Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Causal Inference in the Presence of Hidden Common Causes: an Entropic Approach

Quantum causality is an emerging ﬁeld of study which has the potential to greatly advance our understanding of quantum systems. In this paper, we put forth a theoretical framework for merging quantum information science and causal inference by exploiting entropic principles. For this purpose, we leverage the tradeoﬀ between the entropy of hidden cause and the conditional mutual information of observed variables to develop a scalable algorithmic approach for inferring causality in the presence of latent confounders (common causes) in quantum systems. As an application, we consider a system of three entangled qubits and transmit the second and third qubits over separate noisy quantum channels. In this model, we validate that the ﬁrst qubit is a latent confounder and the common cause of the second and third qubits. In contrast, when two entangled qubits are prepared and one of them is sent over a noisy channel, there is no common confounder. We also demonstrate that the proposed approach outperforms the results of classical causal inference for the Tubingen database when the variables are classical by exploiting quantum dependence between variables through density matrices rather than joint probability distributions. Thus, the proposed approach uniﬁes classical and quantum causal inference in a principled way.
Spectroscopic and photometric analysis of 21 chromospherically active variables: Activity cycles and differential rotation

Abstract We investigate magnetic activity properties of 21 stars via medium resolution optical spectra and long-term photometry. Applying synthetic spectrum fitting method, we find that all targets are cool giant or sub-giant stars possessing overall [M/H] abundances between 0 and $-0.5$ . We find that six of these targets exhibit only linear trend in mean brightness, while eight of them clearly shows cyclic mean brightness variation. Remaining seven target appear to exhibit cyclic mean brightness variation, but this cannot be confirmed due to the long timescales of the predicted cycle compared to the current time range of the photometric data. We further determine seasonal photometric periods and compute average photometric period of each target. Analysed sample in this study provides a quantitative representation of a positive linear correlation between the inverse of the rotation period and the cycle period normalised to the rotation period, on the log-log scale. We also observe no correlation between the activity cycle length and the relative surface shear, indicating that the activity cycle must be driven by a parameter rather than the differential rotation. Our analyses show that the relative surface shear is positively correlated with the rotation period and there is a noticeable separation between main sequence stars and our sample. Compared to our sample, the relative surface shear of a main sequence star is larger for a given rotation period. However, dependence of the relative surface shear on the rotation period appears stronger for our sample. Analysis of the current photometric data indicates that the photometric properties of the observed activity cycles in eight targets seem dissimilar to the sunspot cycle.
Performance Testing Using a Smart Reinforcement Learning-Driven Test Agent

Performance testing with the aim of generating an efficient and effective workload to identify performance issues is challenging. Many of the automated approaches mainly rely on analyzing system models, source code, or extracting the usage pattern of the system during the execution. However, such information and artifacts are not always available. Moreover, all the transactions within a generated workload do not impact the performance of the system the same way, a finely tuned workload could accomplish the test objective in an efficient way. Model-free reinforcement learning is widely used for finding the optimal behavior to accomplish an objective in many decision-making problems without relying on a model of the system. This paper proposes that if the optimal policy (way) for generating test workload to meet a test objective can be learned by a test agent, then efficient test automation would be possible without relying on system models or source code. We present a self-adaptive reinforcement learning-driven load testing agent, RELOAD, that learns the optimal policy for test workload generation and generates an effective workload efficiently to meet the test objective. Once the agent learns the optimal policy, it can reuse the learned policy in subsequent testing activities. Our experiments show that the proposed intelligent load test agent can accomplish the test objective with lower test cost compared to common load testing procedures, and results in higher test efficiency.
Separating Rank Logic from Polynomial Time

In the search for a logic capturing polynomial time the most promising candidates are Choiceless Polynomial Time (CPT) and rank logic. Rank logic extends fixed-point logic with counting by a rank operator over prime fields. We show that the isomorphism problem for CFI graphs over ${{\mathbb{Z}}_{{2^i}}}$ cannot be defined in rank logic, even if the base graph is totally ordered. However, CPT can define this isomorphism problem. We thereby separate rank logic from CPT and in particular from polynomial time.
Classification and purification for the independent quantum channel through quantum error-correction

The essence of quantum error-correction is to use redundant Hilbert space to identify and correct errors, and the channel fidelity of the quantum channel does not affect which errors can be identified and corrected. Based on this, it is found that quantum error-correction can be used to classify the independent quantum channel into 5 types, and 4 of the 5 types can be purified. It is found in quantum error-correction, the decoherence of quantum state may be related to the degree of identification for the state under quantum noise, and the results of this work confirmed that the degree of purity of quantum channel determines its ability to retain the quantum property of the quantum state, not the fidelity. In this work, the identification of the independent Pauli channels by quantum error-correction is demonstrated.
A Review of the Non-Invasive Techniques for Monitoring Different Aspects of Sleep

Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep which is having negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and have now become an important tool for understanding sleep behavior. The gold standard method for sleep analysis is polysomnography (PSG) conducted in a clinical environment but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods which are cheap and easy to use for in-home sleep monitoring. In this paper, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest works done using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on 10 key factors, to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also present some publicly available datasets for different categories of sleep monitoring. In the end, we discuss several open issues and provide future research directions in the area of sleep monitoring.
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding

Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple color spaces are adaptively integrated and highlighted. Inspired by underwater imaging physical models, we design a medium transmission (indicating the percentage of the scene radiance reaching the camera)-guided decoder network to enhance the response of network towards quality-degraded regions. As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods. Extensive experiments demonstrate that our Ucolor achieves superior performance against state-of-the-art methods in terms of both visual quality and quantitative metrics. The code is publicly available at: https://li-chongyi.github. io/Proj_Ucolor. html.
Exponentially Improved Dimensionality Reduction for 𝓁1: Subspace Embeddings and Independence Testing

Despite many applications, dimensionality reduction in the `1-norm is much less understood than in the Euclidean norm. We give two new oblivious dimensionality reduction techniques for the `1-norm which improve exponentially over prior ones: 1. We design a distribution over random matrices S ∈ Rr×n, where r = 2, such that given any matrix A ∈ Rn×d, with probability at least 1 − δ, simultaneously for all x, ‖SAx‖1 = (1 ± ε)‖Ax‖1. Note that S is linear, does not depend on A, and maps `1 into `1. Our distribution provides an exponential improvement on the previous best known map of Wang and Woodruff (SODA, 2019), which required r = 2 Ω(d) , even for constant ε and δ. Our bound is optimal, up to a polynomial factor in the exponent, given a known 2 √ d lower bound for constant ε and δ. 2. We design a distribution over matrices S ∈ Rk×n, where k = 2 2)(ε−1q log d), such that given any q-mode tensor A ∈ (Rd)⊗q, one can estimate the entrywise `1-norm ‖A‖1 from S(A). Moreover, S = S ⊗ S ⊗ · · · ⊗ S and so given vectors u1, . ,uq ∈ R, one can compute S(u1 ⊗ u2⊗· · ·⊗uq) in time 2 2)(ε−1q log d), which is much faster than the d time required to form u1⊗u2⊗· · ·⊗uq. Our linear map gives a streaming algorithm for independence testing using space 2 2)(ε−1q log d), improving the previous doubly exponential (ε−1 log d) O(q) space bound of Braverman and Ostrovsky (STOC, 2010). For subspace embeddings, we also study the setting when A is itself drawn from distributions with independent entries, and obtain a polynomial embedding dimension. For independence testing, we also give algorithms for any distance measure with a polylogarithmic-sized sketch and satisfying an approximate triangle inequality. ar X iv :2 10 4. 12 94 6v 1 [ cs . D S] 2 7 A pr 2 02 1
An optimal control approach to determine resistance‐type boundary conditions from in‐vivo data for cardiovascular simulations

The choice of appropriate boundary conditions is a fundamental step in computational fluid dynamics (CFD) simulations of the cardiovascular system. Boundary conditions, in fact, highly affect the computed pressure and flow rates, and consequently haemodynamic indicators such as wall shear stress (WSS), which are of clinical interest. Devising automated procedures for the selection of boundary conditions is vital to achieve repeatable simulations. However, the most common techniques do not automatically assimilate patient‐specific data, relying instead on expensive and time‐consuming manual tuning procedures. In this work, we propose a technique for the automated estimation of outlet boundary conditions based on optimal control. The values of resistive boundary conditions are set as control variables and optimized to match available patient‐specific data. Experimental results on four aortic arches demonstrate that the proposed framework can assimilate 4D‐Flow MRI data more accurately than two other common techniques based on Murray's law and Ohm's law.
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier Detection

The laborious process of labeling data often bottlenecks projects that aim to leverage the power of supervised machine learning. Active Learning (AL) has been established as a technique to ameliorate this condition through an iterative framework that queries a human annotator for labels of instances with the most uncertain class assignment. Via this mechanism, AL produces a binary classifier trained on less labeled data but with little, if any, loss in predictive performance. Despite its advantages, AL can have difficulty with class-imbalanced datasets and results in an inefficient labeling process. To address these drawbacks, we investigate our unsupervised instance selection (UNISEL) technique followed by a Random Forest (RF) classifier on 10 outlier detection datasets under low-label conditions. These results are compared to AL performed on the same datasets. Further, we investigate the combination of UNISEL and AL. Results indicate that UNISEL followed by an RF performs comparably to AL with an RF and that the combination of UNISEL and AL demonstrates superior performance. The practical implications of these findings in terms of time savings and generalizability afforded by UNISEL are discussed.
Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning

Federated learning enables multiple participants to collaboratively train a model without aggregating the training data. Although the training data are kept within each participant and the local gradients can be securely synthesized, recent studies have shown that such privacy protection is insufficient. The global model parameters that have to be shared for optimization are susceptible to leak information about training data. In this work, we propose Confined Gradient Descent (CGD) that enhances privacy of federated learning by eliminating the sharing of global model parameters. CGD exploits the fact that a gradient descent optimization can start with a set of discrete points and converges to another set at the neighborhood of the global minimum of the objective function. It lets the participants independently train on their local data, and securely share the sum of local gradients to benefit each other. We formally demonstrate CGD's privacy enhancement over traditional FL. We prove that less information is exposed in CGD compared to that of traditional FL. CGD also guarantees desired model accuracy. We theoretically establish a convergence rate for CGD. We prove that the loss of the proprietary models learned for each participant against a model learned by aggregated training data is bounded. Extensive experimental results on two real-world datasets demonstrate the performance of CGD is comparable with the centralized learning, with marginal differences on validation loss (mostly within 0.05) and accuracy (mostly within 1%).
An X-ray and radio study of the Hubble Frontier Field cluster Abell S1063

We present results from Chandra X-ray observations and 325 MHz Giant Metrewave Radio Telescope (GMRT) observations of the massive and X-ray luminous cluster of galaxies Abell S1063. We report the detection of large-scale “excess brightness” in the residual Chandra X-ray surface brightness map, which extends at least 2.7 Mpc towards the north-east from the center of the cluster. We also present a high fidelity X-ray flux and temperature map using Chandra archival data of 122 ksec, which shows the disturbed morphology in the cluster. The residual flux map shows the first observational confirmation of the merging axis proposed by earlier simulation by Gómez et al. (2012). The average temperature within R500 is 11.7± 0.56 keV, which makes AS1063 one of the hottest clusters in the nearby Universe. The integrated radio flux density at 325 MHz is found to be 62.0 ± 6.3 mJy. The integrated spectrum of the radio halo follows a power-law with a spectral index α = −1.43±0.13. The radio halo is found to be significantly under-luminous, which favored for both the hadronic as well as the turbulent re-acceleration mechanism for its origin.
Toward Blockchain for Edge-of-Things: A New Paradigm, Opportunities, and Future Directions

Blockchain is gaining momentum as a promising technology for many application domains, one of them being the Edge-of-Things (EoT) that is enabled by the integration of edge computing and the Internet-of-Things (IoT). Particularly, the amalgamation of blockchain and EoT leads to a new paradigm, called blockchain enabled EoT (BEoT) that is crucial for enabling future low-latency and high-security services and applications. This article envisions a novel BEoT architecture for supporting industrial applications under the management of blockchain at the network edge in a wide range of IoT use cases such as smart home, smart healthcare, smart grid, and smart transportation. The potentials of BEoT in providing security services are also explored, including access authentication, data privacy preservation, attack detection, and trust management. Finally, we point out some key research challenges and future directions in this emerging area.
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning*

State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues. As a workaround, researchers have been looking into integrating EMG with other signals, often in an ad hoc manner. In this paper, we are presenting a method for end-to-end training of a policy for human-in-the-loop robot grasping on real reaching trajectories. For this purpose we use Reinforcement Learning (RL) and Imitation Learning (IL) in DEXTRON (DEXTerity enviRONment), a stochastic simulation environment with real human trajectories that are augmented and selected using a Monte Carlo (MC) simulation method. We also offer a success model which once trained on the expert policy data and the RL policy roll-out transitions, can provide transparency to how the deep policy works and when it is probably going to fail.
Mixture models for spherical data with applications to protein bioinformatics

Finite mixture models are fitted to spherical data. Kent distributions are used for the components of the mixture because they allow considerable flexibility. Previous work on such mixtures has used an approximate maximum likelihood estimator for the parameters of a single component. However, the approximation causes problems when using the EM algorithm to estimate the parameters in a mixture model. Hence the exact maximum likelihood estimator is used here for the individual components. This paper is motivated by a challenging prize problem in structural bioinformatics of how proteins fold. It is known that hydrogen bonds play a key role in the folding of a protein. We explore this hydrogen bond geometry using a data set describing bonds between two amino acids in proteins. An appropriate coordinate system to represent the hydrogen bond geometry is proposed, with each bond represented as a point on a sphere. We fit mixtures of Kent distributions to different subsets of the hydrogen bond data to gain insight into how the secondary structure elements bond together, since the distribution of hydrogen bonds depends on which secondary structure elements are involved.
A Survey on Neural Recommendation: From Collaborative Filtering to Content and Context Enriched Recommendation

Influenced by the stunning success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models, aiming to summarize the field to facilitate future progress. Distinct from existing surveys that categorize existing methods based on the taxonomy of deep learning techniques, we instead summarize the field from the perspective of recommendation modeling, which could be more instructive to researchers and practitioners working on recommender systems. Specifically, we divide the work into three types based on the data they used for recommendation modeling: 1) collaborative filtering models, which leverage the key source of user-item interaction data; 2) content enriched models, which additionally utilize the side information associated with users and items, like user profile and item knowledge graph; and 3) context enriched models, which account for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative works for each type, we finally discuss some promising directions in this field, including benchmarking recommender systems, graph reasoning based recommendation models, and explainable and fair recommendations for social good.
LasHeR: A Large-Scale High-Diversity Benchmark for RGBT Tracking

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a <italic>La</italic> rge-<inline-formula> <tex-math notation="LaTeX">${s}$ </tex-math></inline-formula>cale <inline-formula> <tex-math notation="LaTeX">${H}$ </tex-math></inline-formula>igh-diversity <inline-formula> <tex-math notation="LaTeX">$\text{b}{e}$ </tex-math></inline-formula>nchmark for short-term <inline-formula> <tex-math notation="LaTeX">${R}$ </tex-math></inline-formula>GBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated with a bounding box, making the dataset well and densely annotated. LasHeR is highly diverse capturing from a broad range of object categories, camera viewpoints, scene complexities and environmental factors across seasons, weathers, day and night. We conduct a comprehensive performance evaluation of 12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis. In addition, we release the unaligned version of LasHeR to attract the research interest for alignment-free RGBT tracking, which is a more practical task in real-world applications. The datasets and evaluation protocols are available at: <uri>https://github.com/mmic-lcl/Datasets-and-benchmark-code</uri>.