nils-herrmann/clean_scirepeval huggingface dataset:
Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach

Food choices are personal and complex and have a significant impact on our long-term health and quality of life. By helping users to make informed and satisfying decisions, Recommender Systems (RS) have the potential to support users in making healthier food choices. Intelligent users-modeling is a key challenge in achieving this potential. This paper investigates Ensemble Topic Modelling (EnsTM) based Feature Identification techniques for efficient user-modeling and recipe recommendation. It builds on findings in EnsTM to propose a reduced data representation format and a smart user-modeling strategy that makes capturing user-preference fast, efficient and interactive. This approach enables personalization, even in a cold-start scenario. This paper proposes two different EnsTM based and one Hybrid EnsTM based recommenders. We compared all three EnsTM based variations through a user study with 48 participants, using a large-scale,real-world corpus of 230,876 recipes, and compare against a conventional Content Based (CB) approach. EnsTM based recommenders performed significantly better than the CB approach. Besides acknowledging multi-domain contents such as taste, demographics and cost, our proposed approach also considers user's nutritional preference and assists them finding recipes under diverse nutritional categories. Furthermore, it provides excellent coverage and enables implicit understanding of user's food practices. Subsequent analysis also exposed correlation between certain features and a healthier lifestyle.
Deep Gaussian networks for function approximation on data defined manifolds

In much of the literature on function approximation by deep networks, the function is assumed to be defined on some known domain, such as a cube or sphere. In practice, the data might not be dense on these domains, and therefore, the approximation theory results are observed to be too conservative. In manifold learning, one assumes instead that the data is sampled from an unknown manifold; i.e., the manifold is defined by the data itself. Function approximation on this unknown manifold is then a two stage procedure: first, one approximates the Laplace-Beltrami operator (and its eigen-decomposition) on this manifold using a graph Laplacian, and next, approximates the target function using the eigen-functions. In this paper, we propose a more direct approach to function approximation on unknown, data defined manifolds without computing the eigen-decomposition of some operator, and estimate the degree of approximation in terms of the manifold dimension. This leads to similar results in function approximation using deep networks where each channel evaluates a Gaussian network on a possibly unknown manifold.
Quantum Temporal Logic: from Birkhoff and von Neumann to Pnueli

In this paper, we introduce a model of quantum concurrent program, which can be used to model the behaviour of reactive quantum systems and to design quantum compilers. We investigate quantum temporal logic, QTL, for the specification of quantum concurrent systems by suggesting the time-dependence of events. QTL employs the projections on subspaces as atomic propositions, which was established in the Birkhoff and von Neumann's classic treatise on quantum logic. For deterministic functional quantum program, We prove a quantum Bohm-Jacopini theorem which states that any such program is equivalent to a Q-While program. The decidability of basic QTL formulae for general quantum concurrent program is studied.
Max–Min Fairness Design for MIMO Interference Channels: A Minorization–Maximization Approach

We address the problem of linear precoder (beamformer) design in a multiple-input multiple-output interference channel. The aim is to design the transmit covariance matrices in order to achieve max–min utility fairness for all users. The corresponding optimization problem is non-convex and NP-hard in general. We devise an efficient algorithm based on the minorization–maximization technique to obtain quality solutions to this problem. The proposed method solves a second-order cone convex program at each iteration. We prove that the devised method converges to stationary points of the problem. We also extend our algorithm to the case where there are uncertainties in the noise covariance matrices or channel state information. Simulation results show the effectiveness of the proposed method compared with its main competitor.
The Constrained Round Robin Algorithm for Fair and Efficient Allocation

We consider a multi-agent resource allocation setting that models the assignment of papers to reviewers. A recurring issue in allocation problems is the compatibility of welfare/efficiency and fairness. Given an oracle to find a welfare-achieving allocation, we embed such an oracle into a flexible algorithm called the Constrained Round Robin (CRR) algorithm, that achieves the required welfare level. Our algorithm also allows the system designer to lower the welfare requirements in order to achieve a higher degree of fairness. If the welfare requirement is lowered enough, a strengthening of envy-freeness up to one item is guaranteed. Hence, our algorithm can be viewed as a computationally efficient way to interpolate between welfare and approximate envy-freeness in allocation problems.
Computational challenges for MC event generation

The sophistication of fully exclusive MC event generation has grown at an extraordinary rate since the start of the LHC era, but has been mirrored by a similarly extraordinary rise in the CPU cost of state-of-the-art MC calculations. The reliance of experimental analyses on these calculations raises the disturbing spectre of MC computations being a leading limitation on the physics impact of the HL-LHC, with MC trends showing more signs of further cost-increases rather than the desired speed-ups. I review the methods and bottlenecks in MC computation, and areas where new computing architectures, machine-learning methods, and social structures may help to avert calamity.
Accelerating CNN Training by Sparsifying Activation Gradients

Gradients to activations get involved in most of the calculations during back propagation procedure of Convolution Neural Networks (CNNs) training. However, an important known observation is that the majority of these gradients are close to zero, imposing little impact on weights update. These gradients can be then pruned to achieve high gradient sparsity during CNNs’ training and reduce the computational cost. In particular, we randomly change a gradient to zero or a threshold value if the gradient is below the threshold which is determined by the statistical distribution of activation gradients. We also theoretically proved that the training convergence of the CNN model can be guaranteed when the above activation gradient sparsification method is applied. We evaluated our method on AlexNet, MobileNet, ResNet-{18, 34, 50, 101, 152} with CIFAR-{10, 100} and ImageNet datasets. Experimental results show that our method can substantially reduce the computational cost with negligible accuracy loss or even accuracy improvement. Finally, we analyzed the benefits that the sparsity of activation gradients introduced in detail.
FairSight: Visual Analytics for Fairness in Decision Making

Data-driven decision making related to individuals has become increasingly pervasive, but the issue concerning the potential discrimination has been raised by recent studies. In response, researchers have made efforts to propose and implement fairness measures and algorithms, but those efforts have not been translated to the real-world practice of data-driven decision making. As such, there is still an urgent need to create a viable tool to facilitate fair decision making. We propose FairSight, a visual analytic system to address this need; it is designed to achieve different notions of fairness in ranking decisions through identifying the required actions – understanding, measuring, diagnosing and mitigating biases – that together lead to fairer decision making. Through a case study and user study, we demonstrate that the proposed visual analytic and diagnostic modules in the system are effective in understanding the fairness-aware decision pipeline and obtaining more fair outcomes.
Learning When to Drive in Intersections by Combining Reinforcement Learning and Model Predictive Control

In this paper, we propose a decision making algorithm intended for automated vehicles that negotiate with other possibly non-automated vehicles in intersections. The decision algorithm is separated into two parts: a high-level decision module based on reinforcement learning, and a low-level planning module based on model predictive control. Traffic is simulated with numerous predefined driver behaviors and intentions, and the performance of the proposed decision algorithm was evaluated against another controller. The results show that the proposed decision algorithm yields shorter training episodes and an increased performance in success rate compared to the other controller.
Scalable Place Recognition Under Appearance Change for Autonomous Driving

A major challenge in place recognition for autonomous driving is to be robust against appearance changes due to short-term (e.g., weather, lighting) and long-term (seasons, vegetation growth, etc.) environmental variations. A promising solution is to continuously accumulate images to maintain an adequate sample of the conditions and incorporate new changes into the place recognition decision. However, this demands a place recognition technique that is scalable on an ever growing dataset. To this end, we propose a novel place recognition technique that can be efficiently retrained and compressed, such that the recognition of new queries can exploit all available data (including recent changes) without suffering from visible growth in computational cost. Underpinning our method is a novel temporal image matching technique based on Hidden Markov Models. Our experiments show that, compared to state-of-the-art techniques, our method has much greater potential for large-scale place recognition for autonomous driving.
EcoLens: Visual Analysis of Urban Region Dynamics Using Traffic Data

The rapid development of urbanization during the past decades has significantly improved people's lives but also introduced new challenges on effective functional urban planning and transportation management. The functional regions defined based on a static boundary rarely reflect an individual's daily experience of the space in which they live and visit for a variety of purposes. Fortunately, the increasing availability of spatiotemporal data provides unprecedented opportunities for understanding the structure of an urban area in terms of people's activity pattern and how they form the latent regions over time. These ecological regions, where people temporarily share a similar moving behavior during a short period of time, could provide insights into urban planning and smart-city services. However, existing solutions are limited in their capacity of capturing the evolutionary patterns of dynamic latent regions within urban context. In this work, we introduce an interactive visual analysis approach, EcoLens, that allows analysts to progressively explore and analyze the complex dynamic segmentation patterns of a city using traffic data. We propose an extended non-negative Matrix Factorization based algorithm smoothed over both spatial and temporal dimensions to capture the spatiotemporal dynamics of the city. The algorithm also ensures the orthogonality of its result to facilitate the interpretation of different patterns. A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time. We evaluate the effectiveness of our system via case studies using a real-world dataset and a qualitative interview with the domain expert.
Hexagonal Nanopits with the Zigzag Edge State on Graphite Surfaces Synthesized by Hydrogen-Plasma Etching

We studied, by scanning tunneling microscopy, the morphology of nanopits of monolayer depth created at graphite surfaces by hydrogen plasma etching under various conditions such as H$_2$ pressure, temperature, etching time, and RF power of the plasma generation. In addition to the known pressure-induced transition of the nanopit morphology, we found a sharp temperature-induced transition from many small rather round nanopits of ~150 nm size to few large hexagonal ones of 300-600 nm within a narrow temperature range. The remote and direct plasma modes switching mechanism, which was proposed to explain the pressure-induced transition, is not directly applicable to this newly found transition. Scanning tunneling spectroscopy (STS) measurements of edges of the hexagonal nanopits fabricated at graphite surfaces by this method show clear signatures of the peculiar electronic state localized at the zigzag edge (edge state), i.e., a prominent peak near the Fermi energy accompanied by suppressions on either side in the local density of states. These observations indicate that the hexagonal nanopits consist of a high density of zigzag edges. The STS data also revealed a domain structure of the edge state in which the electronic state varies over a length scale of ~3 nm along the edge. The present study will pave the way for microscopic understanding of the anisotropic etching mechanism and of spin polarization in zigzag nanoribbons which are promising key elements for future graphene nanoelectronics.
Solving a Flowshop Scheduling Problem with Answer Set Programming: Exploiting the Problem to Reduce the Number of Combinations

Planning and scheduling have been a central theme of research in computer science. In particular, the simplicity of the theoretical approach of a no-wait flowshop scheduling problem does not allow to perceive the problem complexity at first sight. In this paper the applicability of the Answer Set Programming language is explored for the solution of the Automated Wet-etching scheduling problem in Semiconductor Manufacturing Systems. A method based in ranges is proposed in order to reduce the huge number of combinations.
Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview

Graph neural networks denote a group of neural network models introduced for the representation learning tasks on graph data specifically. Graph neural networks have been demonstrated to be effective for capturing network structure information, and the learned representations can achieve the state-of-the-art performance on node and graph classification tasks. Besides the different application scenarios, the architectures of graph neural network models also depend on the studied graph types a lot. Graph data studied in research can be generally categorized into two main types, i.e., small graphs vs. giant networks, which differ from each other a lot in the size, instance number and label annotation. Several different types of graph neural network models have been introduced for learning the representations from such different types of graphs already. In this paper, for these two different types of graph data, we will introduce the graph neural networks introduced in recent years. To be more specific, the graph neural networks introduced in this paper include IsoNN, SDBN, LF&ER, GCN, GAT, DifNN, GNL, GraphSage and seGEN. Among these graph neural network models, IsoNN, SDBN and LF&ER are initially proposed for small graphs and the remaining ones are initially proposed for giant networks instead. The readers are also suggested to refer to these papers for detailed information when reading this tutorial paper.
CCR and CAR flows over convex cones

Recently it is proved in  that CCR flows over convex cones are cocycle conjugate if and only if the associated isometric representations are conjugate. We provide a very short, simple and direct proof of that. Using the same idea we prove the analogous statement for CAR flows as well. Further we show that CCR flows are not cocycle conjugate to the CAR flows when the (multi-parameter) isometric representation is `proper', a condition which is satisfied by all known examples.
Fredholm approach to characterize gravitational wave echoes

Gravitational waves are a sensitive probe into the structure of compact astronomical objects and the nature of gravity in the strong regime. Modifications of near-horizon physics can imprint on the late time ringdown waveform, leaving behind a train of echoes, from which useful information about new physics in the strong gravity regime can be extracted. We propose a novel approach to compute the rindown waveform and characterize individual echoes perturbatively using the Fredholm determinants, which can be intuitively represented via a diagrammatical scheme. Direct nonperturbative treatments can also be easily implemented for some cases. Numerically, the method is also effective and accurate even for relatively low resources.
New Technologies for Discovery

For the field of high energy physics to continue to have a bright future, priority within the field must be given to investments in the development of both evolutionary and transformational detector development that is coordinated across the national laboratories and with the university community, international partners and other disciplines. While the fundamental science questions addressed by high energy physics have never been more compelling, there is acute awareness of the challenging budgetary and technical constraints when scaling current technologies. Furthermore, many technologies are reaching their sensitivity limit and new approaches need to be developed to overcome the currently irreducible technological challenges. This situation is unfolding against a backdrop of declining funding for instrumentation, both at the national laboratories and in particular at the universities. This trend has to be reversed for the country to continue to play a leadership role in particle physics, especially in this most promising era of imminent new discoveries that could finally break the hugely successful, but limited, Standard Model of fundamental particle interactions. In this challenging environment it is essential that the community invest anew in instrumentation and optimize the use of the available resources to develop new innovative, cost-effective instrumentation, as this is our best hope to successfully accomplish the mission of high energy physics. This report summarizes the current status of instrumentation for high energy physics, the challenges and needs of future experiments and indicates high priority research areas.
Learning-Aided Physical Layer Attacks Against Multicarrier Communications in IoT

Internet-of-Things (IoT) devices that are limited in power and processing capabilities are susceptible to physical layer (PHY) spoofing (signal exploitation) attacks owing to their inability to implement a full-blown protocol stack for security. The overwhelming adoption of multicarrier techniques such as orthogonal frequency division multiplexing (OFDM) for the PHY layer makes IoT devices further vulnerable to PHY spoofing attacks. These attacks which aim at injecting bogus/spurious data into the receiver, involve inferring transmission parameters and finding PHY characteristics of the transmitted signals so as to spoof the received signal. Non-contiguous (NC) OFDM systems have been argued to have low probability of exploitation (LPE) characteristics against classic attacks based on cyclostationary analysis, and the corresponding PHY has been deemed to be secure. However, with the advent of machine learning (ML) algorithms, adversaries can devise data-driven attacks to compromise such systems. It is in this vein that PHY spoofing performance of adversaries equipped with supervised and unsupervised ML tools are investigated in this paper. The supervised ML approach is based on estimation/classification utilizing deep neural networks (DNN) while the unsupervised one employs variational autoencoders (VAEs). In particular, VAEs are shown to be capable of learning representations from NC-OFDM signals related to their PHY characteristics such as frequency pattern and modulation scheme, which are useful for PHY spoofing. In addition, a new metric based on the disentanglement principle is proposed to measure the quality of such learned representations. Simulation results demonstrate that the performance of the spoofing adversaries highly depends on the subcarriers’ allocation patterns used at the transmitter. Particularly, it is shown that utilizing a random subcarrier occupancy pattern precludes the adversary from spoofing and secures NC-OFDM systems against ML-based attacks.
GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation

Many scientific computing problems, including circuit simulations, rely on efficient lower–upper (LU) decomposition of sparse matrices. Prior studies took advantage of GPUs to parallelize LU decomposition, but they suffer from nontrivial data dependencies. This article presents a new method, called GLU3.0, to accelerate GPU-based sparse LU factorization. —Umit Ogras, Arizona State University
Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction

Quantitative Susceptibility Mapping (QSM) can estimate the underlying tissue magnetic susceptibility and reveal pathology. Current deep-learning-based approaches to solve the QSM inverse problem are restricted on fixed image resolution. They trained a specific model for each image resolution which is inefficient in computing. In this work, we proposed a novel method called Meta-QSM to firstly solve QSM reconstruction of arbitrary image resolution with a single model. In Meta-QSM, weight prediction was used to predict the weights of kernels by taking the image resolution as input. The proposed method was evaluated on synthetic data and clinical data with comparison to existing QSM reconstruction methods. The experimental results showed the Meta-QSM can effectively reconstruct susceptibility maps with different image resolution using one neural network training.
Unification for darkly charged dark matter

We provide a simple UV theory for a Dirac dark matter with a massless Abelian gauge boson. We introduce a single fermion transforming as the $\bf{16}$ representation in the SO(10)$'$ gauge group, which is assumed to be spontaneously broken to SU(5)$'\times$U(1)$'$. The SU(5)$'$ gauge interaction becomes strong at an intermediate scale and then we obtain a light composite Dirac fermion with U(1)$'$ gauge interaction at the low-energy scale. Its thermal relic can explain the observed amount of dark matter consistently with other cosmological and astrophysical constraints. We discuss that a nonzero kinetic mixing between the U(1)$'$ gauge boson and the Hypercharge gauge boson is allowed and the temperature of the visible sector and the dark matter sector can be equal to each other.