nils-herrmann/clean_scirepeval huggingface dataset:
Deep Neural Networks with Auxiliary-Model Regulated Gating for Resilient Multi-Modal Sensor Fusion

Deep neural networks allow for fusion of high-level features from multiple modalities and have become a promising end-to-end solution for multi-modal sensor fusion. While the recently proposed gating architectures improve the conventional fusion mechanisms employed in CNNs, these models are not always resilient particularly under the presence of sensor failures. This paper shows that the existing gating architectures fail to robustly learn the fusion weights that critically gate different modalities, leading to the issue of fusion weight inconsistency. We propose a new gating architecture by incorporating an auxiliary model to regularize the main model such that the fusion weight for each sensory modality can be robustly learned. As a result, this new auxiliary-model regulated architecture and its variants outperform the existing non-gating and gating fusion architectures under both clean and corrupted sensory inputs resulted from sensor failures. The obtained performance gains are rather significant in the latter case.
Data Consistency in Transactional Storage Systems: a Centralised Approach

We introduce an interleaving operational semantics for describing the client-observable behaviour of atomic transactions on distributed key-value stores. Our semantics builds on abstract states comprising centralised, global key-value stores and partial client views. We provide operational definitions of consistency models for our key-value stores which are shown to be equivalent to the well-known declarative definitions of consistency model for execution graphs. We explore two immediate applications of our semantics: specific protocols of geo-replicated databases (e.g. COPS) and partitioned databases (e.g. Clock-SI) can be shown to be correct for a specific consistency model by embedding them in our centralised semantics; programs can be directly shown to have invariant properties such as robustness results against a weak consistency model.
Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop

We present the Twitter Job/Employment Corpus, a collection of tweets annotated by a humans-in-the-loop supervised learning framework that integrates crowdsourcing contributions and expertise on the local community and employment environment. Previous computational studies of job-related phenomena have used corpora collected from workplace social media that are hosted internally by the employers, and so lacks independence from latent job-related coercion and the broader context that an open domain, general-purpose medium such as Twitter provides. Our new corpus promises to be a benchmark for the extraction of job-related topics and advanced analysis and modeling, and can potentially benefit a wide range of research communities in the future.
Enhanced Variational Inference with Dyadic Transformation

Variational autoencoder is a powerful deep generative model with variational inference. The practice of modeling latent variables in the VAE's original formulation as normal distributions with a diagonal covariance matrix limits the flexibility to match the true posterior distribution. We propose a new transformation, dyadic transformation (DT), that can model a multivariate normal distribution. DT is a single-stage transformation with low computational requirements. We demonstrate empirically on MNIST dataset that DT enhances the posterior flexibility and attains competitive results compared to other VAE enhancements.
A Game Theoretical Error-Correction Framework for Secure Traffic-Sign Classification

We introduce a game theoretical error-correction framework to design classification algorithms that are reliable even in adversarial environments, with a specific focus on traffic-sign classification. Machine learning algorithms possess inherent vulnerabilities against maliciously crafted inputs especially at high dimensional input spaces. We seek to achieve reliable and timely performance in classification by redesigning the input space physically to significantly lower dimensions. Traffic-sign classification is an important use-case enabling the redesign of the inputs since traffic-signs have already been designed for their easy recognition by human drivers. We encode the original input samples to, e.g., strings of bits, through error-correction methods that can provide certain distance guarantees in-between any two different encoded inputs. And we model the interaction between the defense and the adversary as a game. Then, we analyze the underlying game using the concept of hierarchical equilibrium, where the defense strategies are designed by taking into account the best possible attack against them. At large scale, for computational simplicity, we provide an approximate solution, where we transform the problem into an efficient linear program with substantially small size compared to the original size of the entire input space. Finally, we examine the performance of the proposed scheme over different traffic-sign classification scenarios.
Predicting quantum advantage by quantum walk with convolutional neural networks

Quantum walks are at the heart of modern quantum technologies. They allow to deal with quantum transport phenomena and are an advanced tool for constructing novel quantum algorithms. Quantum walks on graphs are fundamentally different from classical random walks analogs, in particular, they walk faster than classical ones on certain graphs, enabling in these cases quantum algorithmic applications and quantum-enhanced energy transfer. However, little is known about the possible advantages on arbitrary graphs not having explicit symmetries. For these graphs one would need to perform simulations of classical and quantum walk dynamics to check if the speedup occurs, which could take a long computational time. Here we present a new approach for the solution of the quantum speedup problem, which is based on a machine learning algorithm that predicts the quantum advantage by just ‘looking’ at a graph. The convolutional neural network, which we designed specifically to learn from graphs, observes simulated examples and learns complex features of graphs that lead to a quantum advantage, allowing to identify graphs that exhibit quantum advantage without performing any quantum walk or random walk simulations. The performance of our approach is evaluated for line and random graphs, where classification was always better than random guess even for the most challenging cases. Our findings pave the way to an automated elaboration of novel large-scale quantum circuits utilizing quantum walk based algorithms, and to simulating high-efficiency energy transfer in biophotonics and material science.
Privacy-preserving Q-Learning with Functional Noise in Continuous State Spaces

We consider differentially private algorithms for reinforcement learning in continuous spaces, such that neighboring reward functions are indistinguishable. This protects the reward information from being exploited by methods such as inverse reinforcement learning. Existing studies that guarantee differential privacy are not extendable to infinite state spaces, as the noise level to ensure privacy will scale accordingly to infinity. Our aim is to protect the value function approximator, without regard to the number of states queried to the function. It is achieved by adding functional noise to the value function iteratively in the training. We show rigorous privacy guarantees by a series of analyses on the kernel of the noise space, the probabilistic bound of such noise samples, and the composition over the iterations. We gain insight into the utility analysis by proving the algorithm's approximate optimality when the state space is discrete. Experiments corroborate our theoretical findings and show improvement over existing approaches.
Evaluating Bregman Divergences for Probability Learning from Crowd

The crowdsourcing scenarios are a good example of having a probability distribution over some categories showing what the people in a global perspective thinks. Learn a predictive model of this probability distribution can be of much more valuable that learn only a discriminative model that gives the most likely category of the data. Here we present differents models that adapts having probability distribution as target to train a machine learning model. We focus on the Bregman divergences framework to used as objective function to minimize. The results show that special care must be taken when build a objective function and consider a equal optimization on neural network in Keras framework.
Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference

Computations for the softmax function are significantly expensive when the number of output classes is large. In this paper, we present a novel softmax inference speedup method, Doubly Sparse Softmax (DS-Softmax), that leverages sparse mixture of sparse experts to efficiently retrieve top-k classes. Different from most existing methods that require and approximate a fixed softmax, our method is learning-based and can adapt softmax weights for a better approximation. In particular, our method learns a two-level hierarchy which divides entire output class space into several partially overlapping experts. Each expert is sparse and only contains a subset of output classes. To find top-k classes, a sparse mixture enables us to find the most probable expert quickly, and the sparse expert enables us to search within a small-scale softmax. We empirically conduct evaluation on several real-world tasks (including neural machine translation, language modeling and image classification) and demonstrate that significant computation reductions can be achieved without loss of performance.
End-to-end Learning for Early Classification of Time Series

Classification of time series is a topical issue in machine learning. While accuracy stands for the most important evaluation criterion, some applications require decisions to be made as early as possible. Optimization should then target a compromise between earliness, i.e., a capacity of providing a decision early in the sequence, and accuracy. In this work, we propose a generic, end-to-end trainable framework for early classification of time series. This framework embeds a learnable decision mechanism that can be plugged into a wide range of already existing models. We present results obtained with deep neural networks on a diverse set of time series classification problems. Our approach compares well to state-of-the-art competitors while being easily adaptable by any existing neural network topology that evaluates a hidden state at each time step.
Online Pandora's Boxes and Bandits

We consider online variations of the Pandora’s box problem (Weitzman 1979), a standard model for understanding issues related to the cost of acquiring information for decision-making. Our problem generalizes both the classic Pandora’s box problem and the prophet inequality framework. Boxes are presented online, each with a random value and cost drawn jointly from some known distribution. Pandora chooses online whether to open each box given its cost, and then chooses irrevocably whether to keep the revealed prize or pass on it. We aim for approximation algorithms against adversaries that can choose the largest prize over any opened box, and use optimal offline policies to decide which boxes to open (without knowledge of the value inside)1. We consider variations where Pandora can collect multiple prizes subject to feasibility constraints, such as cardinality, matroid, or knapsack constraints. We also consider variations related to classic multi-armed bandit problems from reinforcement learning. Our results use a reduction-based framework where we separate the issues of the cost of acquiring information from the online decision process of which prizes to keep. Our work shows that in many scenarios, Pandora can achieve a good approximation to the best possible performance.
Learning Position Evaluation Functions Used in Monte Carlo Softmax Search

This paper makes two proposals for Monte Carlo Softmax Search, which is a recently proposed method that is classified as a selective search like the Monte Carlo Tree Search. The first proposal separately defines the node-selection and backup policies to allow researchers to freely design a node-selection policy based on their searching strategies and confirms the principal variation produced by the Monte Carlo Softmax Search to that produced by a minimax search. The second proposal modifies commonly used learning methods for positional evaluation functions. In our new proposals, evaluation functions are learned by Monte Carlo sampling, which is performed with the backup policy in the search tree produced by Monte Carlo Softmax Search. The learning methods under consideration include supervised learning, reinforcement learning, regression learning, and search bootstrapping. Our sampling-based learning not only uses current positions and principal variations but also the internal nodes and important variations of a search tree. This step reduces the number of games necessary for learning. New learning rules are derived for sampling-based learning based on the Monte Carlo Softmax Search and combinations of the modified learning methods are also proposed in this paper.
Compositionality for Recursive Neural Networks

Modelling compositionality has been a longstanding area of research in the field of vector space semantics. The categorical approach to compositionality maps grammar onto vector spaces in a principled way, but comes under fire for requiring the formation of very high-dimensional matrices and tensors, and therefore being computationally infeasible. In this paper I show how a linear simplification of recursive neural tensor network models can be mapped directly onto the categorical approach, giving a way of computing the required matrices and tensors. This mapping suggests a number of lines of research for both categorical compositional vector space models of meaning and for recursive neural network models of compositionality.
Conditional Densities and Simulations of Inhomogeneous Poisson Point Processes: The R package "IPPP"

A number of numeric approaches to simulate Poisson point processes with arbitrary event rates are presented and implemented for R. They include the simulation of the number of points and their location as well as the determination of conditional probability densities and random numbers in cases where additional information (e.g. location of points or number of points occurring) is available upfront.
Tensorized Embedding Layers for Efficient Model Compression

The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing. However, when the vocabulary is large (e.g., 800k unique words in the One-Billion-Word dataset), the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting. We introduce a novel way of parametrizing embedding layers based on the Tensor Train (TT) decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance. Importantly, our method does not take the pre-trained model and compress its weights but rather supplants the standard embedding layers with their TT-based counterparts. The resulting model is then trained end-to-end, however, it can capitalize on larger batches due to the reduced memory requirements. We evaluate our method on a wide range of benchmarks in sentiment analysis, neural machine translation, and language modeling, and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.
Blurred Images Lead to Bad Local Minima

High Initial visual Acuity (HIA) in newborns treated for cataracts, it is argued by Vogelsang et al. (2018), may cause impairments in configural face analysis. This HIA hypothesis is contrary to the standard explanation by a critical period for learning face processing. The hypothesis is supported by computational experiments with an artificial neural network. In our work we argue that the computational methodology used to evaluate the HIA hypothesis is flawed. It essentially shows that when a classifier is tested with images from different resolutions, the classifier benefits from seeing images from different resolution during its training. We therefore offer a better-fitting methodology; employing the modified methodology the HIA hypothesis does not hold in simulations using the same artificial neural network model, and the same data. Vogelsang et al. (2018) also show that initial exposure to low resolution images gives rise to larger receptive fields. Our last set of experiments tests the hypothesis that this might be the underlying reason for the observed impairments. Once again, we are unable to find an advantage to training with images of low initial acuity. We therefore conclude that simulations with artificial networks do not support the hypothesis that High Initial visual Acuity is detrimental.
Automated Analysis, Reporting, and Archiving for Robotic Nondestructive Assay of Holdup Deposits

To decommission deactivated gaseous diffusion enrichment facilities, miles of contaminated pipe must be measured. The current method requires thousands of manual measurements, repeated manual data transcription, and months of manual analysis. The Pipe Crawling Activity Measurement System (PCAMS), developed by Carnegie Mellon University and in commissioning for use at the DOE Portsmouth Gaseous Diffusion Enrichment Facility, uses a robot to measure Uranium-235 from inside pipes and automatically log the data. Radiation measurements, as well as imagery, geometric modeling, and precise measurement positioning data are digitally transferred to the PCAMS server. On the server, data can be automatically processed in minutes and summarized for analyst review. Measurement reports are auto-generated with the push of a button. A database specially-configured to hold heterogeneous data such as spectra, images, and robot trajectories serves as archive. This paper outlines the features and design of the PCAMS Post-Processing Software, currently in commissioning for use at the Portsmouth Gaseous Diffusion Enrichment Facility. The analysis process, the analyst interface to the system, and the content of auto-generated reports are each described. Example pipe-interior geometric surface models, illustration of how key report features apply in operational runs, and user feedback are discussed.
Training Future Engineers to Be Ghostbusters: Hunting for the Spectral Environmental Radioactivity

Although environmental radioactivity is all around us, the collective public imagination often associates a negative feeling to this natural phenomenon. To increase the familiarity with this phenomenon we have designed, implemented, and tested an interdisciplinary educational activity for pre-collegiate students in which nuclear engineering and computer science are ancillary to the comprehension of basic physics concepts. Teaching and training experiences are performed by using a 4” × 4” NaI(Tl) detector for in-situ and laboratory γ-ray spectroscopy measurements. Students are asked to directly assemble the experimental setup and to manage the data-taking with a dedicated Android app, which exploits a client-server system that is based on the Bluetooth communication protocol. The acquired γ-ray spectra and the experimental results are analyzed using a multiple-platform software environment and they are finally shared on an open access Web-GIS service. These all-round activities combining theoretical background, hands-on setup operations, data analysis, and critical synthesis of the results were demonstrated to be effective in increasing students’ awareness in quantitatively investigating environmental radioactivity. Supporting information to the basic physics concepts provided in this article can be found at http://www.fe.infn.it/radioactivity/educational.
Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge

In this paper, we studied extensively on different deep learning based methods to detect melanoma and skin lesion cancers. Melanoma, a form of malignant skin cancer is very threatening to health. Proper diagnosis of melanoma at an earlier stage is crucial for the success rate of complete cure. Dermoscopic images with Benign and malignant forms of skin cancer can be analyzed by computer vision system to streamline the process of skin cancer detection. In this study, we experimented with various neural networks which employ recent deep learning based models like PNASNet-5-Large, InceptionResNetV2, SENet154, InceptionV4. Dermoscopic images are properly processed and augmented before feeding them into the network. We tested our methods on International Skin Imaging Collaboration (ISIC) 2018 challenge dataset. Our system has achieved best validation score of 0.76 for PNASNet-5-Large model. Further improvement and optimization of the proposed methods with a bigger training dataset and carefully chosen hyper-parameter could improve the performances. The code available for download at https://github.com/miltonbd/ISIC_2018_classification.
Open Research Knowledge Graph: Towards Machine Actionability in Scholarly Communication

Despite improved findability of and access to scientific knowledge in recent decades, scholarly communication continues to be document-based. Scientific knowledge remains locked in representations that are inadequate for machine processing. In this article, we present initial steps towards next generation digital libraries and infrastructures that acquire, curate, publish and process scholarly knowledge semantically, in machine readable form leveraging knowledge graphs. The primary contribution of this work is to present and discuss early developments of a system designed to crowdsource machine readable descriptions of research contributions published in scholarly articles and a knowledge graph infrastructure for description storage and access. We report on the results of a first experimental evaluation of the concept and its implementation with the participants of a recent international conference. The results suggest that users find such a system useful, and the possibilities it could enable intriguing.