nils-herrmann/clean_scirepeval huggingface dataset:
Anomaly Detection for Astronomical Data

Modern astronomical observatories can produce massive amount of data that are beyond the capability of the researchers to even take a glance. These scientific observations present both great opportunities and challenges for astronomers and machine learning researchers. In this project we address the problem of detecting anomalies/novelties in these large-scale astronomical data sets. Two types of anomalies, the point anomalies and the group anomalies, are considered. The point anomalies include individual anomalous objects, such as single stars or galaxies that present unique characteristics. The group anomalies include anomalous groups of objects, such as unusual clusters of the galaxies that are close together. They both have great values for astronomical studies, and our goal is to detect them automatically in un-supervised ways. For point anomalies, we adopt the subspace-based detection strategy and proposed a robust low-rank matrix decomposition algorithm for more reliable results. For group anomalies, we use hierarchical probabilistic models to capture the generative mechanism of the data, and then score the data groups using various probability measures. Experimental evaluation on both synthetic and real world data sets shows the effectiveness of the proposed methods. On a real astronomical data sets, we obtained several interesting anecdotal results. Initial inspections by the astronomers confirm the usefulness of these machine learning methods in astronomical research.
Clique percolation in random networks.

The notion of k-clique percolation in random graphs is introduced, where k is the size of the complete subgraphs whose large scale organizations are analytically and numerically investigated. For the Erdos-Rényi graph of N vertices we obtain that the percolation transition of k-cliques takes place when the probability of two vertices being connected by an edge reaches the threshold p(c) (k) = [(k - 1)N](-1/(k - 1)). At the transition point the scaling of the giant component with N is highly nontrivial and depends on k. We discuss why clique percolation is a novel and efficient approach to the identification of overlapping communities in large real networks.
The Influence of IS Affordances on Work Practices in Health Care: A Relational Coordination Approach

High-reliability healthcare organizations, such as Intensive Care Units, operate in challenging high-velocity environments regarding decision-making, uncertainty, and time constraints. Further, these organizations are multidisciplinary, professionally driven, and characterized by a strong hierarchical structure. Given such conditions, effective cross-functional communication is critical for ensuring successful work practices. We propose a theoretical model of IS affordances and Relational Coordination in the presence of functional status differences. We establish a set of propositions regarding how use patterns and types of IS affordances enable or constrain potential effects on Relational Coordination. We are currently conducting a case study of an ICU in a major University hospital, with the goal to iteratively refine our theory and propositions. We hope that this research will offer an in-depth understanding of the types of opportunities related to information systems, and the role of individual-level interpretations and actions across functional groups for these opportunities.
Graph Partitioning using Quantum Annealing on the D-Wave System

Graph partitioning (GP) applications are ubiquitous throughout mathematics, computer science, chemistry, physics, bio-science, machine learning, and complex systems. Post Moore's era supercomputing has provided us an opportunity to explore new approaches for traditional graph algorithms on quantum computing architectures. In this work, we explore graph partitioning using quantum annealing on the D-Wave 2X machine. Motivated by a recently proposed graph-based electronic structure theory applied to quantum molecular dynamics (QMD) simulations, graph partitioning is used for reducing the calculation of the density matrix into smaller subsystems rendering the calculation more computationally efficient. Unconstrained graph partitioning as community clustering based on the modularity metric can be naturally mapped into the Hamiltonian of the quantum annealer. On the other hand, when constraints are imposed for partitioning into equal parts and minimizing the number of cut edges between parts, a quadratic unconstrained binary optimization (QUBO) reformulation is required. This reformulation may employ the graph complement to fit the problem in the Chimera graph of the quantum annealer. Partitioning into 2 parts and k parts concurrently for arbitrary k are demonstrated with benchmark graphs, random graphs, and small material system density matrix based graphs. Results for graph partitioning using quantum and hybrid classical-quantum approaches are shown to be comparable to current "state of the art" methods and sometimes better.
Evolution of Memory Architecture

Computer memories continue to serve the role that they first served in the electronic discrete variable automatic computer (EDVAC) machine documented by John von Neumann, namely that of supplying instructions and operands for calculations in a timely manner. As technology has made possible significantly larger and faster machines with multiple processors, the relative distance in processor cycles of this memory has increased considerably. Microarchitectural techniques have evolved to share this memory across ever-larger systems of processors with deep cache hierarchies and have managed to hide this latency for many applications, but are proving to be expensive and energy-inefficient for newer types of problems working on massive amounts of data. New paradigms include scale-out systems distributed across hundreds and even thousands of nodes, in-memory databases that keep data in memory much longer than the duration of a single task, and near-data computation, where some of the computation is off-loaded to the location of the data to avoid wasting energy in the movement of data. This paper provides a historical perspective on the evolution of memory architecture, and suggests that the requirements of new problems and new applications are likely to fundamentally change processor and system architecture away from the currently established von Neumann model.
Fast Random Sampling of Triangular Meshes

We present a simple and fast algorithm for generating randomly distributed points on a triangle mesh with probability density specified by a two-dimensional texture. Efficiency is achieved by resampling the density texture on an adaptively subdivided version of the input mesh. This allows us to generate the samples up to 40× faster than the rejection sampling algorithm, the fastest existing alternative. We demonstrate the algorithm in two applications: fast placement of hair roots on a surface and sampling of illumination from a complex luminaire. Part of our mesh sampling procedure is a new general acceleration technique for drawing samples from a 1D discrete probability distribution whose utility extends beyond the mesh sampling problem.
Cloning Cryptographic RFID

We develop a new, custom-built hardware for emulating contactless smartcards compliant to ISO 14443. The device is based on a modern low-cost microcontroller and can support basically all relevant (cryptographic) protocols used by contactless smartcards today, e.g., those based on AES or Triple-DES. As a proof of concept, we present a full emulation of Mifare Classic cards on the basis of our highly optimized implementation of the stream cipher Crypto1. The implementation enables the creation of exact clones of such cards, including the UID. We furthermore reverse-engineered the protocol of DESFire EV1 and realize the first emulation of DESFire and DESFire EV1 cards in the literature. We practically demonstrate the capabilities of our emulator by spoofing several real-world systems, e.g., creating a contactless payment card which allows an attacker to set the stored credit balance as desired and hence make an infinite amount of payments.
CHALLENGES AND OPPORTUNITIES FOR IOT-ENABLED CYBERMANUFACTURING : SOME INITIAL FINDINGS FROM AN IOT-ENABLED MANUFACTURING TECHNOLOGY TESTBED

Internet of Things (IoT) have gained tremendous momentum and importance in recent years. Initiated from services and consumer products industries, there is a growing interest in using IoT technologies in various industries. In manufacturing, advanced or smart manufacturing and cybermanufacturing are also drawing increasing attention. Because IoT devices such as IoT sensors are usually much cheaper and smaller than the traditional sensors, there is a potential for instrumenting manufacturing systems with massive number of sensors, then big data subsequently collected from IoT sensors can be utilized to advance manufacturing. This type of IoT applications has not drawn much attention from either academic researchers or industrial practitioners. One possible reason is that the benefits of such applications have not been recognized or tested. Therefore, we built an IoT-enabled manufacturing technology testbed (MTT) to explore the potential of IoT sensors. In this work, the characteristics of a type of IoT temperature sensor were studied and mathematical models were developed to capture these characteristics and to accurately reproduce the observed behaviors. Based on the initial findings from our MTT experiments, challenges and opportunities for IoT-enabled manufacturing are discussed.
Optimal assay design for determining the in vitro sensitivity of ring stage Plasmodium falciparum to artemisinins.

Recent reports demonstrate that failure of artemisinin-based antimalarial therapies is associated with an altered response of early blood stage Plasmodium falciparum. This has led to increased interest in the use of pulse assays that mimic clinical drug exposure for analysing artemisinin sensitivity of highly synchronised ring stage parasites. We report a methodology for the reliable execution of drug pulse assays and detail a synchronisation strategy that produces well-defined tightly synchronised ring stage cultures in a convenient time-frame.
FEATURE ATTRIBUTION AS FEATURE SELECTION

Feature attribution methods identify “relevant” features as an explanation of a complex machine learning model. Several feature attribution methods have been proposed; however, only a few studies have attempted to define the “relevance” of each feature mathematically. In this study, we formalize the feature attribution problem as a feature selection problem. In our proposed formalization, there arise two possible definitions of relevance. We name the feature attribution problems based on these two relevances as Exclusive Feature Selection (EFS) and Inclusive Feature Selection (IFS). We show that several existing feature attribution methods can be interpreted as approximation algorithms for EFS and IFS. Moreover, through exhaustive experiments, we show that IFS is better suited as the formalization for the feature attribution problem than EFS.
The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes

Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources — including Na+ and Ca2+ spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations — can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.
A Survey of Evolutionary Algorithms for Clustering

This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.
Twitter Sentiment Analysis with Deep Convolutional Neural Networks

This paper describes our deep learning system for sentiment analysis of tweets. The main contribution of this work is a new model for initializing the parameter weights of the convolutional neural network, which is crucial to train an accurate model while avoiding the need to inject any additional features. Briefly, we use an unsupervised neural language model to train initial word embeddings that are further tuned by our deep learning model on a distant supervised corpus. At a final stage, the pre-trained parameters of the network are used to initialize the model. We train the latter on the supervised training data recently made available by the official system evaluation campaign on Twitter Sentiment Analysis organized by Semeval-2015. A comparison between the results of our approach and the systems participating in the challenge on the official test sets, suggests that our model could be ranked in the first two positions in both the phrase-level subtask A (among 11 teams) and on the message-level subtask B (among 40 teams). This is an important evidence on the practical value of our solution.
Shadow Detection in Single Still Images using TAM based Multi Step Algorithm

Processing of foreground images in surveillance applications usually requires that shadows have been identified and removed from the detected foreground. A novel approach to shadow detection from single still images is presented in this paper. This approach includes a pre processing step to identify the presence of shadows in the image and then A Tricolor Attenuation Model based multi step shadow detection algorithm is used to extract shadows. The tricolor attenuation model (TAM) describes the attenuation relationship between shadow and its non shadow region is derived based on image formation theory. In contrast, the method proposed here is not only designed for specific applications but can automatically detect and extract the shadows from single still images, even those with complex outdoor scenes. In addition, this method does not need any user intervention and prior knowledge. It is entirely a data-driven approach. I.  Shadow detection over the past decades covers many explicit applications such as traffic surveillance, face recognition and image segmentation. Real images with shadows can not be used for image synthesis. Shadows give geometric and semantic information in an image, including cues about the shape and the relative spatial position of objects, characteristics of surfaces and light sources. In outdoor scenes, there are mainly two light sources: direct sunlight, which can be regarded as a point light source; diffuse skylight, which can be regarded as an area light source. Shadow occurs when an object totally or partially occludes directly from the light source. Generally, shadow is divided into two parts: 1-Self shadow is a part of shadow on the main object where it is not illuminated by light. 2-Cast shadow is the dark area projected by an object on the background.  Cast shadow can be further divided into umbra and penumbra region. The umbra region is the part of a cast shadow where direct light is completely blocked and the penumbra region is the part of a cast shadow where light is partially blocked.
Claytronics: highly scalable communications, sensing, and actuation networks

We propose a demonstration of extremely scalable modular robotics algorithms developed as part of the Claytronics Project (http://www-2.cs.cmu.edu/~claytronics/), as well as a demonstration of proof-of-concept prototypes. Our effort envisions multi-million-module robot ensembles able to morph into three-dimensional scenes, eventually with sufficient fidelity so as to convince a human observer the scenes are real. Although this work is potentially revolutionary in the sense that it holds out the possibility of radically altering the relationship between computation, humans, and the physical world, many of the research questions involved are similar in flavor to more mainstream systems research, albeit larger in scale. For instance, as in sensor networks, each robot will incorporate sensing, computation, and communications components. However, unlike most sensor networks each robot will also include mechanisms for actuation and motion. Many of the key challenges in this project involve coordination and communication of sensing and actuation across such large ensembles of independent units.
Compassion Fatigue among Healthcare, Emergency and Community Service Workers: A Systematic Review

Compassion fatigue (CF) is stress resulting from exposure to a traumatized individual. CF has been described as the convergence of secondary traumatic stress (STS) and cumulative burnout (BO), a state of physical and mental exhaustion caused by a depleted ability to cope with one's everyday environment. Professionals regularly exposed to the traumatic experiences of the people they service, such as healthcare, emergency and community service workers, are particularly susceptible to developing CF. This can impact standards of patient care, relationships with colleagues, or lead to more serious mental health conditions such as posttraumatic stress disorder (PTSD), anxiety or depression. A systematic review of the effectiveness of interventions to reduce CF in healthcare, emergency and community service workers was conducted. Thirteen relevant studies were identified, the majority of which were conducted on nurses (n = 10). Three included studies focused on community service workers (social workers, disability sector workers), while no studies targeting emergency service workers were identified. Seven studies reported a significant difference post-intervention in BO (n = 4) or STS (n = 3). This review revealed that evidence of the effectiveness of CF interventions in at-risk health and social care professions is relatively recent. Therefore, we recommend more research to determine how best to protect vulnerable workers at work to prevent not only CF, but also the health and economic consequences related to the ensuing, and more disabling, physical and mental health outcomes.
Proximal Splitting Methods in Signal Processing ∗

The proximity operator of a convex function is a natural exte nsion of the notion of a projection operator onto a convex set. This tool, which plays a central role in the analysis and the numerical solution of convex opt imization problems, has recently been introduced in the arena of inverse problems an d, especially, in signal processing, where it has become increasingly important. In this paper, we review the basic properties of proximity operators which are relevant to signal processing and present optimization methods based on these operators. The e proximal splitting methods are shown to capture and extend several well-known a lgorithms in a unifying framework. Applications of proximal methods in signal r ecovery and synthesis are discussed.
Clustering Through Decision Tree Construction

Clustering aims to find the intrinsic structure of data by organizing data objects into similarity groups or clusters. It is often called unsupervised learning as no class labels denoting an a priori partition of the objects are given. This is in contrast with supervised learning (e.g., classification) for which the data objects are already labeled with known classes. Past research in clustering has produced many algorithms. However, these algorithms have some major shortcomings. In this paper, we propose a novel clustering technique, which is based on a supervised learning technique called decision tree construction. The new technique is able to overcome many of these shortcomings. The key idea is to use a decision tree to partition the data space into cluster and empty (sparse) regions at different levels of details. The technique is able to find "natural" clusters in large high dimensional spaces efficiently. It is suitable for clustering in the full dimensional space as well as in subspaces. It also provides comprehensible descriptions of clusters. Experiment results on both synthetic data and real-life data show that the technique is effective and also scales well for large high dimensional datasets.
Transfer Learning for Music Classification and Regression Tasks

In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as lowand high-level music features.
Categorizing User Sessions at Pinterest

Di erent users can use a given Internet application in many different ways. The ability to record detailed event logs of user inapplication activity allows us to discover ways in which the application is being used. This enables personalization and also leads to important insights with actionable business and product outcomes. Here we study the problem of user session categorization, where the goal is to automatically discover categories/classes of user insession behavior using event logs, and then consistently categorize each user session into the discovered classes. We develop a three stage approach which uses clustering to discover categories of sessions, then builds classi ers to classify new sessions into the discovered categories, and nally performs daily classi cation in a distributed pipeline. An important innovation of our approach is selecting a set of events as long-tail features, and replacing them with a new feature that is less sensitive to product experimentation and logging changes. This allows for robust and stable identi cation of session types even though the underlying application is constantly changing. We deploy the approach to Pinterest and demonstrate its e ectiveness. We discover insights that have consequences for product monetization, growth, and design. Our solution classi es millions of user sessions daily and leads to actionable insights.