Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Local Average Treatment Effect by Data Combination

It is important to estimate the local average treatment effect (LATE) when compliance with a treatment assignment is incomplete. The previously proposed methods for LATE estimation required all relevant variables to be jointly observed in a single dataset; however, it is sometimes difficult or even impossible to collect such data in many real-world problems for technical or privacy reasons. We consider a novel problem setting in which LATE, as a function of covariates, is nonparametrically identified from the combination of separately observed datasets. For estimation, we show that the direct least squares method, which was originally developed for estimating the average treatment effect under complete compliance, is applicable to our setting. However, model selection and hyperparameter tuning for the direct least squares estimator can be unstable in practice since it is defined as a solution to the minimax problem. We then propose a weighted least squares estimator that enables simpler model selection by avoiding the minimax objective formulation. Unlike the inverse probability weighted (IPW) estimator, the proposed estimator directly uses the pre-estimated weight without inversion, avoiding the problems caused by the IPW methods. We demonstrate the effectiveness of our method through experiments using synthetic and real-world datasets.
Data Preparation for Software Vulnerability Prediction: A Systematic Literature Review

Software Vulnerability Prediction (SVP) is a data-driven technique for software quality assurance that has recently gained considerable attention in the Software Engineering research community. However, the difficulties of preparing Software Vulnerability (SV) related data is considered as the main barrier to industrial adoption of SVP approaches. Given the increasing, but dispersed, literature on this topic, it is needed and timely to systematically select, review, and synthesize the relevant peer-reviewed papers reporting the existing SV data preparation techniques and challenges. We have carried out a Systematic Literature Review (SLR) of SVP research in order to develop a systematized body of knowledge of the data preparation challenges, solutions, and the needed research. Our review of the 61 relevant papers has enabled us to develop a taxonomy of data preparation for SVP related challenges. We have analyzed the identified challenges and available solutions using the proposed taxonomy. Our analysis of the state of the art has enabled us identify the opportunities for future research. This review also provides a set of recommendations for researchers and practitioners of SVP approaches.
Encoding Distributional Soft Actor-Critic for Autonomous Driving in Multi-lane Scenarios

In this paper, we propose a new reinforcement learning (RL) algorithm, called encoding distributional soft actor-critic (E-DSAC), for decision-making in autonomous driving. Unlike existing RL-based decision-making methods, E-DSAC is suitable for situations where the number of surrounding vehicles is variable and eliminates the requirement for manually pre-designed sorting rules, resulting in higher policy performance and generality. We first develop an encoding distributional policy iteration (DPI) framework by embedding a permutation invariant module, which employs a feature neural network (NN) to encode the indicators of each vehicle, in the distributional RL framework. The proposed DPI framework is proved to exhibit important properties in terms of convergence and global optimality. Next, based on the developed encoding DPI framework, we propose the E-DSAC algorithm by adding the gradient-based update rule of the feature NN to the policy evaluation process of the DSAC algorithm. Then, the multi-lane driving task and the corresponding reward function are designed to verify the effectiveness of the proposed algorithm. Results show that the policy learned by E-DSAC can realize efficient, smooth, and relatively safe autonomous driving in the designed scenario. And the final policy performance learned by E-DSAC is about three times that of DSAC. Furthermore, its effectiveness has also been verified in real vehicle experiments.
Anesthesia in mice activates discrete populations of neurons throughout the brain

The brain undergoes rapid, dramatic, and reversible transitioning between states of wakefulness and unconsciousness during natural sleep and in pathological conditions such as hypoxia, hypotension, and concussion. Transitioning can also be induced pharmacologically using general anesthetic agents. The effect is selective. Mobility, sensory perception, memory formation, and awareness are lost while numerous housekeeping functions persist. How is selective transitioning accomplished? Classically a handful of brainstem and diencephalic “arousal nuclei” have been implicated in driving brain‐state transitions on the grounds that their net activity systematically varies with brain state. Here we used transgenic targeted recombination in active populations mice to label neurons active during wakefulness with one reporter and neurons active during pentobarbital‐induced general anesthesia with a second, contrasting reporter. We found ‘wake‐on’ and ‘anesthesia‐on’ neurons in widely distributed regions‐of‐interest, but rarely encountered neurons labeled with both reporters. Nearly all labeled neurons were either wake‐on or anesthesia‐on. Thus, anesthesia‐on neurons are not unique to the few nuclei discovered to date whose activity appears to increase during anesthesia. Rather neuronal populations selectively active during anesthesia are located throughout the brain where they likely play a causative role in transitioning between wakefulness and anesthesia. The widespread neuronal suppression reported in prior comparisons of the awake and anesthetized brain in animal models and noninvasive imaging in humans reflects only net differences. It misses the ubiquitous presence of neurons whose activity increases during anesthesia. The balance in recruitment of anesthesia‐on versus wake‐on neuronal populations throughout the brain may be a key driver of regional and global vigilance states.
Nurses’ perceptions of social rejection, resilience and well‐being during COVID‐19: A national comparative study

Abstract Aims and Objectives To determine the level of social rejection and well‐being of nurses, whether resilience is a mediator between them and to compare nurses who worked versus did not work on COVID‐19 wards. Background During the COVID‐19 pandemic health care workers reported psychological distress and social rejection. Methods An online survey was sent to nursing social media groups in Israel. Respondents completed a Demographic, Social Rejection, Resilience and General Well‐being questionnaire. Results Two hundred and forty‐seven nurses responded. The majority were female with a mean age of 43.6 years Approximately one‐third were worried about infecting their family members and many agreed that their family fears that the nurse will infect them. Nurses reported their partner, family members, neighbours and the public physically distanced themselves from them. Approximately one quarter reported feeling lonely. Statistically significant differences were found between those who worked versus not work on a COVID‐19 unit on general well‐being, and social rejection. No differences were found in resilience scores. Conclusions Social rejection was felt by many nurses as shown by an inverse relationship between the closeness of the relationship and the sense of social rejection and a high level of loneliness and depression. A higher level of social rejection and lower well‐being were found among nurses working on COVID‐19 wards as opposed to those who did not. General well‐being was found to be exceptionally low during COVID‐19. Resilience did not mediate the relationship between social rejection and general well‐being. Relevance to clinical practice Perceived social rejection might be associated with decreased well‐being. The level of resilience is related to the level of well‐being among nurses in general. Nurses not working in COVID‐19 wards have higher levels of well‐being and less social rejection compared with nurses working in these wards.
Understanding the landscape and propagation of COVID-19 misinformation and its correction on Sina Weibo

The prevalence of health misinformation on social media could significantly influence individuals’ health behaviors. To examine the prevalent topics, propagation, and correction of coronavirus disease 2019 (COVID-19) misinformation, automated content analyses were conducted for posts on Sina Weibo, which is China’s largest microblogging site. In total, 177,816 posts related to COVID-19 misinformation during the COVID-19 outbreak in China were analyzed. The structural topic modeling identified 23 valid topics regarding COVID-19 misinformation and its correction, which were further categorized into three general themes. Sentiment analysis was conducted to generate positive and negative sentiment scores for each post. The zero-inflated Poisson model indicated that only the negative sentiment was a significant predictor of the number of comments (β = 0.003, p < 0.001) but not reposts. Furthermore, users are more prone to repost and comment on information regarding prevention/treatment (e.g., traditional Chinese medicine preventing COVID) as well as potential threats of COVID-19 (e.g., COVID-19 was defined as an epidemic by World Health Organization). Health education and promotion implications are discussed.
Photoprintable Gelatin-graft-Poly(trimethylene carbonate) by Stereolithography for Tissue Engineering Applications.

The stereolithography process is a powerful additive manufacturing technology to fabricate scaffolds for regenerative medicine. Nevertheless, the quest for versatile inks allowing one to produce scaffolds with controlled properties is still unsatisfied. In this original article, we tackle this bottleneck by synthesizing a panel of photoprocessable hybrid copolymers composed of gelatin-graft-poly(trimethylene carbonate)s (Gel-g-PTMCn). We demonstrated that by changing the length of PTMC blocks grafted from gelatin, it is possible to tailor the final properties of the photofabricated objects. We reported here on the synthesis of Gel-g-PTMCn with various lengths of PTMC blocks grafted from gelatin using hydroxy and amino side groups of the constitutive amino acids. Then, the characterization of the resulting hybrid copolymers was fully investigated by quantitative NMR spectroscopy before rendering them photosensitive by methacrylation of the PTMC terminal groups. Homogeneous composition of the photocrosslinked hybrid polymers was demonstrated by EDX spectroscopy and electronic microscopy. To unravel the individual contribution of the PTMC moiety on the hybrid copolymer behavior, water absorption, contact angle measurements, and degradation studies were undertaken. Interestingly, the photocrosslinked materials immersed in water were examined using tensile experiments and displayed a large panel of behavior from hydrogel to elastomer-like depending on the PTMC/gel ratio. Moreover, the absence of cytotoxicity was conducted following the ISO 10993 assay. As a proof of concept, 3D porous objects were successfully fabricated using stereolithography. Those results validate the great potential of this panel of inks for tissue engineering and regenerative medicine.
Vapour and Solution Uptake Properties of Starch and Cellulose Biopolymers

This study was aimed at gaining further insight on the role of hydration in adsorption processes of biopolymer/adsorbate systems using complementary methods (electromagnetic interference (EMI) shielding, calorimetry, and solvent/vapour adsorption isotherms). Cellulose and starch-based materials were used as the adsorbents, whereas water (liquid and vapour), ethanol and p-nitrophenol (PNP) in aqueous solution were the adsorbate systems. The biopolymer/water systems had higher uptake capacity overall, where starch materials showed higher uptake capacity than cellulose among the various solvents. The secondary and tertiary structure of the biopolymers was a key factor affecting their uptake capacity, as evidenced by the enhanced adsorption properties of starch over cellulose, along with higher uptake of amylose (AM) versus amylopectin (AP) in starch biopolymers. EMI results also confirmed that AM starch had higher adsorption toward water than ethanol. The textural properties and surface chemistry of the biopolymers were probed using dye adsorption (PNP at pH 8.5) in aqueous solution that showed parallel trends with water vapour adsorption isotherms. Isothermal Titration Calorimetry (ITC) revealed that the heat of adsorption in AP differed from that of AM since the biopolymer tertiary structure governs the accessibility of biopolymer adsorption sites. The role of branching in AP and amorphous domains in AM/AP composites are inferred to play a key role in hydration-driven allosterism known for such biopolymer/water vapour adsorption processes.
Mitochondrial A‐kinase anchoring proteins in cardiac ventricular myocytes

Compartmentation of cAMP signaling is a critical factor for maintaining the integrity of receptor‐specific responses in cardiac myocytes. This phenomenon relies on various factors limiting cAMP diffusion. Our previous work in adult rat ventricular myocytes (ARVMs) indicates that PKA regulatory subunits anchored to the outer membrane of mitochondria play a key role in buffering the movement of cytosolic cAMP. PKA can be targeted to discrete subcellular locations through the interaction of both type I and type II regulatory subunits with A‐kinase anchoring proteins (AKAPs). The purpose of this study is to identify which AKAPs and PKA regulatory subunit isoforms are associated with mitochondria in ARVMs. Quantitative PCR data demonstrate that mRNA for dual specific AKAP1 and 2 (D‐AKAP1 & D‐AKAP2), acyl‐CoA‐binding domain‐containing 3 (ACBD3), optic atrophy 1 (OPA1) are most abundant, while Rab32, WAVE‐1, and sphingosine kinase type 1 interacting protein (SPHKAP) were barely detectable. Biochemical and immunocytochemical analysis suggests that D‐AKAP1, D‐AKAP2, and ACBD3 are the predominant mitochondrial AKAPs exposed to the cytosolic compartment in these cells. Furthermore, we show that both type I and type II regulatory subunits of PKA are associated with mitochondria. Taken together, these data suggest that D‐AKAP1, D‐AKAP2, and ACBD3 may be responsible for tethering both type I and type II PKA regulatory subunits to the outer mitochondrial membrane in ARVMs. In addition to regulating PKA‐dependent mitochondrial function, these AKAPs may play an important role by buffering the movement of cAMP necessary for compartmentation.
Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification

Person re-identification (re-ID) has gained more and more attention due to its widespread applications in intelligent video surveillance. Unfortunately, the mainstream deep learning methods still need a large quantity of labeled data to train models, and annotating data is an expensive work in real-world scenarios. In addition, due to domain gaps between different datasets, the performance is dramatically decreased when re-ID models pre-trained on label-rich datasets (source domain) are directly applied to other unlabeled datasets (target domain). In this paper, we attempt to remedy these problems from two aspects, namely data and methodology. Firstly, we develop a data collector to automatically generate synthetic re-ID samples in a computer game, and construct a data labeler to simultaneously annotate them, which free humans from heavy data collections and annotations. Based on them, we build two synthetic person re-ID datasets with different scales,"GSPR"and"mini-GSPR"datasets. Secondly, we propose a synthesis-based multi-domain collaborative refinement (SMCR) network, which contains a synthetic pretraining module and two collaborative-refinement modules to implement sufficient learning for the valuable knowledge from multiple domains. Extensive experiments show that our proposed framework obtains significant performance improvements over the state-of-the-art methods on multiple unsupervised domain adaptation tasks of person re-ID.
Maximum Matching sans Maximal Matching: A New Approach for Finding Maximum Matchings in the Data Stream Model

The problem of finding a maximum size matching in a graph (known as the maximum matching problem) is one of the most classical problems in computer science. Despite a significant body of work dedicated to the study of this problem in the data stream model, the state-of-the-art single-pass semi-streaming algorithm for it is still a simple greedy algorithm that computes a maximal matching, and this way obtains 1/2-approximation. Some previous works described two/three-pass algorithms that improve over this approximation ratio by using their second and third passes to improve the above mentioned maximal matching. One contribution of this paper continuous this line of work by presenting new three-pass semi-streaming algorithms that work along these lines and obtain improved approximation ratios of 0.6111 and 0.5694 for triangle-free and general graphs, respectively. Unfortunately, a recent work (Konrad and Naidu, 2021) shows that the strategy of constructing a maximal matching in the first pass and then improving it in further passes has limitations. Additionally, this technique is unlikely to get us closer to single-pass semi-streaming algorithms obtaining a better than 1/2-approximation. Therefore, it is interesting to come up with algorithms that do something else with their first pass (we term such algorithms non-maximal-matching-first algorithms). No such algorithms are currently known (to the best of our knowledge), and the main contribution of this paper is describing such algorithms that obtain approximation ratios of 0.5384 and 0.5555 in two and three passes, respectively, for general graphs (the result for three passes improves over the previous state-of-the-art, but is worse than the result of this paper mentioned in the previous paragraph for general graphs).
RNA splicing programs define tissue compartments and cell types at single-cell resolution

The extent splicing is regulated at single-cell resolution has remained controversial due to both available data and methods to interpret it. We apply the SpliZ, a new statistical approach, to detect cell-type-specific splicing in >110K cells from 12 human tissues. Using 10X Chromium data for discovery, 9.1% of genes with computable SpliZ scores are cell-type-specifically spliced, including ubiquitously expressed genes MYL6 and RPS24. These results are validated with RNA FISH, single-cell PCR, and Smart-seq2. SpliZ analysis reveals 170 genes with regulated splicing during human spermatogenesis, including examples conserved in mouse and mouse lemur. The SpliZ allows model-based identification of subpopulations indistinguishable based on gene expression, illustrated by subpopulation-specific splicing of classical monocytes involving an ultraconserved exon in SAT1. Together, this analysis of differential splicing across multiple organs establishes that splicing is regulated cell-type-specifically.
Remote magnetic sensing and faithful quantum teleportation through non-localized qubits

One of the most important applications of quantum physics is quantum teleportation, the possibility to transfer quantum states over arbitrary distances. In this paper, we address the idea of remote magnetic sensing in a teleportation scenario with topological qubits more robust against noise. We also investigate the enhancement of quantum teleportation through non-local characteristics of the topological qubits. In particular, we show that how this nonlocal property, help us to achieve near-perfect quantum teleportation even with mixed quantum states. Moreover, the important role of non-Markovianity as a resource for quantum teleportation is discussed. Considering the limitations imposed by decoherence and the subsequent mixedness of the resource state, we find that our results may solve important challenges in realizing faithful teleportation over long distances.
Comparing Quantum Gravity Models: String Theory, Loop Quantum Gravity, and Entanglement Gravity versus SU(∞)-QGR

In a previous article we proposed a new model for quantum gravity (QGR) and cosmology, dubbed SU(∞)-QGR. One of the axioms of this model is that Hilbert spaces of the Universe and its subsystems represent the SU(∞) symmetry group. In this framework, the classical spacetime is interpreted as being the parameter space characterizing states of the SU(∞) representing Hilbert spaces. Using quantum uncertainty relations, it is shown that the parameter space—the spacetime—has a 3+1 dimensional Lorentzian geometry. Here, after a review of SU(∞)-QGR, including a demonstration that its classical limit is Einstein gravity, we compare it with several QGR proposals, including: string and M-theories, loop quantum gravity and related models, and QGR proposals inspired by the holographic principle and quantum entanglement. The purpose is to find their common and analogous features, even if they apparently seem to have different roles and interpretations. The hope is that this exercise provides a better understanding of gravity as a universal quantum force and clarifies the physical nature of the spacetime. We identify several common features among the studied models: the importance of 2D structures; the algebraic decomposition to tensor products; the special role of the SU(2) group in their formulation; the necessity of a quantum time as a relational observable. We discuss how these features can be considered as analogous in different models. We also show that they arise in SU(∞)-QGR without fine-tuning, additional assumptions, or restrictions.
Unique surface-state connection between Weyl and nodal ring fermions in ferromagnetic material 
Cs2MoCl6

For topological materials with coexistence of Weyl nodes and nodal rings, the surface-state configuration and connection are unique yet have never been studied and discussed before. In this paper, we predict a ferromagnetic (FM) material, Cs2MoCl6, with coexistence of Weyl and nodering fermions in its spinful FM electronic band structure, which is unusual since FM materials are very rare in nature and node-ring band crossings will usually open a gap when spin-orbit coupling (SOC) is taken into consideration. We find that the surface states of Cs2MoCl6 show different properties along different directions, i.e, the surface states are in the drumhead shape showing the node-ring property on the (001) surface and in the helicoid shape showing the Weyl property on the (010) surface. Interestingly, both the drumhead surface states and the helicoid surface states will cross the projected points of the Weyl and nodal ring along different directions. In particular, helicoid surface states on the (010) surface will meet the nodal ring tangentially, with their shapes change abruptly as a function of the energy. We implement both first-principle calculation and an analytical model to understand the unique surface-state connection for systems with the coexistence of Weyl nodes and nodal rings (or nodal lines). This result is universal and irrespective of the presence/absence of and time-reversal symmetry (T ).
Frequency Response Data-Based LPV Controller Synthesis Applied to a Control Moment Gyroscope

Control of systems with operating condition-dependent dynamics, including control moment gyroscopes (CMGs), often requires operating condition-dependent controllers to achieve high control performance. The aim of this brief is to develop a frequency response data-driven linear parameter-varying (LPV) control design approach for single-input single-output (SISO) systems, which allows improved performance for a CMG. A stability theory using a closed-loop frequency response function (FRF) data is developed, which is subsequently used in a synthesis procedure that guarantees local stability and performance. Experimental results on a CMG demonstrate the performance improvements.