Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamically Decoding Source Domain Knowledge For Unseen Domain Generalization

Domain generalization is an important problem which has gain much attention recently. While most existing studies focus on learning domain-invariant feature representations, some researchers try ensemble learning of multi experts and demonstrate promising performance. However, in existing multi-expert learning frameworks, the source domain knowledge has not yet been much explored, resulting in sub-optimal performance. In this paper
Application-Oriented Performance Benchmarks for Quantum Computing

In this work, we introduce an open-source suite of quantum application-oriented performance benchmarks that is designed to measure the effectiveness of quantum computing hardware at executing quantum applications. These benchmarks probe a quantum computer's performance on various algorithms and small applications as the problem size is varied, by mapping out the fidelity of the results as a function of circuit width and depth using the framework of volumetric benchmarking. In addition to estimating the fidelity of results generated by quantum execution, the suite is designed to benchmark certain aspects of the execution pipeline in order to provide end users with a practical measure of both the quality of and the time to solution. Our methodology is constructed to anticipate advances in quantum computing hardware that are likely to emerge in the next five years. This benchmarking suite is designed to be readily accessible to a broad audience of users and provides benchmarks that correspond to many well-known quantum computing algorithms.
Workload-Aware Materialization of Junction Trees

Bayesian networks are popular probabilistic models that capture the conditional dependencies among a set of variables. Inference in Bayesian networks is a fundamental task for answering probabilistic queries over a subset of variables in the data. However, exact inference in Bayesian networks is \NP-hard, which has prompted the development of many practical inference methods. In this paper, we focus on improving the performance of the junction-tree algorithm, a well-known method for exact inference in Bayesian networks. In particular, we seek to leverage information in the workload of probabilistic queries to obtain an optimal workload-aware materialization of junction trees, with the aim to accelerate the processing of inference queries. We devise an optimal pseudo-polynomial algorithm to tackle this problem and discuss approximation schemes. Compared to state-of-the-art approaches for efficient processing of inference queries via junction trees, our methods are the first to exploit the information provided in query workloads. Our experimentation on several real-world Bayesian networks confirms the effectiveness of our techniques in speeding-up query processing.
DRAFT-What you always wanted to know but could not find about block-based environments

Block-based environments are visual programming environments, which are becoming more and more popular because of their ease of use. The ease of use comes thanks to their intuitive graphical representation and structural metaphors (jigsaw-like puzzles) to display valid combinations of language constructs to the users. Part of the current popularity of block-based environments is thanks to Scratch. As a result they are often associated with tools for children or young learners. However, it is unclear how these types of programming environments are developed and used in general. So we conducted a systematic literature review on block-based environments by studying 152 papers published between 2014 and 2020, and a non-systematic tool review of 32 block-based environments. In particular, we provide a helpful inventory of block-based editors for end-users on different topics and domains. Likewise, we focused on identifying the main components of block-based environments, how they are engineered, and how they are used. This survey should be equally helpful for language engineering researchers and language engineers alike.
Heterogeneous Overdispersed Count Data Regressions via Double-Penalized Estimations

Recently, the high-dimensional negative binomial regression (NBR) for count data has been widely used in many scientific fields. However, most studies assumed the dispersion parameter as a constant, which may not be satisfied in practice. This paper studies the variable selection and dispersion estimation for the heterogeneous NBR models, which model the dispersion parameter as a function. Specifically, we proposed a double regression and applied a double ℓ1-penalty to both regressions. Under the restricted eigenvalue conditions, we prove the oracle inequalities for the lasso estimators of two partial regression coefficients for the first time, using concentration inequalities of empirical processes. Furthermore, derived from the oracle inequalities, the consistency and convergence rate for the estimators are the theoretical guarantees for further statistical inference. Finally, both simulations and a real data analysis demonstrate that the new methods are effective.
Multi-Trigger-Key: Toward Multi-Task Privacy Preserving in Deep Learning

Deep learning-based Multi-Task Classification (MTC) is widely used in applications such as facial attributes and healthcare, which require robust privacy guarantees. In this study, we propose a novel Multi-Trigger-Key (MTK) framework to fulfill the privacy-preservation objective of protecting sensitive information throughout the entire workflow of MTC. We provide two real world examples that demonstrates how MTK can be implemented in the context of healthcare and financial tasks. Each secured task in the multi-task dataset is linked to a specially crafted trigger-key, processed by a data distributor, a secret key distributor, an assembler, and a model optimizer/keeper in the MTK system. If a user is authorized to access certain data, the insertion of trigger keys will reveal the accurate information. Furthermore, the learning process is structured to allow the four MTK agents to collaboratively distribute privacy protection. To address the information leakage problem caused by correlations among different classes, MTK training also includes a tuning parameter, which is used to balance the protective efficacy and model performance. Theoretical assurances and experimental results demonstrate that privacy protection is effective without significantly compromising model performance.
Self-Supervision is All You Need for Solving Rubik's Cube

Existing combinatorial search methods are often complex and require some level of expertise. This work introduces a simple and efficient deep learning method for solving combinatorial problems with a predefined goal, represented by Rubik's Cube. We demonstrate that, for such problems, training a deep neural network on random scrambles branching from the goal state is sufficient to achieve near-optimal solutions. When tested on Rubik's Cube, 15 Puzzle, and 7$\times$7 Lights Out, our method outperformed the previous state-of-the-art method DeepCubeA, improving the trade-off between solution optimality and computational cost, despite significantly less training data. Furthermore, we investigate the scaling law of our Rubik's Cube solver with respect to model size and training data volume.
Data-driven behavioural biometrics for continuous and adaptive user verification using Smartphone and Smartwatch

Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to changes in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user’s identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results that show how the rate of misclassification drops while using this model with adversarial data.
Conceptual Expansion Neural Architecture Search (CENAS)

Architecture search optimizes the structure of a neural network for some task instead of relying on manual authoring. However, it is slow, as each potential architecture is typically trained from scratch. In this paper we present an approach called Conceptual Expansion Neural Architecture Search (CENAS) that combines a sample-efficient, computational creativity-inspired transfer learning approach with neural architecture search. This approach finds models faster than naive architecture search via transferring existing weights to approximate the parameters of the new model. It outperforms standard transfer learning by allowing for the addition of features instead of only modifying existing features. We demonstrate that our approach outperforms standard neural architecture search and transfer learning methods in terms of efficiency, performance, and parameter counts on a variety of transfer learning tasks.
Hyperparameter Tuning with Renyi Differential Privacy

For many differentially private algorithms, such as the prominent noisy stochastic gradient descent (DP-SGD), the analysis needed to bound the privacy leakage of a single training run is well understood. However, few studies have reasoned about the privacy leakage resulting from the multiple training runs needed to fine tune the value of the training algorithm's hyperparameters. In this work, we first illustrate how simply setting hyperparameters based on non-private training runs can leak private information. Motivated by this observation, we then provide privacy guarantees for hyperparameter search procedures within the framework of Renyi Differential Privacy. Our results improve and extend the work of Liu and Talwar (STOC 2019). Our analysis supports our previous observation that tuning hyperparameters does indeed leak private information, but we prove that, under certain assumptions, this leakage is modest, as long as each candidate training run needed to select hyperparameters is itself differentially private.
Peaked sources and narrow‐line Seyfert 1s: A love story

European Southern Observatory (ESO), Alonso de Córdova 3107, Casilla 19, Santiago 19001, Chile Finnish Centre for Astronomy with ESO (FINCA), University of Turku, Vesilinnantie 5, FI-20014 University of Turku, Finland Aalto University Metsähovi Radio Observatory, Metsähovintie 114, FI-02540 Kylmälä, Finland European Space Agency (ESA), European Space Astronomy Centre (ESAC), Camino Bajo del Castillo s/n, 28692 Villanueva de la Cañada, Madrid, Spain
Cross-Language Learning for Product Matching

Transformer-based entity matching methods have significantly moved the state of the art for less-structured matching tasks such as matching product offers in e-commerce. In order to excel at these tasks, Transformer-based matching methods require a decent amount of training pairs. Providing enough training data can be challenging, especially if a matcher for non-English product descriptions should be learned. This poster explores along the use case of matching product offers from different e-shops to which extent it is possible to improve the performance of Transformer-based matchers by complementing a small set of training pairs in the target language, German in our case, with a larger set of English-language training pairs. Our experiments using different Transformers show that extending the German set with English pairs improves the matching performance in all cases. The impact of adding the English pairs is especially high in low-resource settings in which only a rather small number of non-English pairs is available. As it is often possible to automatically gather English training pairs from the Web by exploiting schema.org annotations, our results are relevant for many product matching scenarios targeting low-resource languages.
Joint Calibration and Mapping of Satellite Altimetry Data Using Trainable Variational Models

Satellite radar altimeters are a key source of observation of ocean surface dynamics. However, current sensor technology and mapping techniques do not yet allow to systematically resolve scales smaller than 100km. With their new sensors, upcoming wide-swath altimeter missions such as SWOT should help resolve finer scales. Current mapping techniques rely on the quality of the input data, which is why the raw data go through multiple preprocessing stages before being used. Those calibration stages are improved and refined over many years and represent a challenge when a new type of sensor start acquiring data. Here we show how a data-driven variational data assimilation framework could be used to jointly learn a calibration operator and an interpolator from non-calibrated data . The proposed framework significantly outperforms the operational state-of-the-art mapping pipeline and truly benefits from wide-swath data to resolve finer scales on the global map as well as in the SWOT sensor geometry.
TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network

Point cloud is one of the widely used techniques for representing and storing 3D geometric data. In the past several methods have been proposed for processing point clouds. Methods such as PointNet and FoldingNet have shown promising results for tasks like 3D shape classification and segmentation. This work proposes a tree-structured autoencoder framework to generate robust embeddings of point clouds by utilizing hierarchical information using graph convolution. We perform multiple experiments to assess the quality of embeddings generated by the proposed encoder architecture and visualize the t-SNE map to highlight its ability to distinguish between different object classes. We further demonstrate the applicability of the proposed framework in applications like: 3D point cloud completion and Single image-based 3D reconstruction.
What Should 6G Architectures Be?

The accelerated convergence of digital and real-world lifestyles has imposed unprecedented demands on today’s wireless network architectures, as it is highly desirable for such architectures to support wireless devices everywhere with high capacity and minimal signaling overhead. Conventional architectures, such as cellular architectures, are not able to satisfy these requirements simultaneously, and are thus no longer suitable for the 6G era. In this paper, we propose a capacity-centric (C 2 ) architecture for 6G. It is designed based on the principles of maximizing the number of non-overlapping clusters with the average cluster capacity guaranteed to be higher than a certain threshold, and thus provides a ﬂexible way to balance the capacity requirement against the signaling overhead. Our analytical results reveal that C 2 has superior generality, wherein both the cellular and the fully coordinated architectures can be viewed as its extreme cases. Simulation results show that the average capacity of C 2 is at least three times higher compared to that of the cellular architecture. More importantly, different from the widely adopted conventional wisdom that base-station distributions dominate architecture designs, we ﬁnd that the C 2 architecture is independent of base-station distributions, and instead user-side information should be the focus in 6G architecture designs.
The Effect of Age on the Grouping of Open Clusters: The Primordial Group Hypothesis

The Primordial Group hypothesis states that only sufficiently young open clusters (OCs) can be multiple, and old OCs are essentially isolated. We tested this postulate through four different studies using a manual search of Gaia EDR3 and extensive literature. First, we revisited the work of de La Fuente Marcos and de La Fuente Marcos (2009), which states that only ca. 40% of OC pairs are of primordial origin. However, no plausible binary system among their proposed OC pairs having at least one member older than 0.1 Gyr was found. Second, we researched the OCs < 0.01 Gyr old in Tarricq et al. (2021) and found that ca. 71% of them remain in their primordial groups. Third, a similar study of the oldest OCs (age > 4 Gyr) showed that they are essentially alone. Forth, the well-known case of the double cluster in Perseus and some other binary systems described in the literature were also shown to accommodate the title hypothesis. A simplified bimodal model allows for retrieval of the overall fraction of related OCs (approximately 12–16%) from our results, assuming that young clusters remain associated at ~0.04 Gyr. The obtained results further support that OCs are born in groups (Casado 2021).
Learning Pessimism for Reinforcement Learning

Off-policy deep reinforcement learning algorithms commonly compensate for overestimation bias during temporal-difference learning by utilizing pessimistic estimates of the expected target returns. In this work, we propose Generalized Pessimism Learning (GPL), a strategy employing a novel learnable penalty to enact such pessimism. In particular, we propose to learn this penalty alongside the critic with dual TD-learning, a new procedure to estimate and minimize the magnitude of the target returns bias with trivial computational cost. GPL enables us to accurately counteract overestimation bias throughout training without incurring the downsides of overly pessimistic targets. By integrating GPL with popular off-policy algorithms, we achieve state-of-the-art results in both competitive proprioceptive and pixel-based benchmarks.
Self-Evolutionary Optimization for Pareto Front Learning

Multi-task learning (MTL), which aims to improve performance by learning multiple tasks simultaneously, inherently presents an optimization challenge due to multiple objectives. Hence, multi-objective optimization (MOO) approaches have been proposed for multitasking problems. Recent MOO methods approximate multiple optimal solutions (Pareto front) with a single unified model, which is collectively referred to as Pareto front learning (PFL). In this paper, we show that PFL can be re-formulated into another MOO problem with multiple objectives, each of which corresponds to different preference weights for the tasks. We leverage an evolutionary algorithm (EA) to propose a method for PFL called self-evolutionary optimization (SEO) by directly maximizing the hypervolume. By using SEO, the neural network learns to approximate the Pareto front conditioned on multiple hyper-parameters that drastically affect the hypervolume. Then, by generating a population of approximations simply by inferencing the network, the hyper-parameters of the network can be optimized by EA. Utilizing SEO for PFL, we also introduce self-evolutionary Pareto networks (SEPNet), enabling the unified model to approximate the entire Pareto front set that maximizes the hypervolume. Extensive experimental results confirm that SEPNet can find a better Pareto front than the current state-of-the-art methods while minimizing the increase in model size and training cost.
Fourier Plane-Wave Series Expansion for Holographic MIMO Communications

Imagine a MIMO communication system that fully exploits the propagation characteristics offered by an electromagnetic channel and ultimately approaches the limits imposed by wireless communications. This is the concept of Holographic MIMO communications. Accurate and tractable channel modeling is critical to understanding its full potential. Classical stochastic models used by communications theorists are derived under the electromagnetic far-field assumption, i.e. planar wave approximation over the array. However, such assumption breaks down when electromagnetically large (compared to the wavelength) antenna arrays are considered - as envisioned in future wireless communications. In this paper, we start from the first principles of wave propagation and provide a Fourier plane-wave series expansion of the channel response, which fully captures the essence of electromagnetic propagation in arbitrary scattering and is also valid in the (radiative) near-field. The expansion is based on the Fourier spectral representation and has an intuitive physical interpretation, as it statistically describes the angular coupling between source and receiver. When discretized uniformly, it leads to a low-rank semi-unitarily equivalent approximation of the spatial electromagnetic channel in the angular domain. The developed channel model is used to compute the ergodic capacity of a point-to-point Holographic MIMO system with different degrees of channel state information.
LECT2, A Novel and Direct Biomarker of Liver Fibrosis in Patients With CHB

Chronic hepatitis B (CHB) patients with severe liver fibrosis would be more likely to progress to a poorer prognosis. Treatment is considered once the liver fibrosis reaches significant liver fibrosis (≥S2). Leukocyte cell-derived chemotaxin-2 (LECT2) has been shown to contribute to liver fibrosis progression. No research has focused on the role of LECT2 in liver fibrosis in CHB patients. This study enrolled 227 CHB patients and divided them into the training group (n = 147) and validation group (n = 80), respectively. The expression of LECT2 in serum, protein and mRNA of the human liver tissues was detected to analyze the possible associations between LECT2 and liver fibrosis. A receiver operating characteristic curve (ROC) was used to estimate the efficacy of LECT2 for predicting liver fibrosis. The data showed that there was a positive relationship between LECT2 and the progression of liver fibrosis. In the training group, LECT2 was demonstrated to have better effectiveness than APRI and FIB-4. The AUC was 0.861, 0.698, and 0.734 for significant liver fibrosis, and 0.855, 0.769, and 0.752 for advanced liver fibrosis. Besides, the efficacy of LECT2 in different statuses of patients with CHB was examined and the effectiveness of LECT2 had also been confirmed in the validation group. All the results confirmed that LECT2 could act as a perfect predictor and thus offers a novel and direct biomarker to estimate liver fibrosis more accurately.
Characteristics and Rhodamine B Adsorption Ability of Modified Sepiolites

Sepiolite has been treated with ethanol under isothermal conditions and characterized by XRD, SEM, FT-IR, and BET measurements. The materials showed a typical lamellar structure and fibrous morphology. After treatment of sepiolite, the surface area of the solid is significantly improved while the material structure still remains. Both fresh and treated sepiolites were used as adsorbents for the adsorption of rhodamine B in water. In isothermal conditions, both samples exhibit a good ability to adsorb rhodamine B in water. Experimental results indicate that the adsorption of rhodamine B for sepiolite was fitted to the Langmuir and Freundlich adsorption models. The treated sepiolite showed a higher adsorption capacity than does the fresh sample.