, extraction of an actor from a background scene to allow substitu tion of a different background. Of the several attacks on this diff cult and persistent problem, we discuss here only the special ca of separating a desired foreground image from a background of constant, or almost constant, backing color. This backing color has often been blue, so the problem, and its solution, have bee called blue screen matting . However, other backing colors, such as yellow or (increasingly) green, have also been used, so we o ten generalize to constant color matting . The mathematics of constant color matting is presented and proven to be unsolvable as generally practiced. This, of course, flies in the face of the fact that the technique is commonly used in film and video, so we demonstrate constraints on the general problem that lead to so tions, or at least significantly prune the search space of solution We shall also demonstrate that an algorithmic solution is possib by allowing the foreground object to be shot against two constant backing colors—in fact, against two completely arbitrary backin so long as they differ everywhere.
Automated Diagnosis of Glaucoma Using Digital Fundus Images

Glaucoma is a disease of the optic nerve caused by the increase in the intraocular pressure of the eye. Glaucoma mainly affects the optic disc by increasing the cup size. It can lead to the blindness if it is not detected and treated in proper time. The detection of glaucoma through Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) is very expensive. This paper presents a novel method for glaucoma detection using digital fundus images. Digital image processing techniques, such as preprocessing, morphological operations and thresholding, are widely used for the automatic detection of optic disc, blood vessels and computation of the features. We have extracted features such as cup to disc (c/d) ratio, ratio of the distance between optic disc center and optic nerve head to diameter of the optic disc, and the ratio of blood vessels area in inferior-superior side to area of blood vessel in the nasal-temporal side. These features are validated by classifying the normal and glaucoma images using neural network classifier. The results presented in this paper indicate that the features are clinically significant in the detection of glaucoma. Our system is able to classify the glaucoma automatically with a sensitivity and specificity of 100% and 80% respectively.
Measuring semantic similarity using wordnet-based context vectors

Semantic relatedness between words or concepts is a fundamental problem in many applications of computational linguistics and artificial intelligence. In this paper, a new measure based on the semantic ontology database WordNet is proposed which combines gloss information of concepts with semantic relationships, and organizes concepts as high- dimensional vectors. Other relatedness measures are compared and an experimental evaluation against several benchmark sets of human similarity ratings is presented. The context vector measure is shown to have one of the best performances.
Single active element PID controllers

This paper presents a detailed overview of the circuits allowing the realization of PID controllers with single active element. We provide analysis of the circuits using the operational amplifier (OpAmp) and operational transconductance amplifiers (OTA). Based on these analyses, we present an alternative structure using the second generation current conveyer CCII. Compared to the classical solutions, presented controller exhibits improved features such as symmetrical transfers of feedback and reference paths. The paper is completed by the design equations and the example of design.
Studying Evolution with Self-Replicating Computer Programs

A critical discussion is presented on the use of self-replicating program systems as tools for the formulation of generalised theories of evolution. Results generated by such systems must be treated with caution, but, if used properly, they can ooer us unprecedented opportunities for empirical , comparative studies. A new system called Cosmos is introduced, which is based upon Ray's Tierra 15]. The major diierence between Cosmos and previous systems is that individual self-replicating programs in Cosmos are modelled (in a very simpliied fashion) on cellular organisms. Previous systems have generally used simpler self-replicators. The hope is that Cosmos may be better able to address questions concerning the sudden emergence of complex multicellular biological organisms during the Cambrian explosion. Results of initial exploratory runs are presented, which are somewhat diierent to those of similar runs on Tierra. These diierences were expected , and indicate the sensitivity of such systems to the precise details of the language in which the self-replicating programs are written. With the strengths and weaknesses of the methodology in mind, some directions for future research with Cosmos are discussed. Within the last decade, computers have become powerful and aaordable enough to enable a number of research groups to study the evolution of life in a new way. Rather than following the traditional approach of trying to capture properties of whole populations in mathematical models, the new approach models a large number of individual self-replicating entities which are competing against each other for resources required for replication. This is achieved by creating a computer which can run a large number of self-replicating programs in parallel 1. 1 In practice, a virtual computer is created (i.e. implemented in software), with parallelism simulated by time-slicing between Tom Ray pioneered this approach with his Tierra system 15, 16]. Since then, a number of other systems have also been developed, including Avida, developed by Chris Adami and Titus Brown 2], Computer Zoo, written by Jakob Skipper 18], and John Koza's system of self-replicating LISP-like programs 8]. Using such a methodology to study evolutionary systems is attractive for a number of reasons. For example, as the self-replicators are being modelled individually rather than as populations, the simulation respects the fact that a gene does not work in isolation. Rather, it is part of a large ensemble of genes which must all work together with some degree of cooperation in order for the individual organism …
Robust odometry estimation for RGB-D cameras

The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and outliers in the image data, we propose to use a robust error function that reduces the influence of large residuals. Furthermore, our formulation allows for the inclusion of a motion model which can be based on prior knowledge, temporal filtering, or additional sensors like an IMU. Our method is attractive for robots with limited computational resources as it runs in real-time on a single CPU core and has a small, constant memory footprint. In an extensive set of experiments carried out both on a benchmark dataset and synthetic data, we demonstrate that our approach is more accurate and robust than previous methods. We provide our software under an open source license.
Hierarchical Trajectory Planning of an Autonomous Car Based on the Integration of a Sampling and an Optimization Method

This paper presents a hierarchical trajectory planning based on the integration of a sampling and an optimization method for urban autonomous driving. To manage a complex driving environment, the upper behavioral trajectory planner searches the macro-scale trajectory to determine the behavior of an autonomous car by using environment models, such as traffic control device and objects. This planner infers reasonable behavior and provides it to the motion trajectory planner. For planning the behavioral trajectory, the sampling-based approach is used due to its advantage of a free-form cost function for discrete models of the driving environments and simplification of the searching area. The lower motion trajectory planner determines the micro-scale trajectory based on the results of the upper trajectory planning with the environment model. The lower planner strongly considers vehicle dynamics within the planned behavior of the behavioral trajectory. Therefore, the planning space of the lower planner can be limited, allowing for improvement of the efficiency of the numerical optimization of the lower planner to find the best trajectory. For the motion trajectory planning, the numerical optimization is applied due to its advantages of a mathematical model for the continuous elements of the driving environments and low computation to converge minima in the convex function. The proposed algorithms of the sampling-based behavioral and optimization-based motion trajectory were evaluated through experiments in various scenarios of an urban area.
16-Way Radial Divider/Combiner for Solid State Power Amplifiers in the K Band

This paper presents the design and measurement results of a 16-way radial power divider/combiner for solid state power amplifiers in the K band using an original assembly technique of the combined ways. The design is based on a radial non-resonant cavity connected to 8 peripheral rectangular waveguides with two waveguide-to-microstrip transitions inserted in each peripheral rectangular waveguide. Each combined way includes on the same tray the input and output waveguide-to-microstrip transitions and the MMIC amplifier location. The trays can be individually tested before mounting in the 16-way divider/combiner. The low insertion loss of the 16-way power divider/combiner leads to an estimated combining efficiency better than 86.5% from 16.5 GHz to 21 GHz with a maximum value about 88% from 17.2 to 19.2 GHz.