Miracle-dz/UniFAR-FTUs huggingface dataset:
I Want This Product but Different : Multimodal Retrieval with Synthetic Query Expansion

This paper addresses the problem of media retrieval using a multimodal query (a query which combines visual input with additional semantic information in natural language feedback). We propose a SynthTriplet GAN framework which resolves this task by expanding the multimodal query with a synthetically generated image that captures semantic information from both image and text input. We introduce a novel triplet mining method that uses a synthetic image as an anchor to directly optimize for embedding distances of generated and target images. We demonstrate that apart from the added value of retrieval illustration with synthetic image with the focus on customization and user feedback, the proposed method greatly surpasses other multimodal generation methods and achieves state of the art results in the multimodal retrieval task. We also show that in contrast to other retrieval methods, our method provides explainable embeddings.
Searching for Search Errors in Neural Morphological Inflection

Neural sequence-to-sequence models are currently the predominant choice for language generation tasks. Yet, on word-level tasks, exact inference of these models reveals the empty string is often the global optimum. Prior works have speculated this phenomenon is a result of the inadequacy of neural models for language generation. However, in the case of morphological inflection, we find that the empty string is almost never the most probable solution under the model. Further, greedy search often finds the global optimum. These observations suggest that the poor calibration of many neural models may stem from characteristics of a specific subset of tasks rather than general ill-suitedness of such models for language generation.
Efficient Maintenance of Distance Labelling for Incremental Updates in Large Dynamic Graphs

Finding the shortest path distance between an arbitrary pair of vertices is a fundamental problem in graph theory. A tremendous amount of research has been successfully attempted on this problem, most of which is limited to static graphs. Due to the dynamic nature of real-world networks, there is a pressing need to address this problem for dynamic networks undergoing changes. In this paper, we propose an \emph{online incremental} method to efficiently answer distance queries over very large dynamic graphs. Our proposed method incorporates incremental update operations, i.e. edge and vertex additions, into a highly scalable framework of answering distance queries. We theoretically prove the correctness of our method and the preservation of labelling minimality. We have also conducted extensive experiments on 12 large real-world networks to empirically verify the efficiency, scalability, and robustness of our method.
A Graph Neural Network to Model User Comfort in Robot Navigation

Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to minimise the disruption caused to humans while moving. This implies predicting how people will move and complying with social conventions. Avoiding disrupting personal spaces, people’s paths and interactions are examples of these social conventions. This paper leverages Graph Neural Networks to model robot disruption considering the movement of the humans and the robot so that the model built can be used by path planning algorithms. Along with the model, this paper presents an evolution of the dataset SocNav1 [25] which considers the movement of the robot and the humans, and an updated scenario-to-graph transformation which is tested using diﬀerent Graph Neural Network blocks. The model trained achieves close-to-human performance in the dataset. In addition to its accuracy, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered in comparison with handcrafted models. The dataset and the model are available in a public repository 1 .
Euclid: Effects of sample covariance on the number counts of galaxy clusters

Aims. We investigate the contribution of shot-noise and sample variance to the uncertainty of cosmological parameter constraints inferred from cluster number counts in the context of the Euclid survey. Methods. By analysing 1000 Euclid-like light-cones, produced with the PINOCCHIO approximate method, we validate the analytical model of Hu & Kravtsov 2003 for the covariance matrix, which takes into account both sources of statistical error. Then, we use such covariance to define the likelihood function that better extracts cosmological information from cluster number counts at the level of precision that will be reached by the future Euclid photometric catalogs of galaxy clusters. We also study the impact of the cosmology dependence of the covariance matrix on the parameter constraints. Results. The analytical covariance matrix reproduces the variance measured from simulations within the 10 per cent level; such difference has no sizeable effect on the error of cosmological parameter constraints at this level of statistics. Also, we find that the Gaussian likelihood with cosmology-dependent covariance is the only model that provides an unbiased inference of cosmological parameters without underestimating the errors.
Constraints on power law cosmology from cosmic chronometer, standard ruler, and standard candle data

In this paper I investigate how well simple power law expansion fits observational data in comparison to the standard ΛCDM model. I analyze a data set consisting of cosmic chronometer, standard ruler, and standard candle measurements, finding that the ΛCDM model provides a better fit to most combinations of these data than the power law ansatz.
Theory and Functionals in the Hypercomplex Kohn-Sham DFT Method

The static/strong correlation error inherent in existing functionals has severely limited the further application of Kohn-Sham (KS) density functional theory (DFT). The recently developed hypercomplex KS (HCKS) theory shows great potential to overcome the fundamental limitations of the conventional KS-DFT, hence further development and application of HCKS will effectively guide the construction of new functionals toward better description of strong correlation. To this end, this work derives the working equations for the HCKS calculation and proves that HCKS using common functionals has much lower computational complexity than reduced density matrix functional theory (RDMFT), while maintaining the same density search space as RDMFT. Moreover, this work combines HCKS with hybrid functionals that are based on orbitals and their occupations. The test on triplet-singlet gaps shows that the systematic error of HCKS due to the lack of static correlation in approximate functionals can be effectively reduced by including appropriate amount of static correlation correction, while the large systematic errors of RKS and UKS rarely change with the correction. Therefore, HCKS creates new channels for the development and evaluation of approximate functionals, and the insights and advances gained in this work will be helpful to reduce the static correlation error in approximate functionals.
Non-asymptotic approximations of neural networks by Gaussian processes

We study the extent to which wide neural networks may be approximated by Gaussian processes when initialized with random weights. It is a well-established fact that as the width of a network goes to infinity, its law converges to that of a Gaussian process. We make this quantitative by establishing explicit convergence rates for the central limit theorem in an infinite-dimensional functional space, metrized with a natural transportation distance. We identify two regimes of interest; when the activation function is polynomial, its degree determines the rate of convergence, while for non-polynomial activations, the rate is governed by the smoothness of the function.
(-k)-critical Trees and K-minimal Trees

In a graph $G=(V,E)$, a module is a vertex subset $M$ of $V$ such that every vertex outside $M$ is adjacent to all or none of $M$. For example, $\emptyset$, $\{x\}$ $(x\in V )$ and $V$ are modules of $G$, called trivial modules. A graph, all the modules of which are trivial, is prime; otherwise, it is decomposable. A vertex $x$ of a prime graph $G$ is critical if $G - x$ is decomposable. Moreover, a prime graph with $k$ non-critical vertices is called $(-k)$-critical graph. A prime graph $G$ is $k$-minimal if there is some $k$-vertex set $X$ of vertices such that there is no proper induced subgraph of $G$ containing $X$ is prime. From this perspective, I. Boudabbous proposes to find the $(-k)$-critical graphs and $k$-minimal graphs for some integer $k$ even in a particular case of graphs. This research paper attempts to answer I. Boudabbous's question. First, it describes the $(-k)$-critical tree. As a corollary, we determine the number of nonisomorphic $(-k)$-critical tree with $n$ vertices where $k\in \{1,2,\lfloor\frac{n}{2}\rfloor\}$. Second, it provide a complete characterization of the $k$-minimal tree. As a corollary, we determine the number of nonisomorphic $k$-minimal tree with $n$ vertices where $k\leq 3$.
Extreme r-process Enhanced Stars at High Metallicity in Fornax

We present and discuss three extremely r-process enhanced stars located in the massive dwarf spheroidal galaxy Fornax. These stars are very unique with an extreme Eu enrichment (1.25 ≤ [Eu/Fe]≤1.45) at high metallicities (−1.3 ≤ [Fe/H]≤−0.8). They have the largest Eu abundances ever observed in a dwarf galaxy opening new opportunities to further understand the origin of heavy elements formed by the r-process. We derive stellar abundances of Co, Zr, La, Ce, Pr, Nd, Er, and Lu using one-dimensional, local thermodynamic equilibrium codes and model atmospheres in conjunction with state-of-the art yield predictions. We derive Zr in the largest sample of stars (105) known to date in a dwarf galaxy. Accurate stellar abundances combined with a careful assessment of the yield predictions have revealed three metal-rich stars in Fornax showing a pure r-process pattern. We define a new class of stars, namely, Eu-stars, as r-II stars (i.e., [Eu/Fe] > 1) at high metallicities (i.e., [Fe/H] ≳ −1.5). The stellar abundance pattern contains Lu, observed for the first time in a dwarf galaxy, and reveals that a late burst of star formation has facilitated extreme r-process enhancement late in the galaxy’s history (<4 Gyr ago). Due to the large uncertainties associated with the nuclear physics input in the yield predictions, we cannot yet determine the r-process site leading to the three Eu-stars in Fornax. Our results demonstrate that extremely r-rich stars are not only associated with ultra-faint low-mass dwarf galaxies, but can be born also in massive dwarf galaxies.
Oscars: Adaptive Semi-Synchronous Parallel Model for Distributed Deep Learning with Global View

Deep learning has become an indispensable part of life, such as face recognition, NLP, etc., but the training of deep model has always been a challenge, and in recent years, the complexity of training data and models has shown explosive growth, so the training method is gradually transformed into distributed training. Classical synchronization strategy can guarantee accuracy but frequent communication can lead to a slow training speed, although asynchronous strategy training speed but can not guarantee the accuracy, and in the face of the training of the heterogeneous cluster, the above work is not efficient to work, on the one hand, can cause serious waste of resources, on the other hand, frequent communication also made slow training speed, so this paper proposes a semi-synchronous training strategy based on local-SDG, effectively improve the utilization efficiency of heterogeneous resources cluster and reduce communication overhead, to accelerate the training and ensure the accuracy of the model.
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training

Self-training is a standard approach to semi-supervised learning where the learner's own predictions on unlabeled data are used as supervision during training. In this paper, we reinterpret this label assignment process as an optimal transportation problem between examples and classes, wherein the cost of assigning an example to a class is mediated by the current predictions of the classifier. This formulation facilitates a practical annealing strategy for label assignment and allows for the inclusion of prior knowledge on class proportions via flexible upper bound constraints. The solutions to these assignment problems can be efficiently approximated using Sinkhorn iteration, thus enabling their use in the inner loop of standard stochastic optimization algorithms. We demonstrate the effectiveness of our algorithm on the CIFAR-10, CIFAR-100, and SVHN datasets in comparison with FixMatch, a state-of-the-art self-training algorithm. Our code is available at https://github.com/stanford-futuredata/sinkhorn-label-allocation.
A three‐dimensional hybrid finite element — spectral boundary integral method for modeling earthquakes in complex unbounded domains

We present a 3D hybrid method which combines the finite element method (FEM) and the spectral boundary integral method (SBIM) to model nonlinear problems in unbounded domains. The flexibility of FEM is used to model the complex, heterogeneous, and nonlinear part— such as the dynamic rupture along a fault with near fault plasticity—and the high accuracy and computational efficiency of SBIM is used to simulate the exterior half spaces perfectly truncating all incident waves. The exact truncation allows us to greatly reduce the domain of spatial discretization compared to a traditional FEM approach, leading to considerable savings in computational time and memory requirements. The coupling of FEM and SBIM is achieved by the exchange of traction and displacement boundary conditions at the computationally defined boundary. The method is suited to implementation on massively parallel computers. We validate the developed method by means of a benchmark problem. Three more complex examples with a low velocity fault zone, low velocity off‐fault inclusion, and interaction of multiple faults, respectively, demonstrate the capability of the hybrid scheme in solving problems of very large sizes. Finally, we discuss potential applications of the hybrid method for problems in geophysics and engineering.
Inhomogeneous dust eclipses in young stars: the case of CQ Tauri

We find that dust clouds which eclipse young stars obscure the stellar disc inhomogeneously. In the particular case of CQ Tau, we find isolated optically thick structures with sizes $\lesssim0. 6R_*$ and derive the typical $A_{V}$ gradient in the plane of the sky, finding it as high as a few magnitudes per stellar radius. The large extinction gradients and complex structure of the obscuring clouds lead not only to a noticeable Rossiter--McLaughlin effect, but also to complex and variable shaping of stellar absorption lines.
Time Matters in Using Data Augmentation for Vision-based Deep Reinforcement Learning

Data augmentation technique from computer vision has been widely considered as a regularization method to improve data efﬁciency and generalization performance in vision-based reinforcement learning. We variate the timing of using augmentation, which is, in turn, critical depending on tasks to be solved in training and testing. According to our experiments on Open AI Proc-gen Benchmark, if the regularization imposed by augmentation is helpful only in testing, it is better to procrastinate the augmentation after training than to use it during training in terms of sample and computation complexity. We note that some of such augmentations can disturb the training process. Conversely, an augmentation providing regularization useful in training needs to be used during the whole training period to fully utilize its beneﬁt in terms of not only generalization but also data efﬁciency. These phenomena suggest a useful timing control of data augmentation in reinforcement learning.
Ensemble Transfer Learning of Elastography and B-mode Breast Ultrasound Images

Computer-aided detection (CAD) of benign and malignant breast lesions becomes increasingly essential in breast ultrasound (US) imaging. The CAD systems rely on imaging features identified by the medical experts for their performance, whereas deep learning (DL) methods automatically extract features from the data. The challenge of the DL is the insufficiency of breast US images available to train the DL models. Here, we present an ensemble transfer learning model to classify benign and malignant breast tumors using B-mode breast US (B-US) and strain elastography breast US (SE-US) images. This model combines semantic features from AlexNet&ResNet models to classify benign from malignant tumors. We use both B-US and SE-US images to train the model and classify the tumors. We retrospectively gathered 85 patients' data, with 42 benign and 43 malignant cases confirmed with the biopsy. Each patient had multiple B-US and their corresponding SE-US images, and the total dataset contained 261 B-US images and 261 SE-US images. Experimental results show that our ensemble model achieves a sensitivity of 88.89% and specificity of 91.10%. These diagnostic performances of the proposed method are equivalent to or better than manual identification. Thus, our proposed ensemble learning method would facilitate detecting early breast cancer, reliably improving patient care.
Murat: Multi-RAT False Base Station Detector

In recent years, there has been an increasing interest in false base station detection systems. Most of these rely on software that users download into their mobile phones. The software either performs an analysis of radio environment measurements taken by the mobile phone or reports these measurements to a server on the Internet, which then analyzes the aggregated measurements collected from many mobile phones. These systems suffer from two main drawbacks. First, they require modification to the mobile phones in the form of software and an active decision to participate from users. This severely limits the number of obtained measurements. Second, they do not make use of the information the mobile network has regarding network topology and configuration. This results in less reliable predictions than could be made. We present a network-based system for detecting false base stations that operate on any 3GPP radio access technology, without requiring modifications to mobile phones, and that allows taking full advantage of network topology and configuration information available to an operator. The analysis is performed by the mobile network based on measurement reports delivered by mobile phones as part of normal operations to maintain the wireless link. We implemented and validated the system in a lab experiment and a real operator trial. Our approach was adopted by the 3GPP standardization organization.
Poor Work Efficiency is Associated with Poor Exercise Capacity and Health-Related Quality of Life in Patients with Chronic Obstructive Pulmonary Disease

Introduction Chronic obstructive pulmonary disease (COPD) is a progressive disease with deteriorating cardiopulmonary function that decreases the health-related quality of life (HRQL) and exercise capacity. Patients with COPD often have cardiovascular and muscular problems that hinder oxygen uptake by peripheral tissues, resulting in poor oxygen consumption efficiency. It is important to develop new physiological parameters to evaluate oxygen consumption efficiency during activities and to evaluate its association with exercise capacity and HRQL. Work efficiency (WE) measures oxygen consumption efficiency during exercise. We hypothesize that patients with poor WE should have exercise intolerance and poor HRQL. Therefore, we aimed to evaluate the association between WE and exercise capacity, HRQL and other cardiopulmonary parameters. Patients and Methods Seventy-eight patients with COPD were evaluated with spirometry, cardiopulmonary exercise testing, and assessment of dyspnea score and HRQL (using the St. George’s Respiratory Questionnaire [SGRQ]). Cardiopulmonary exercise testing was performed using a cycle ergometer with an incremental protocol and exhaled breath analysis to assess oxygen consumption. WE was defined as the relationship between oxygen consumption and workload. Results There were 31 patients with normal WE (group I) and 47 patients (group II) with poor WE. Patients with poor WE had lower exercise capacity (maximal oxygen consumption, group I vs II as 1050±53 vs 845 ±34 mL/min, p=0.0011), poorer HRQL (SGRQ score 41.1±3.0 vs 55±2.2, p=0.0002), higher exertional dyspnea score (5.1±0.2 vs 6.1±0.2, p= 0.0034) and early anaerobic metabolism during exercise (anaerobic threshold, 672±27 vs 583 ±18 mL/min, p=0.0052). Conclusion WE is associated with exercise capacity and HRQL. Here, patients with poor WE also had exercise intolerance, poorer HRQL, and more exertional dyspnea.
Does Napping Enhance the Consolidation of Clinically Relevant Information? A Comparison of Individuals with Low and Elevated Depressive Symptoms

Purpose Sleep, both overnight and daytime naps, can facilitate the consolidation of declarative memories in healthy humans. However, it is unclear whether such beneficial effects of sleep occur in special populations, such as individuals with elevated neuropsychiatric symptoms, and if they apply to clinically relevant material that may have personal significance to those populations. Methods We examined memory retention over a 60-minute interval of wakefulness or nap opportunity in participants with low or elevated scores (≤13 and ≥21, respectively) on the Beck Depression Inventory-II (BDI-II). Memory for depression-related information was assessed by (a) free-recall of a video depicting a personal experience narrative of the impact of depression on cognition and workplace performance; and (b) a paired-associates task linking depression-related cognitive symptoms to appropriate coping strategies. Results The results showed no overall difference in recall between the nap and waking condition. However, across the full sample of participants, there were significant positive correlations between total sleep time and paired associates recall, and slow wave sleep (SWS) percentage and story free recall performance. Unexpectedly, participants with elevated BDI-II scores exhibited better free-recall performance compared to those with low scores. Conclusion These results suggest that sleep, specifically SWS, may stabilize memories for clinically relevant information in populations with low and elevated depressive symptoms. The superior recall in participants with elevated-BDI scores may be related to the personal significance and stronger encoding of depression-related information. These observations raise the possibility that mnemonic deficits in depressed patients may be, at least in part, related to the type of information used to assess memory performance.