Miracle-dz/UniFAR-FTUs huggingface dataset:
TripClick: The Log Files of a Large Health Web Search Engine

Click logs are valuable resources for a variety of information retrieval (IR) tasks. This includes query understanding/analysis, as well as learning effective IR models particularly when the models require large amounts of training data. We release a large-scale domain-specific dataset of click logs, obtained from user interactions of the Trip Database health web search engine. Our click log dataset comprises approximately 5.2 million user interactions collected between 2013 and 2020. We use this dataset to create a standard IR evaluation benchmark - TripClick - with around 700,000 unique free-text queries and 1.3 million pairs of query-document relevance signals, whose relevance is estimated by two click-through models. As such, the collection is one of the few datasets offering the necessary data richness and scale to train neural IR models with a large amount of parameters, and notably the first in the health domain. Using TripClick, we conduct experiments to evaluate a variety of IR models, showing the benefits of exploiting this data to train neural architectures. In particular, the evaluation results show that the best performing neural IR model significantly improves the performance by a large margin relative to classical IR models, especially for more frequent queries.
Explaining Credit Risk Scoring through Feature Contribution Alignment with Expert Risk Analysts

Credit assessments activities are essential for financial institutions and allow the global economy to grow. Building robust, solid and accurate models that estimate the probability of a default of a company is mandatory for credit insurance companies, moreover when it comes to bridging the trade finance gap. Automating the risk assessment process will allow credit risk experts to reduce their workload and focus on the critical and complex cases, as well as to improve the loan approval process by reducing the time to process the application. The recent developments in Artificial Intelligence are offering new powerful opportunities. However, most AI techniques are labelled as blackbox models due to their lack of explainability. For both users and regulators, in order to deploy such technologies at scale, being able to understand the model logic is a must to grant accurate and ethical decision making. In this study, we focus on companies credit scoring and we benchmark different machine learning models. The aim is to build a model to predict whether a company will experience financial problems in a given time horizon. We address the black box problem using eXplainable Artificial Techniques in particular, post-hoc explanations using SHapley Additive exPlanations. We bring light by providing an expert-aligned feature relevance score highlighting the disagreement between a credit risk expert and a model feature attribution explanation in order to better quantify the convergence towards a better human-aligned decision making.
A correction method for the telluric absorptions and application to Lijiang Observatory

Observing a telluric standard star for correcting the telluric absorption lines of spectrum will take a significant amount of precious telescope time, especially in the long-term spectral monitoring project. Beyond that, it is difficult to select a suitable telluric standard star near in both time and airmass to the scientific object. In this paper, we present a method of correcting the telluric absorption lines by combining the advantages of long slit spectroscopy. By rotating the slit, we observed the scientific object and a nearby comparison star in one exposure, so that the spectra of both objects should have the same telluric transmission spectrum. The telluric transmission spectrum was constructed by dividing the observed spectrum of the comparison star by its stellar template, and was used to correct the telluric absorption lines of the scientific object. Using the long slit spectrograph of the Lijiang 2.4-meter telescope, we designed a long-term spectroscopic observation strategy, and finished a four-year spectroscopic monitoring for a pair of objects (an active galactic nucleus and a non-varying comparison star). We applied this method to correct the telluric absorption lines of the long-term monitored spectra by the Lijiang 2.4-meter telescope, and investigated the variation of the telluric absorptions at Lijiang Observatory. We found that the telluric absorption transparency is mainly modulated by the seasonal variability of the relative humidity, airmass and seeing. Using the scatter of the [OIII] λ5007 fluxes emitted from the narrow-line region of active galactic nuclei as an indicator, we found that the correction accuracy of the telluric absorption lines is 1%.
Deep Tiling: Texture Tile Synthesis Using a Deep Learning Approach

Texturing is a fundamental process in computer graphics. Texture is leveraged to enhance the visualization outcome for a 3D scene. In many cases a texture image cannot cover a large 3D model surface because of its small resolution. Conventional techniques like repeating, mirror repeating or clamp to edge do not yield visually acceptable results. Deep learning based texture synthesis has proven to be very effective in such cases. All deep texture synthesis methods trying to create larger resolution textures are limited in terms of GPU memory resources. In this paper, we propose a novel approach to example-based texture synthesis by using a robust deep learning process for creating tiles of arbitrary resolutions that resemble the structural components of an input texture. In this manner, our method is firstly much less memory limited owing to the fact that a new texture tile of small size is synthesized and merged with the original texture and secondly can easily produce missing parts of a large texture.
UrbanVCA: a vector-based cellular automata framework to simulate the urban land-use change at the land-parcel level

Vector-based cellular automata (CA) based on real land-parcel has become an important trend in current urban development simulation studies. Compared with raster-based and parcel-based CA models, vector CA models are difficult to be widely used because of their complex data structures and technical difficulties. The UrbanVCA, a brand-new vector CA-based urban development simulation framework was proposed in this study, which supports multiple machine-learning models. To measure the simulation accuracy better, this study also first proposes a vector-based landscape index (VecLI) model based on the real land-parcels. Using Shunde, Guangdong as the study area, the UrbanVCA simulates multiple types of urban land-use changes at the land-parcel level have achieved a high accuracy (FoM=0.243) and the landscape index similarity reaches 87.3%. The simulation results in 2030 show that the eco-protection scenario can promote urban agglomeration and reduce ecological aggression and loss of arable land by at least 60%. Besides, we have developed and released UrbanVCA software for urban planners and researchers.
Chandra Observations of the Planck Early Sunyaev–Zeldovich Sample: A Reexamination of Masses and Mass Proxies

Using Chandra observations, we derive the Y X proxy and associated total mass measurement, , for 147 clusters with z < 0.35 from the Planck early Sunyaev–Zeldovich catalog, and for 80 clusters with z < 0.22 from an X-ray flux-limited sample. We reextract the Planck Y SZ measurements and obtain the corresponding mass proxy, , from the full Planck mission maps, minimizing Malmquist bias due to observational scatter. The masses reextracted using the more precise X-ray position and characteristic size agree with the published PSZ2 values, but yield a significant reduction in the scatter (by a factor of two) in the – relation. The slope is 0.93 ± 0.03, and the median ratio, , is within the expectations from known X-ray calibration systematics. Y SZ/Y X is 0.88 ± 0.02, in good agreement with predictions from cluster structure, and implying a low level of clumpiness. In agreement with the findings of the Planck Collaboration, the slope of the Y SZ– flux relation is significantly less than unity (0.89 ± 0.01). Using extensive simulations, we show that this result is not due to selection effects, intrinsic scatter, or covariance between quantities. We demonstrate analytically that changing the Y SZ–Y X relation from apparent flux to intrinsic properties results in a best-fit slope that is closer to unity and increases the dispersion about the relation. The redistribution resulting from this transformation implies that the best-fit parameters of the – relation will be sample-dependent.
Synthesis of cassava starch‐g‐poly(methyl methacrylate) copolymers with benzoyl peroxide as an initiator

Graft copolymers of cassava starch and methyl methacrylate (MMA) were synthesized by free-radical polymerization with benzoyl peroxide (BPO) as an initiator in an aqueous medium at 80°C. The formation of graft copolymers was confirmed by analysis of the obtained products with Fourier transform infrared spectroscopy and scanning electron microscopy. The effects of the amount of cassava starch, the amount of MMA monomer, the amount of BPO, and the reaction time on the grafting characteristics were studied. The optimum condition for grafting were obtained when 5 g of cassava starch, 5 g of MMA, 0.1 g of BPO, and a reaction time of 3 h were used. These condition provided a graft copolymer with 25.00% add-on, 81.40% monomer conversion, 54.30% homopoly(methyl methacrylate) formed, 45.70% grafting efficiency, 37.20% grafting ratio, and 95.54% yield. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4083–4089, 2006
Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation

We aim to estimate food portion size, a property that is strongly related to the presence of food object in 3D space, from single monocular images under real life setting. Specifically, we are interested in end-to-end estimation of food portion size, which has great potential in the field of personal health management. Unlike image segmentation or object recognition where annotation can be obtained through large scale crowd sourcing, it is much more challenging to collect datasets for portion size estimation since human cannot accurately estimate the size of an object in an arbitrary 2D image without expert knowledge. To address such challenge, we introduce a real life food image dataset collected from a nutrition study where the groundtruth food energy (calorie) is provided by registered dietitians, and will be made available to the research community. We propose a deep regression process for portion size estimation by combining features estimated from both RGB and learned energy distribution domains. Our estimates of food energy achieved state-of-the-art with a MAPE of 11.47%, significantly outperforms non-expert human estimates by 27.56%.
GraphIQA: Learning Distortion Graph Representations for Blind Image Quality Assessment

A good distortion representation is crucial for the success of deep blind image quality assessment (BIQA). However, most previous methods do not effectively model the relationship between distortions or the distribution of samples with the same distortion type but different distortion levels. In this work, we start from the analysis of the relationship between perceptual image quality and distortion-related factors, such as distortion types and levels. Then, we propose a Distortion Graph Representation (DGR) learning framework for IQA, named GraphIQA, in which each distortion is represented as a graph, i.e., DGR. One can distinguish distortion types by learning the contrast relationship between these different DGRs, and can infer the ranking distribution of samples from different levels in a DGR. Specifically, we develop two sub-networks to learn the DGRs: a) Type Discrimination Network (TDN) that aims to embed DGR into a compact code for better discriminating distortion types and learning the relationship between types; b) Fuzzy Prediction Network (FPN) that aims to extract the distributional characteristics of the samples in a DGR and predicts fuzzy degrees based on a Gaussian prior. Experiments show that our GraphIQA achieves state-of-the-art performance on many benchmark datasets of both synthetic and authentic distortions.
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction

The building sector consumes the largest energy in the world, and there have been considerable research interests in energy consumption and comfort management of buildings. Inspired by recent advances in reinforcement learning (RL), this paper aims at assessing the potential of RL in building climate control problems with occupant interaction. We apply a recent RL approach, called DDPG (deep deterministic policy gradient), for the continuous building control tasks and assess its performance with simulation studies in terms of its ability to handle (a) the partial state observability due to sensor limitations; (b) complex stochastic system with high-dimensional state-spaces, which are jointly continuous and discrete; (c) uncertainties due to ambient weather conditions, occupant's behavior, and comfort feelings. Especially, the partial observability and uncertainty due to the occupant interaction significantly complicate the control problem. Through simulation studies, the policy learned by DDPG demonstrates reasonable performance and computational tractability.
Implementation of Relativistic Coupled Cluster Theory for Massively Parallel GPU-Accelerated Computing Architectures

In this paper, we report reimplementation of the core algorithms of relativistic coupled cluster theory aimed at modern heterogeneous high-performance computational infrastructures. The code is designed for parallel execution on many compute nodes with optional GPU coprocessing, accomplished via the new ExaTENSOR back end. The resulting ExaCorr module is primarily intended for calculations of molecules with one or more heavy elements, as relativistic effects on the electronic structure are included from the outset. In the current work, we thereby focus on exact two-component methods and demonstrate the accuracy and performance of the software. The module can be used as a stand-alone program requiring a set of molecular orbital coefficients as the starting point, but it is also interfaced to the DIRAC program that can be used to generate these. We therefore also briefly discuss an improvement of the parallel computing aspects of the relativistic self-consistent field algorithm of the DIRAC program.
Internal Wasserstein Distance for Adversarial Attack and Defense

Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks that would trigger misclassification of DNNs but may be imperceptible to human perception. Adversarial defense has been an important way to improve the robustness of DNNs. Existing attack methods often construct adversarial examples relying on some metrics like the $\ell_p$ distance to perturb samples. However, these metrics can be insufficient to conduct adversarial attacks due to their limited perturbations. In this paper, we propose a new internal Wasserstein distance (IWD) to capture the semantic similarity of two samples, and thus it helps to obtain larger perturbations than currently used metrics such as the $\ell_p$ distance. We then apply the internal Wasserstein distance to perform adversarial attack and defense. In particular, we develop a novel attack method relying on IWD to calculate the similarities between an image and its adversarial examples. In this way, we can generate diverse and semantically similar adversarial examples that are more difficult to defend by existing defense methods. Moreover, we devise a new defense method relying on IWD to learn robust models against unseen adversarial examples. We provide both thorough theoretical and empirical evidence to support our methods.
Improvement of phase sensitivity in SU(1,1) interferometer via a Kerr nonlinear

We propose a theoretical scheme to enhance the phase sensitivity by introducing a Kerr nonlinear phase shift into the traditional SU(1,1) interferometer with a coherent state input and homodyne detection. We investigate the realistic effects of photon losses on phase sensitivity and quantum Fisher information. The results show that compared with the linear phase shift in SU(1,1) interferometer, the Kerr nonlinear case can not only enhance the phase sensitivity and quantum Fisher information, but also significantly suppress the photon losses. We also observe that at the same accessible parameters, internal losses have a greater influence on the phase sensitivity than the external ones. It is interesting that, our scheme shows an obvious advantage of low-cost input resources to obtain higher phase sensitivity and larger quantum Fisher information due to the introduction of nonlinear phase element.
Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction

The contribution of this paper includes two aspects. First, we study the lower bound complexity for the minimax optimization problem whose objective function is the average of n individual smooth component functions. We consider Proximal Incremental First-order (PIFO) algorithms which have access to gradient and proximal oracle for each individual component. We develop a novel approach for constructing adversarial problems, which partitions the tridiagonal matrix of classical examples into n groups. This construction is friendly to the analysis of incremental gradient and proximal oracle. With this approach, we demonstrate the lower bounds of first-order algorithms for finding an ε-suboptimal point and an ε-stationary point in different settings. Second, we also derive the lower bounds of minimization optimization with PIFO algorithms from our approach, which can cover the results in [35] and improve the results in [41].
Spatio-Temporal Bayesian Learning for Mobile Edge Computing Resource Planning in Smart Cities

A smart city improves operational efficiency and comfort of living by harnessing techniques such as the Internet of Things (IoT) to collect and process data for decision-making. To better support smart cities, data collected by IoT should be stored and processed appropriately. However, IoT devices are often task-specialized and resource-constrained, and thus, they heavily rely on online resources in terms of computing and storage to accomplish various tasks. Moreover, these cloud-based solutions often centralize the resources and are far away from the end IoTs and cannot respond to users in time due to network congestion when massive numbers of tasks offload through the core network. Therefore, by decentralizing resources spatially close to IoT devices, mobile edge computing (MEC) can reduce latency and improve service quality for a smart city, where service requests can be fulfilled in proximity. As the service demands exhibit spatial-temporal features, deploying MEC servers at optimal locations and allocating MEC resources play an essential role in efficiently meeting service requirements in a smart city. In this regard, it is essential to learn the distribution of resource demands in time and space. In this work, we first propose a spatio-temporal Bayesian hierarchical learning approach to learn and predict the distribution of MEC resource demand over space and time to facilitate MEC deployment and resource management. Second, the proposed model is trained and tested on real-world data, and the results demonstrate that the proposed method can achieve very high accuracy. Third, we demonstrate an application of the proposed method by simulating task offloading. Finally, the simulated results show that resources allocated based upon our models’ predictions are exploited more efficiently than the resources are equally divided into all servers in unobserved areas.
Learning developmental mode dynamics from single-cell trajectories

Embryogenesis is a multiscale process during which developmental symmetry breaking transitions give rise to complex multicellular organisms. Recent advances in high-resolution live-cell microscopy provide unprecedented insights into the collective cell dynamics at various stages of embryonic development. This rapid experimental progress poses the theoretical challenge of translating high-dimensional imaging data into predictive low-dimensional models that capture the essential ordering principles governing developmental cell migration in complex geometries. Here, we combine mode decomposition ideas that have proved successful in condensed matter physics and turbulence theory with recent advances in sparse dynamical systems inference to realize a computational framework for learning quantitative continuum models from single-cell imaging data. Considering pan-embryo cell migration during early gastrulation in zebrafish as a widely studied example, we show how cell trajectory data on a curved surface can be coarse-grained and compressed with suitable harmonic basis functions. The resulting low-dimensional representation of the collective cell dynamics enables a compact characterization of developmental symmetry breaking and the direct inference of an interpretable hydrodynamic model, which reveals similarities between pan-embryo cell migration and active Brownian particle dynamics on curved surfaces. Due to its generic conceptual foundation, we expect that mode-based model learning can help advance the quantitative biophysical understanding of a wide range of developmental structure formation processes.
Iterative Reweighted Algorithms for Joint User Identification and Channel Estimation in Spatially Correlated Massive MTC

Joint user identification and channel estimation (JUICE) is a main challenge in grant-free massive machine-type communications (mMTC). The sparse pattern in users’ activity allows to solve the JUICE as a compressed sensing problem in a multiple measurement vector (MMV) setup. This paper addresses the JUICE under the practical spatially correlated fading channel. We formulate the JUICE as an iterative reweighted ℓ2,1-norm optimization. We develop a computationally efficient alternating direction method of multipliers (ADMM) approach to solve it. In particular, by leveraging the second-order statistics of the channels, we reformulate the JUICE problem to exploit the covariance information and we derive its ADMM-based solution. The simulation results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances.
Reactive Plasma Deposited Si‐N Films for MOS‐LSI Passivation

Amorphous Si‐N films have been synthesized from and by reactive plasma deposition at 275°C in an improved radial flow reactor. With appropriate control of machine and process variables, films have been made with Si/N ratio of 0.75–1.5, density of 2.8‐2.2g cm−3, refractive index of 1.9–2.3, stress of compressive to tensile, and electrical resistivity at of 1020‐104 Ω‐cm. The process is MOS compatible and it produces relatively thick (1 μm) crack‐resistant Si‐N films (at 450°–500°C) having excellent step coverage and good adhesion to both Au and Al metallization. This paper describes the deposition technique, effect of deposition parameters on various film properties, and the advantages these films offer in silicon integrated circuit technology.