Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral States of OJ 287 blazar from Multi-wavelength Observations with $AstroSat$

We present AstroSat soft X-ray, near-UV (NUV) and far-UV (FUV) observations of a blazar, OJ 287, carried out in 2017, 2018, and 2020. The simultaneous observations with NuSTAR in 2017 provide a broad-band look encompassing NUV, FUV, soft and hard X-rays. Captured in three different broadband spectral states in three observations, the X-ray spectrum is found to be the hardest during 2018, while the high-energy-end of the simultaneous optical-FUV spectrum shows a steepening that is modeled with a broken power-law spectrum. The spectral energy distribution (SED) in 2017 shows a relatively flatter optical-FUV and soft X-ray spectra, implying an additional emission component. The 2020 optical-FUV spectrum is harder than in 2017 and 2018, with an extremely soft X-ray spectrum and a hardening above ∼1 GeV, similar to the SEDs of High-energy-peaked BL Lac objects (HBL), thereby establishing that this additional emission component has HBL-like properties. The AstroSat multi-wavelength observations trace the spectral evolution from the end-phase of the HBL component in 2017 to its disappearance in 2018 followed by its revival in 2020. A single zone leptonic model reproduces the 2018 broadband spectrum while the 2017 and 2020 SEDs require an additional HBL-like emitting zone. The spectral evolution of the high-energy-end of optical-UV spectrum, revealed by the FUV observations in 2017 and 2018, strongly suggests that X-ray spectral changes in the normal broadband spectral state of OJ 287 are primarily due to the evolution of the optical-UV synchrotron spectrum.
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning

Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms. However, by focusing on item relevance, one pays a significant price in terms of other important metrics: users get stuck in a "filter bubble" and their array of options is significantly reduced, hence degrading the quality of the user experience and leading to churn. Recommendation, and in particular session-based/sequential recommendation, is a complex task with multiple - and often conflicting objectives - that existing state-of-the-art approaches fail to address. In this work, we take on the aforementioned challenge and introduce Scalarized Multi-Objective Reinforcement Learning (SMORL) for the RS setting, a novel Reinforcement Learning (RL) framework that can effectively address multi-objective recommendation tasks. The proposed SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations. We integrate this framework with four state-of-the-art session-based recommendation models and compare it with a single-objective RL agent that only focuses on accuracy. Our experimental results on two real-world datasets reveal a substantial increase in aggregate diversity, a moderate increase in accuracy, reduced repetitiveness of recommendations, and demonstrate the importance of reinforcing diversity and novelty as complementary objectives.
Effects of counter-rotating-wave terms on the noisy frequency estimation

We investigate the problem of estimating the tunneling frequency of a two-level atomic system embedded in a dissipative environment by employing a numerically rigorous hierarchical equations of motion method. The eﬀect of counter-rotating-wave terms on the attainable precision of the noisy quantum metrology is systematically studied beyond the usual framework of perturbative treat-ments. We ﬁnd the counter-rotating-wave terms are able to boost the noisy quantum metrological performance in the intermediate and strong coupling regimes, whether the dissipative environment is composed of bosons or fermions. The result presented in this paper may pave a guideline to design a high-precision quantum estimation scenario under practical decoherence.
Dist2Cycle: A Simplicial Neural Network for Homology Localization

Simplicial complexes can be viewed as high dimensional generalizations of graphs that explicitly encode multi-way ordered relations between vertices at different resolutions, all at once. This concept is central towards detection of higher dimensional topological features of data, features to which graphs, encoding only pairwise relationships, remain oblivious. While attempts have been made to extend Graph Neural Networks (GNNs) to a simplicial complex setting, the methods do not inherently exploit, or reason about, the underlying topological structure of the network. We propose a graph convolutional model for learning functions parametrized by the k-homological features of simplicial complexes. By spectrally manipulating their combinatorial k-dimensional Hodge Laplacians, the proposed model enables learning topological features of the underlying simplicial complexes, specifically, the distance of each k-simplex from the nearest "optimal" k-th homology generator, effectively providing an alternative to homology localization.
DocScanner: Robust Document Image Rectification with Progressive Learning

Compared with flatbed scanners, portable smartphones provide more convenience for physical document digitization. However, such digitized documents are often distorted due to uncontrolled physical deformations, camera positions, and illumination variations. To this end, we present DocScanner, a novel framework for document image rectification. Different from existing solutions, DocScanner addresses this issue by introducing a progressive learning mechanism. Specifically, DocScanner maintains a single estimate of the rectified image, which is progressively corrected with a recurrent architecture. The iterative refinements make DocScanner converge to a robust and superior rectification performance, while the lightweight recurrent architecture ensures the running efficiency. To further improve the rectification quality, based on the geometric priori between the distorted and the rectified images, a geometric regularization is introduced during training to further improve the performance. Extensive experiments are conducted on the Doc3D dataset and the DocUNet Benchmark dataset, and the quantitative and qualitative evaluation results verify the effectiveness of DocScanner, which outperforms previous methods on OCR accuracy, image similarity, and our proposed distortion metric by a considerable margin. Furthermore, our DocScanner shows superior efficiency in runtime latency and model size.
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation

With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains. The core of GCNs lies in its message passing mechanism to aggregate neighborhood information. However, we observed that message passing largely slows down the convergence of GCNs during training, especially for large-scale recommender systems, which hinders their wide adoption. LightGCN makes an early attempt to simplify GCNs for collaborative filtering by omitting feature transformations and nonlinear activations. In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation. Instead of explicit message passing, UltraGCN resorts to directly approximate the limit of infinite-layer graph convolutions via a constraint loss. Meanwhile, UltraGCN allows for more appropriate edge weight assignments and flexible adjustment of the relative importances among different types of relationships. This finally yields a simple yet effective UltraGCN model, which is easy to implement and efficient to train. Experimental results on four benchmark datasets show that UltraGCN not only outperforms the state-of-the-art GCN models but also achieves more than 10x speedup over LightGCN.
xDEEP-MSI: Explainable Bias-Rejecting Microsatellite Instability Deep Learning System in Colorectal Cancer

The prediction of microsatellite instability (MSI) in colorectal cancer (CRC) using deep learning (DL) techniques directly from hematoxylin and eosin stained slides (H&E) has been shown feasible by independent works. Nonetheless, when available, relevant information from clinical, oncological and family history could be used to further inform DL predictions. The present work analyzes the effects from leveraging multimodal inputs and multitask supervision in a previously published DL system for the prediction of MSI in CRC (xDEEP-MSI). xDEEP-MSI was a multiple bias rejecting DL system based on adversarial networks trained and validated in 1788 patients from a total of 25 participating centers from EPICOLON and HGUA projects. In the present work, xDEEP-MSI is further enriched with weakly supervised learning in multiple molecular alterations (MSI status, K-RAS and BRAF mutations and Lynch Syndrome confirmed by germline mutations), adapted to multimodal inputs with variable degree of completeness (image, age, gender, localization of CRC, revised Bethesda criteria, Amsterdam II criteria and additional oncological history) and a self-supervised multiple instance learning that integrates multiple image-tiles, to obtain patient-level predictions. The AUC, including all three selected tissues (tumor epithelium, mucin and lymphocytic regions) and 5 magnifications, increases from 0.9 +/- 0.03, to 0.94 +/- 0.02. The sensibility and specificity reaches 92.5% 95%CI(79.6-98.4%) and 93.4% 95%CI(90.0-95.8%) respectively. To the best of our knowledge this is the first work that jointly uses multimodal inputs, multiple instance learning and multiple molecular supervision for the prediction of MSI in CRC from H&E, demonstrating their gains in performance. Prospective validation in an external independent dataset is still required.
Signaling β Transforming Growth Factor Sites Reveals Roles of ETS 1 and TFAP 2 A in Microarray Analysis of Smad 2 / 3 Binding Chromatin Immunoprecipitation on

Published Ahead of Print 27 October 2008. 2009, 29(1):172. DOI: 10.1128/MCB.01038-08. Mol. Cell. Biol. Takeshi Imamura, Kohei Miyazono and Hiroyuki Aburatani Hirokazu Taniguchi, Keiji Miyazawa, Makoto Sunamura, Daizo Koinuma, Shuichi Tsutsumi, Naoko Kamimura, Signaling β Transforming Growth Factor Sites Reveals Roles of ETS1 and TFAP2A in Microarray Analysis of Smad2/3 Binding Chromatin Immunoprecipitation on
Approximating the Arboricity in Sublinear Time

We consider the problem of approximating the arboricity of a graph $G= (V,E)$, which we denote by $\mathsf{arb}(G)$, in sublinear time, where the arboricity of a graph is the minimal number of forests required to cover its edges. An algorithm for this problem may perform degree and neighbor queries, and is allowed a small error probability. We design an algorithm that outputs an estimate $\hat{\alpha}$, such that with probability $1-1/\textrm{poly}(n)$, $\mathsf{arb}(G)/c\log^2 n \leq \hat{\alpha} \leq \mathsf{arb}(G)$, where $n=|V|$ and $c$ is a constant. The expected query complexity and running time of the algorithm are $O(n/\mathsf{arb}(G))\cdot \textrm{poly}(\log n)$, and this upper bound also holds with high probability. %($\widetilde{O}(\cdot)$ is used to suppress $\textrm{poly}(\log n)$ dependencies). This bound is optimal for such an approximation up to a $\textrm{poly}(\log n)$ factor.
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations

Feature importance (FI) estimates are a popular form of explanation, and they are commonly created and evaluated by computing the change in model confidence caused by removing certain input features at test time. For example, in the standard Sufficiency metric, only the top-k most important tokens are kept. In this paper, we study several under-explored dimensions of FI explanations, providing conceptual and empirical improvements for this form of explanation. First, we advance a new argument for why it can be problematic to remove features from an input when creating or evaluating explanations: the fact that these counterfactual inputs are out-of-distribution (OOD) to models implies that the resulting explanations are socially misaligned. The crux of the problem is that the model prior and random weight initialization influence the explanations (and explanation metrics) in unintended ways. To resolve this issue, we propose a simple alteration to the model training process, which results in more socially aligned explanations and metrics. Second, we compare among five approaches for removing features from model inputs. We find that some methods produce more OOD counterfactuals than others, and we make recommendations for selecting a feature-replacement function. Finally, we introduce four search-based methods for identifying FI explanations and compare them to strong baselines, including LIME, Anchors, and Integrated Gradients. Through experiments with six diverse text classification datasets, we find that the only method that consistently outperforms random search is a Parallel Local Search (PLS) that we introduce. Improvements over the second-best method are as large as 5.4 points for Sufficiency and 17 points for Comprehensiveness. All supporting code for experiments in this paper is publicly available at https://github.com/peterbhase/ExplanationSearch.
aggressive B-cell lymphomas associated with a favorable cancer-specific survival in patients with NR 4 A 1-mediated apoptosis suppresses lymphomagenesis and

Beham-Schmid and Peter Neumeister Prokesch, Marcel Scheideler, Anne Krogsdam, Gerhard G. Thallinger, Helmut Schaider, Christine Steinbauer, Daniela Schwarzenbacher, Sonja Reitter, Julia Feichtinger, Sascha Tierling, Andreas Alexander J. A. Deutsch, Beate Rinner, Kerstin Wenzl, Martin Pichler, Katharina Troppan, Elisabeth aggressive B-cell lymphomas associated with a favorable cancer-specific survival in patients with NR4A1-mediated apoptosis suppresses lymphomagenesis and is
Contrastive Learning of Global-Local Video Representations

Contrastive learning has delivered impressive results for various tasks in the self-supervised regime. However, existing approaches optimize for learning representations specific to downstream scenarios, i.e., \textit{global} representations suitable for tasks such as classification or \textit{local} representations for tasks such as detection and localization. While they produce satisfactory results in the intended downstream scenarios, they often fail to generalize to tasks that they were not originally designed for. In this work, we propose to learn video representations that generalize to both the tasks which require global semantic information (e.g., classification) and the tasks that require local fine-grained spatio-temporal information (e.g., localization). We achieve this by optimizing two contrastive objectives that together encourage our model to learn global-local visual information given audio signals. We show that the two objectives mutually improve the generalizability of the learned global-local representations, significantly outperforming their disjointly learned counterparts. We demonstrate our approach on various tasks including action/sound classification, lip reading, deepfake detection, event and sound localization (https://github.com/yunyikristy/global\_local).
Monocular multi-layer layout estimation for warehouse racks

Given a monocular color image of a warehouse rack, we aim to predict the bird's-eye view layout for each shelf in the rack, which we term as 'multi-layer' layout prediction. To this end, we present RackLay, a deep neural network for real-time shelf layout estimation from a single image. Unlike previous layout estimation methods which provide a single layout for the dominant ground plane alone, RackLay estimates the top-view and front-view layout for each shelf in the considered rack populated with objects. RackLay's architecture and its variants are versatile and estimate accurate layouts for diverse scenes characterized by varying number of visible shelves in an image, large range in shelf occupancy factor and varied background clutter. Given the extreme paucity of datasets in this space and the difficulty involved in acquiring real data from warehouses, we additionally release a flexible synthetic dataset generation pipeline WareSynth which allows users to control the generation process and tailor the dataset according to the contingent application. The ablations across architectural variants and comparison with strong prior baselines vindicate the efficacy of RackLay as an apt architecture for the novel problem of multi-layered layout estimation. We also show that fusing the top-view and front-view enables 3D reasoning applications such as metric free space estimation for the considered rack.
Using PPP Information to Implement a Global Real-Time Virtual Network DGNSS Approach

Differential GNSS (DGNSS) has been demonstrated to provide reliable, high-quality range correction information enabling real-time navigation with centimeter to sub-meter accuracy, which is required for applications such as connected and autonomous vehicles. However, DGNSS requires a local reference station near each user. For a continental or global scale implementation, this information dissemination approach would require a dense network of reference stations whose construction and maintenance would be prohibitively expensive. Precise Point Positioning (PPP) affords more flexibility as a public service for Global Navigation Satellite Systems (GNSS) receivers, but its State Space Representation (SSR) format is not supported by most receivers in the field or on the market. This article proposes a novel Virtual Network DGNSS (VNDGNSS) approach and an optimization algorithm that is key to its implementation. The approach capitalizes on the existing PPP infrastructure without the need for new physical reference stations. Specifically, no reference station is needed in the local vicinity of any user. By connecting to public GNSS SSR data services, a VNDGNSS server maintains current information about satellite code bias, satellite orbit and clock, and atmospheric models. Construction of the Radio Technical Commission for Maritime Services (RTCM) Observation Space Representation (OSR) messages from this SSR information requires both the signal time-of-transmission and the satellite position at that time which are consistent with the time-of-reception for each client. This article presents an algorithm to determine these quantities. Then the VNDGNSS computes and transmits RTCM OSR messages to user receivers. This approach achieves global dissemination coverage for real-time navigation without the need for additional local base stations near each user. The results of real-time stationary and moving platform evaluations are included, using u-blox M8P and ZED-F9P receivers. The performance surpasses the Society of Automotive Engineering (SAE) specification (68% of horizontal error $\leqslant$ 1.5 m and vertical error $\leqslant$ 3 m) and shows significantly better horizontal performance than GNSS Open Service (OS). The moving tests also show better horizontal performance than the ZED-F9P receiver with Satellite Based Augmentation Systems (SBAS) enabled and achieve the lane-level accuracy (95% of horizontal errors less than 1 m).
Hadroproduction of four top quarks in the powheg box

We present a new Monte Carlo event generator for the hadronic production of four top quarks in the POWHEG BOX framework. Besides the dominant next-to-leading order QCD corrections at $\mathcal{O}(\alpha_s^5)$ we also include all subleading electroweak productions channels at leading-order accuracy. We validate our theoretical predictions by comparing to parton-shower matched predictions obtained within the MC@NLO framework for stable top quarks. Furthermore, we investigate in detail the various sources of theoretical uncertainties. Finally, we investigate a single lepton plus jets signature to study for the first time the impact of the electroweak production modes as well as spin-correlation effects at the fiducial level.
Emoji-aware Co-attention Network with EmoGraph2vec Model for Sentiment Anaylsis

In social media platforms, emojis have an extremely high occurrence in computer-mediated communications. Many emojis are used to strengthen the emotional expressions and the emojis that co-occurs in a sentence also have a strong sentiment connection. However, when it comes to emoji representation learning, most studies have only utilized the ﬁxed descriptions provided by the Unicode Consortium, without consideration of actual usage scenario. As for the sentiment analysis task, many researchers ignore the emotional impact of the interaction between text and emojis. It results that the emotional semantics of emojis cannot be fully explored. In this work, we propose a method to learn emoji representations called EmoGraph2vec and design an emoji-aware co-attention network that learns the mutual emotional semantics between text and emojis on short texts of social media. In EmoGraph2vec, we form an emoji co-occurrence network on real social data and enrich the semantic information based on an external knowledge base EmojiNet to obtain emoji node embeddings. Our model designs a co-attention mechanism to incorporate the text and emojis, and integrates a squeeze-and-excitation (SE) block into a convolutional neural network as a classiﬁer. Finally, we use the transfer learning method to increase converge speed and achieve higher accuracy. Experimental results show that the proposed model can outperform several baselines for sentiment analysis on benchmark datasets. Additionally, we conduct a series of ablation and comparison experiments to investigate the eﬀectiveness of our model.
Anisotropic pressure effects in hydrodynamic description of waves propagating parallel to the magnetic field in relativistically hot plasmas

The structure of novel hydrodynamic model of plasmas with the relativistic temperatures consisted of four equations for the material fields is presented for the regime of anisotropic pressure and other tensors describing the thermal effects. Presented model constructed of equation for evolution of the concentration, the velocity field, the average reverse relativistic γ functor, and the flux of the reverse relativistic γ functor, which are considered as main hydrodynamic variables. Four pressurelike tensors (two second rank tensors and one fourth rank tensor) describe the thermal effects. Among them we have the flux of the particle current and the current of the flux of the reverse relativistic γ functor. The high-frequency excitations are considered analytically in order to trace the contribution of the anisotropy of pressure-like tensors in their spectra.