Reviewerly/exHarmony huggingface dataset:
Simulating Periodic Systems on a Quantum Computer Using Molecular Orbitals

The variational quantum eigensolver (VQE) is one of the most appealing quantum algorithms to simulate electronic structure properties of molecules on near-term noisy intermediate-scale quantum devices. In this work, we generalize the VQE algorithm for simulating periodic systems. However, the numerical study of a one-dimensional (1D) infinite hydrogen chain using existing VQE algorithms shows a remarkable deviation of the ground-state energy with respect to the exact full configuration interaction (FCI) result. Here, we present two schemes to improve the accuracy of quantum simulations for periodic systems. The first one is a modified VQE algorithm, which introduces a unitary transformation of Hartree–Fock orbitals to avoid the complex wave function. The second one is combining VQE with the quantum subspace expansion approach (VQE/QSE). Numerical benchmark calculations demonstrate that both of the two schemes provide an accurate description of the potential energy curve of the 1D hydrogen chain. In addition, excited states computed with the VQE/QSE approach also agree very well with FCI results.
Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks

Generating realistic 3D faces is of high importance for computer graphics and computer vision applications. Generally, research on 3D face generation revolves around linear statistical models of the facial surface. Nevertheless, these models cannot represent faithfully either the facial texture or the normals of the face, which are very crucial for photo-realistic face synthesis. Recently, it was demonstrated that Generative Adversarial Networks (GANs) can be used for generating high-quality textures of faces. Nevertheless, the generation process either omits the geometry and normals, or independent processes are used to produce 3D shape information. In this paper, we present the first methodology that generates high-quality texture, shape, and normals jointly, which can be used for photo-realistic synthesis. To do so, we propose a novel GAN that can generate data from different modalities while exploiting their correlations. Furthermore, we demonstrate how we can condition the generation on the expression and create faces with various facial expressions. The qualitative results shown in this paper are compressed due to size limitations, full-resolution results and the accompanying video can be found in the supplementary documents. The code and models are available at the project page: https://github.com/barisgecer/TBGAN.
Accountability in a Permissioned Blockchain: Formal Analysis of Hyperledger Fabric

While accountability is a well-known concept in distributed systems and cryptography, in the literature on blockchains (and, more generally, distributed ledgers) the formal treatment of accountability has been a blind spot: there does not exist a formalization let alone a formal proof of accountability for any blockchain yet. Therefore, in this work we put forward and propose a formal treatment of accountability in this domain. Our goal is to formally state and prove that if in a run of a blockchain a central security property, such as consistency, is not satisfied, then misbehaving parties can be identified and held accountable. Accountability is particularly useful for permissioned blockchains where all parties know each other, and hence, accountability incentivizes all parties to behave honestly. We exemplify our approach for one of the most prominent permissioned blockchains: Hyperledger Fabric in its most common instantiation.
Directional convergence and alignment in deep learning

In this paper, we show that although the minimizers of cross-entropy and related classification losses are off at infinity, network weights learned by gradient flow converge in direction, with an immediate corollary that network predictions, training errors, and the margin distribution also converge. This proof holds for deep homogeneous networks -- a broad class of networks allowing for ReLU, max-pooling, linear, and convolutional layers -- and we additionally provide empirical support not just close to the theory (e.g., the AlexNet), but also on non-homogeneous networks (e.g., the DenseNet). If the network further has locally Lipschitz gradients, we show that these gradients also converge in direction, and asymptotically align with the gradient flow path, with consequences on margin maximization, convergence of saliency maps, and a few other settings. Our analysis complements and is distinct from the well-known neural tangent and mean-field theories, and in particular makes no requirements on network width and initialization, instead merely requiring perfect classification accuracy. The proof proceeds by developing a theory of unbounded nonsmooth Kurdyka-Łojasiewicz inequalities for functions definable in an o-minimal structure, and is also applicable outside deep learning.
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation

Deep learning algorithms are well-known to have a propensity for fitting the training data very well and often fit even outliers and mislabeled data points. Such fitting requires memorization of training data labels, a phenomenon that has attracted significant research interest but has not been given a compelling explanation so far. A recent work of Feldman (2019) proposes a theoretical explanation for this phenomenon based on a combination of two insights. First, natural image and data distributions are (informally) known to be long-tailed, that is have a significant fraction of rare and atypical examples. Second, in a simple theoretical model such memorization is necessary for achieving close-to-optimal generalization error when the data distribution is long-tailed. However, no direct empirical evidence for this explanation or even an approach for obtaining such evidence were given. In this work we design experiments to test the key ideas in this theory. The experiments require estimation of the influence of each training example on the accuracy at each test example as well as memorization values of training examples. Estimating these quantities directly is computationally prohibitive but we show that closely-related subsampled influence and memorization values can be estimated much more efficiently. Our experiments demonstrate the significant benefits of memorization for generalization on several standard benchmarks. They also provide quantitative and visually compelling evidence for the theory put forth in (Feldman, 2019).
Where to Prune: Using LSTM to Guide Data-Dependent Soft Pruning

While convolutional neural network (CNN) has achieved overwhelming success in various vision tasks, its heavy computational cost and storage overhead limit the practical use on mobile or embedded devices. Recently, compressing CNN models has attracted considerable attention, where pruning CNN filters, also known as the channel pruning, has generated great research popularity due to its high compression rate. In this paper, a new channel pruning framework is proposed, which can significantly reduce the computational complexity while maintaining sufficient model accuracy. Unlike most existing approaches that seek to-be-pruned filters layer by layer, we argue that choosing appropriate layers for pruning is more crucial, which can result in more complexity reduction but less performance drop. To this end, we utilize a long short-term memory (LSTM) to learn the hierarchical characteristics of a network and generate a global network pruning scheme. On top of it, we propose a data-dependent soft pruning method, dubbed Squeeze-Excitation-Pruning (SEP), which does not physically prune any filters but selectively excludes some kernels involved in calculating forward and backward propagations depending on the pruning scheme. Compared with the hard pruning, our soft pruning can better retain the capacity and knowledge of the baseline model. Experimental results demonstrate that our approach still achieves comparable accuracy even when reducing 70.1% Floating-point operation per second (FLOPs) for VGG and 47.5% for Resnet-56.
Why Normalizing Flows Fail to Detect Out-of-Distribution Data

Detecting out-of-distribution (OOD) data is crucial for robust machine learning systems. Normalizing flows are flexible deep generative models that often surprisingly fail to distinguish between in- and out-of-distribution data: a flow trained on pictures of clothing assigns higher likelihood to handwritten digits. We investigate why normalizing flows perform poorly for OOD detection. We demonstrate that flows learn local pixel correlations and generic image-to-latent-space transformations which are not specific to the target image dataset. We show that by modifying the architecture of flow coupling layers we can bias the flow towards learning the semantic structure of the target data, improving OOD detection. Our investigation reveals that properties that enable flows to generate high-fidelity images can have a detrimental effect on OOD detection.
Neuro-Fuzzy Based Handover Authentication Protocol for Ultra Dense 5G Networks

Smart grids improve the management of various power network components as well as the monitoring of power flows. Their deployments require very fast communication channels, which can be facilitated by 5G networks. However, 5G's ultra-densification boost system capacity and spectrum efficiency but introduce vulnerabilities due to the incorporation of many wireless network technologies and devices. The conventional 5G handover authentication protocols have security challenges that expose them to attacks such as message replay. As such, many authentication protocols have been proposed to secure smart grid data transmission. However, these protocols are unreliable in attacks detection and prevention, energy inefficient, have high latencies, heavy communication as well as computational overheads. In this paper, a 5G key management and handover protocol is developed to address some of these security and performance issues. The simulation results show that the proposed protocol preserves perfect forward key secrecy, is robust against de-synchronization, replay, man-in-the-middle (MITM), denial of services (DoS) and jamming attacks. Performance analysis showed that it has low communication overheads, space complexity and handover authentication latencies. Compared with 3GPP R16, the proposed protocol showed a 25% and 42.9% improvement in communication overheads and space complexity respectively.
Complexity of training ReLU neural network

In this paper, we explore some basic questions on the complexity of training neural networks with ReLU activation function. We show that it is NP-hard to train a two-hidden layer feedforward ReLU neural network. If dimension of the input data and the network topology is fixed, then we show that there exists a polynomial time algorithm for the same training problem. We also show that if sufficient over-parameterization is provided in the first hidden layer of ReLU neural network, then there is a polynomial time algorithm which finds weights such that output of the over-parameterized ReLU neural network matches with the output of the given data.
Ship detection in optical sensing images based on YOLOv5

Ship detection technology is an important development direction in the field of optical remote sensing image processing. In recent years, convolutional neural networks have achieved good results in ship target detection and recognition. We train the latest model YOLOv5 on our dataset in this paper. The results show that YOLOv5 can be well applied in the field of ship detection.
Multi-class Weather Forecasting from Twitter Using Machine Learning Aprroaches

Now in the era of big data, many are applying information methods accurately especially by social media. The aims of this study to classify the weather based on Twitter automatically using text mining by using Support Vector Machine (SVM), MultinomialNaive Bayes (MNB), and Logistic Regression (LR) method. The experimental results show that SVM substantially outperforms various other machine learning algorithms for the task of text classification with an accuracy value of 93%. This result proves that SVM is very suitable for text categorization. We use clustering technique to read the pattern in customers’ opinion about the restaurant based on some measurement variables.
Horus: Interference-Aware and Prediction-Based Scheduling in Deep Learning Systems

To accelerate the training of Deep Learning (DL) models, clusters of machines equipped with hardware accelerators such as GPUs are leveraged to reduce execution time. State-of-the-art resource managers are needed to increase GPU utilization and maximize throughput. While co-locating DL jobs on the same GPU has been shown to be effective, this can incur interference causing slowdown. In this article we propose Horus: an interference-aware and prediction-based resource manager for DL systems. Horus proactively predicts GPU utilization of heterogeneous DL jobs extrapolated from the DL model's computation graph features, removing the need for online profiling and isolated reserved GPUs. Through micro-benchmarks and job co-location combinations across heterogeneous GPU hardware, we identify GPU utilization as a general proxy metric to determine good placement decisions, in contrast to current approaches which reserve isolated GPUs to perform online profiling and directly measure GPU utilization for each unique submitted job. Our approach promotes high resource utilization and makespan reduction; via real-world experimentation and large-scale trace driven simulation, we demonstrate that Horus outperforms other DL resource managers by up to 61.5 percent for GPU resource utilization, 23.7-30.7 percent for makespan reduction and 68.3 percent in job wait time reduction.
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach

Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets. Especially for fine-grained recognition, which targets at distinguishing subordinate categories, it will significantly reduce the labeling costs by leveraging free web data. Despite its significant practical and research value, the webly supervised fine-grained recognition problem is not extensively studied in the computer vision community, largely due to the lack of high-quality datasets. To fill this gap, in this paper we construct two new benchmark webly supervised fine-grained datasets, termed WebFG-496 and WebiNat-5089, respectively. In concretely, WebFG-496 consists of three sub-datasets containing a total of 53,339 web training images with 200 species of birds (Web-bird), 100 types of aircrafts (Web-aircraft), and 196 models of cars (Web-car). For WebiNat-5089, it contains 5089 sub-categories and more than 1.1 million web training images, which is the largest webly supervised fine-grained dataset ever. As a minor contribution, we also propose a novel webly supervised method (termed "Peer-learning") for benchmarking these datasets. Comprehensive experimental results and analyses on two new benchmark datasets demonstrate that the proposed method achieves superior performance over the competing baseline models and states-of-the-art. Our benchmark datasets and the source codes of Peer-learning have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/weblyFG-dataset.
Fairea: a model behaviour mutation approach to benchmarking bias mitigation methods

The increasingly wide uptake of Machine Learning (ML) has raised the significance of the problem of tackling bias (i.e., unfairness), making it a primary software engineering concern. In this paper, we introduce Fairea, a model behaviour mutation approach to benchmarking ML bias mitigation methods. We also report on a large-scale empirical study to test the effectiveness of 12 widely-studied bias mitigation methods. Our results reveal that, surprisingly, bias mitigation methods have a poor effectiveness in 49% of the cases. In particular, 15% of the mitigation cases have worse fairness-accuracy trade-offs than the baseline established by Fairea; 34% of the cases have a decrease in accuracy and an increase in bias.
Learning multiscale hierarchical attention for video summarization

In this paper, we propose a multiscale hierarchical attention approach for supervised video summarization. Different from most existing supervised methods which employ bidirectional long short-term memory networks, our method exploits the underlying hierarchical structure of video sequences and learns both the short-range and long-range temporal representations via a intra-block and a inter-block attention. Specifically, we first separate each video sequence into blocks of equal length and employ the intra-block and inter-block attention to learn local and global information, respectively. Then, we integrate the frame-level, block-level, and video-level representations for the frame-level importance score prediction. Next, we conduct shot segmentation and compute shot-level importance scores. Finally, we perform key shot selection to produce video summaries. Moreover, we extend our method into a two-stream framework, where appearance and motion information is leveraged. Experimental results on the SumMe and TVSum datasets validate the effectiveness of our method against state-of-the-art methods.
GeoNeRF: Generalizing NeRF with Geometry Priors

We present GeoNeRF, a generalizable photorealistic novel view synthesis method based on neural radiance fields. Our approach consists of two main stages: a ge-ometry reasoner and a renderer. To render a novel view, the geometry reasoner first constructs cascaded cost volumes for each nearby source view. Then, using a Transformer- based attention mechanism and the cascaded cost volumes, the renderer infers geometry and appearance, and ren-ders detailed images via classical volume rendering techniques. This architecture, in particular, allows sophis-ticated occlusion reasoning, gathering information from consistent source views. Moreover, our method can eas-ily be fine-tuned on a single scene, and renders com-petitive results with per-scene optimized neural rendering methods with a fraction of computational cost. Ex-periments show that GeoNeRF outperforms state-of-the- art generalizable neural rendering models on various syn-thetic and real datasets. Lastly, with a slight modification to the geometry reasoner, we also propose an alter-native model that adapts to RGBD images. This model di-rectly exploits the depth information often available thanks to depth sensors. The implementation code is available at https://www.idiap.ch/paper/geonerf.
Adaptive Graph Auto-Encoder for General Data Clustering

Graph-based clustering plays an important role in the clustering area. Recent studies about graph neural networks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GNN</i> ) have achieved impressive success on graph-type data. However, in general clustering tasks, the graph structure of data does not exist such that GNN can not be applied to clustering directly and the strategy to construct a graph is crucial for performance. Therefore, how to extend GNN into general clustering tasks is an attractive problem. In this paper, we propose a graph auto-encoder for general data clustering, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdaGAE</i> , which constructs the graph adaptively according to the generative perspective of graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-euclidean structure sufficiently. Importantly, we find that the simple update of the graph will result in severe degeneration, which can be concluded as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">better reconstruction means worse update</i> . We provide rigorous analysis theoretically and empirically. Then we further design a novel mechanism to avoid the collapse. Via extending the generative graph models to general type data, a graph auto-encoder with a novel decoder is devised and the weighted graphs can be also applied to GNN. AdaGAE performs well and stably in different scale and type datasets. Besides, it is insensitive to the initialization of parameters and requires no pretraining.
Virtual reality as a didactic resource from the perspective of engineering teachers

Abstract This paper analyzes the assessment of engineering professors of different nationalities and universities regarding the use of virtual reality (VR) technologies in the classroom. In particular, the existence of gaps in these evaluations by ownership (private or public) of the university where each professor teaches is analyzed, both within the complete sample of participants and within the subsets formed when they are differentiated by gender, age, and time of teaching experience. For this purpose, a questionnaire has been designed and validated with 22 inquiries that have been dispersed in six scales, corresponding to six different dimensions that affect the didactic use of VR. The questionnaire has been distributed to a set of 279 university professors from different Engineering schools and countries. The results, which have been analyzed quantitatively, both descriptive and inferential, indicate that engineering teachers give high evaluations to VR as a didactic tool, but show a certain lack of knowledge and specific training regarding its use. Furthermore, there is a gap between private and public universities, where private university professors express overall higher evaluations of VR than public university professors.
NeRF-Supervision: Learning Dense Object Descriptors from Neural Radiance Fields

Thin, reflective objects such as forks and whisks are common in our daily lives, but they are particularly chal-lenging for robot perception because it is hard to reconstruct them using commodity RGB-D cameras or multi-view stereo techniques. While traditional pipelines struggle with objects like these, Neural Radiance Fields (NeRFs) have recently been shown to be remarkably effective for performing view synthesis on objects with thin structures or reflective materials. In this paper we explore the use of NeRF as a new source of supervision for robust robot vision systems. In particular, we demonstrate that a NeRF representation of a scene can be used to train dense object descriptors. We use an optimized NeRF to extract dense correspondences between multiple views of an object, and then use these correspondences as training data for learning a view-invariant representation of the object. NeRF's usage of a density field allows us to reformulate the correspondence problem with a novel distribution-of-depths formulation, as opposed to the conventional approach of using a depth map. Dense correspondence models supervised with our method significantly outperform off-the-shelf learned descriptors by 106% (PCK@3px metric, more than doubling performance) and outperform our baseline supervised with multi-view stereo by 29%. Furthermore, we demonstrate the learned dense descriptors enable robots to perform accurate 6-degree of freedom (6-DoF) pick and place of thin and reflective objects.