Miracle-dz/UniFAR-FTUs huggingface dataset:
Average-case hardness of estimating probabilities of random quantum circuits with a linear scaling in the error exponent

We consider the hardness of computing additive approximations to output probabilities of random quantum circuits. We consider three random circuit families, namely, Haar random, $p=1$ QAOA, and random IQP circuits. Our results are as follows. For Haar random circuits with $m$ gates, we improve on prior results by showing $\mathsf{coC_=P}$ hardness of average-case additive approximations to an imprecision of $2^{-O(m)}$. Efficient classical simulation of such problems would imply the collapse of the polynomial hierarchy. For constant depth circuits i.e., when $m=O(n)$, this linear scaling in the exponent is within a constant of the scaling required to show hardness of sampling. Prior to our work, such a result was shown only for Boson Sampling in Bouland et al (2021). We also use recent results in polynomial interpolation to show $\mathsf{coC_=P}$ hardness under $\mathsf{BPP}$ reductions rather than $\mathsf{BPP}^{\mathsf{NP}}$ reductions. This improves the results of prior work for Haar random circuits both in terms of the error scaling and the power of reductions. Next, we consider random $p=1$ QAOA and IQP circuits and show that in the average-case, it is $\mathsf{coC_=P}$ hard to approximate the output probability to within an additive error of $2^{-O(n)}$. For $p=1$ QAOA circuits, this work constitutes the first average-case hardness result for the problem of approximating output probabilities for random QAOA circuits, which include Sherrington-Kirkpatrick and Erd\"{o}s-Renyi graphs. For IQP circuits, a consequence of our results is that approximating the Ising partition function with imaginary couplings to an additive error of $2^{-O(n)}$ is hard even in the average-case, which extends prior work on worst-case hardness of multiplicative approximation to Ising partition functions.
Multiple phase spirals suggest multiple origins in Gaia DR3

Gaia Data Release 2 (DR2) revealed that the Milky Way contains significant indications of departures from equilibrium in the form of asymmetric features in the phase space density of stars in the Solar neighborhood. One such feature is the z–vz phase spiral, interpreted as the response of the disk to the influence of a perturbation perpendicular to the disk plane, which could be external (e.g., a satellite) or internal (e.g., the bar or spiral arms). In this work we use Gaia DR3 to dissect the phase spiral by dividing the local data set into groups with similar azimuthal actions, Jφ, and conjugate angles, θφ, which selects stars on similar orbits and at similar orbital phases, thus having experienced similar perturbations in the past. These divisions allow us to explore areas of the Galactic disk larger than the surveyed region. The separation improves the clarity of the z–vz phase spiral and exposes changes to its morphology across the different action-angle groups. In particular, we discover a transition to two armed ‘breathing spirals’ in the inner Milky Way. We conclude that the local data contains signatures of not one, but multiple perturbations with the prospect to use their distinct properties to infer the properties of the interactions that caused them.
Discovering Object Masks with Transformers for Unsupervised Semantic Segmentation

The task of unsupervised semantic segmentation aims to cluster pixels into semantically meaningful groups. Specifically, pixels assigned to the same cluster should share high-level semantic properties like their object or part category. This paper presents MaskDistill: a novel framework for unsupervised semantic segmentation based on three key ideas. First, we advocate a data-driven strategy to generate object masks that serve as a pixel grouping prior for semantic segmentation. This approach omits handcrafted priors, which are often designed for specific scene compositions and limit the applicability of competing frameworks. Second, MaskDistill clusters the object masks to obtain pseudo-ground-truth for training an initial object segmentation model. Third, we leverage this model to filter out low-quality object masks. This strategy mitigates the noise in our pixel grouping prior and results in a clean collection of masks which we use to train a final segmentation model. By combining these components, we can considerably outperform previous works for unsupervised semantic segmentation on PASCAL (+11% mIoU) and COCO (+4% mask AP50). Interestingly, as opposed to existing approaches, our framework does not latch onto low-level image cues and is not limited to object-centric datasets. The code and models will be made available.
Modeling Generalized Specialist Approach To Train Quality Resilient Snapshot Ensemble

Convolutional neural networks (CNNs) apply well with food image recognition due to the ability to learn discriminative visual features. Nevertheless, recognizing distorted images is challenging for existing CNNs. Hence, the study modelled a generalized specialist approach to train a quality resilient ensemble. The approach aids the models in the ensemble framework retain general skills of recognizing clean images and shallow skills of classifying noisy images with one deep expertise area on a particular distortion. Subsequently, a novel data augmentation random quality mixup (RQMixUp) is combined with snapshot ensembling to train G-Specialist. During each training cycle of G-Specialist, a model is fine-tuned on the synthetic images generated by RQMixup, intermixing clean and distorted images of a particular distortion at a randomly chosen level. Resultantly, each snapshot in the ensemble gained expertise on several distortion levels, with shallow skills on other quality distortions. Next, the filter outputs from diverse experts were fused for higher accuracy. The learning process has no additional cost due to a single training process to train experts, compatible with a wide range of supervised CNNs for transfer learning. Finally, the experimental analysis on three real-world food and a Malaysian food database showed significant improvement for distorted images with competitive classification performance on pristine food images.
Unraveling the Observational Signatures of Cloud–Cloud Collision and Hub-filament Systems in W31

To understand the formation process of massive stars, we present a multiscale and multiwavelength study of the W31 complex hosting two extended H ii regions (i.e., G10.30-0.15 (hereafter, W31-N) and G10.15-0.34 (hereafter, W31-S)) powered by a cluster of O-type stars. Several Class i protostars and a total of 49 ATLASGAL 870 μm dust clumps (at d = 3.55 kpc) are found toward the H ii regions where some of the clumps are associated with the molecular outflow activity. These results confirm the existence of a single physical system hosting the early phases of star formation. The Herschel 250 μm continuum map shows the presence of a hub-filament system (HFS) toward both W31-N and W31-S. The central hubs harbor H ii regions and they are depicted with extended structures (with T d ∼ 25–32 K) in the Herschel temperature map. In the direction of W31-S, an analysis of the NANTEN2 12CO(J = 1−0) and SEDIGISM 13CO(J = 2−1) line data supports the presence of two cloud components around 8 and 16 km s−1, and their connection in velocity space. A spatial complementary distribution between the two cloud components is also investigated toward W31-S, where the signposts of star formation, including massive O-type stars, are concentrated. These findings favor the applicability of cloud–cloud collision (CCC) around ∼2 Myr ago in W31-S. Overall, our observational findings support the theoretical scenario of CCC in W31, which explains the formation of massive stars and the existence of HFSs.
The Gender Gap in Scholarly Self-Promotion on Social Media

Self-promotion in science is ubiquitous but may not be exercised equally by men and women. Research on self-promotion in other domains suggests that, due to bias in self-assessment and adverse reactions to non-gender-conforming behaviors (``pushback''), women tend to self-promote less often than men. We test whether this pattern extends to scholars by examining self-promotion over six years using 23M Tweets about 2.8M research papers by 3.5M authors. Overall, women are about 28% less likely than men to self-promote their papers even after accounting for important confounds, and this gap has grown over time. Moreover, differential adoption of Twitter does not explain the gender gap, which is large even in relatively gender-balanced broad research areas, where bias in self-assessment and pushback are expected to be smaller. Further, the gap increases with higher performance and status, being most pronounced for productive women from top-ranked institutions who publish in high-impact journals. Critically, we find differential returns with respect to gender: while self-promotion is associated with increased tweets of papers, the increase is smaller for women than for men. Our findings suggest that self-promotion varies meaningfully by gender and help explain gender differences in the visibility of scientific ideas.
Scheduling Delays and Curtailment for Household Appliances With Deterministic Load Profiles Using MPC

Smart home appliances can time-shift and curtail their power demand to assist demand side management or allow operation with limited power, as in an off-grid application. This letter proposes a scheduling process to start appliances with time-varying deterministic load profiles. Self-triggered model predictive control is used to limit the household net power demand below a given threshold. Meanwhile, deterministic load profiles are more difficult to schedule compared to variable charging or thermal loads because system failure will occur once power demand is not satisfied. The proposed scheme formulates the decision of the load shifting time as a continuous optimization problem, and an inhomogeneous time grid system is introduced to handle the optimization of different appliances and their consensus at this resolution. The efficacy of the proposed scheme is studied by numerical comparison with a mixed-integer MPC controller and by a case study of three home appliances and an interruptible washing machine.
A multi-objective constrained POMDP model for breast cancer screening

Breast cancer is a common and deadly disease, but it is often curable when diagnosed early. While most countries have large-scale screening programs, there is no consensus on a single globally accepted policy for breast cancer screening. The complex nature of the disease; limited availability of screening methods such as mammography,magnetic resonance imaging (MRI), and ultrasound screening; and public health policies all factor into the development of screening policies. Resource availability concerns necessitate the design of policies which conform to a budget, a problem which can be modelled as a constrained partially observable Markov decision process (CPOMDP). In this study, we propose a multi-objective CPOMDP model for breast cancer screening with two objectives: minimize the lifetime risk of dying due to breast cancer and maximize the quality-adjusted life years. Additionally, we consider an expanded action space which allows for screening methods beyond mammography. Each action has a unique impact on quality-adjusted life years and lifetime risk, as well as a unique cost. Our results reveal the Pareto frontier of optimal solutions for average and high risk patients at different budget levels, which can be used by decision makers to set policies in practice.
Hierarchical Primitive Composition: Simultaneous Activation of Skills with Inconsistent Action Dimensions in Multiple Hierarchies

Deep reinforcement learning has shown its effectiveness in various applications, providing a promising direction for solving tasks with high complexity. However, naively applying classical RL for learning a complex long-horizon task with a single control policy is inefficient. Thus, policy modularization tackles this problem by learning a set of modules that are mapped to primitives and properly orchestrating them. In this study, we further expand the discussion by incorporating simultaneous activation of the skills and structuring them into multiple hierarchies in a recursive fashion. Moreover, we sought to devise an algorithm that can properly orchestrate the skills with different action spaces via multiplicative Gaussian distributions, which highly increases the reusability. By exploiting the modularity, interpretability can also be achieved by observing the modules that are used in the new task if each of the skills is known. We demonstrate how the proposed scheme can be employed in practice by solving a pick and place task with a 6 DoF manipulator, and examine the effects of each property from ablation studies.
Empirical and Instance–Dependent Estimation of Markov Chain and Mixing Time

We address the problem of estimating the mixing time of a Markov chain from a single trajectory of observations. Unlike most previous works which employed Hilbert space methods to estimate spectral gaps, we opt for an approach based on contraction with respect to total variation. Specifically, we estimate the contraction coefficient introduced in Wolfer (2020), inspired from Dobrushin's. This quantity, unlike the spectral gap, controls the mixing time up to strong universal constants and remains applicable to non–reversible chains. We improve existing fully data–dependent confidence intervals around this contraction coefficient, which are both easier to compute and thinner than spectral counterparts. Furthermore, we introduce a novel analysis beyond the worst–case scenario by leveraging additional information about the transition matrix. This allows us to derive instance–dependent rates for estimating the matrix with respect to the induced uniform norm, and some of its mixing properties.This article is protected by copyright. All rights reserved.
Environmental, Social, Governance scores and the Missing pillar—Why does missing information matter?

Environmental, Social, and Governance (ESG) scores measure companies’ performance concerning sustainability and are organized on three pillars: Environmental, Social, and Governance. These complementary non-ﬁnancial ESG scores should provide information about companies’ ESG performance and risks. However, the extent of not yet published ESG information makes the reliability of ESG scores questionable. To explicitly capture the not yet published information on ESG category scores, a new pillar, the so-called Missing (M) pillar, is proposed and added to the new deﬁnition of the Environmental, Social, Governance, and Missing (ESGM) scores. By relying on the data provided by Reﬁnitiv, we show that the ESGM scores strengthen the companies’ risk relationship. These new scores could beneﬁt investors and practitioners as ESG exclusion strategies using only ESG scores might exclude assets with a low score solely because of their missing information and not necessarily because of a low ESG merit.
EFFECT OF ALCOHOLIC SEED EXTRACT OF CAESALPINIA BONDUCELLA ON MORPHOMETRY OF ENDOCRINE BETA CELL IN NORMAL AND ALLOXAN INDUCED DIABETIC MALE ALBINO RATS

Present study was designed to evaluate the efficacy of seed extract of Caesalpinia bonducella a medicinal plant in healing and ameliorating pancreatic beta cell in alloxan induced diabetic male albino rats. The rats were divided into six groups Normal-Control, Diabetic-Control, Normal+200mg ASECB, Normal+400mg ASECB, Diabetic+200mg ASECB and Diabetic+400mg ASECB. Rats made diabetic by intra peritoneal injection of alloxan. Oral administration of different doses of ASECB to normal and diabetic rats was conducted for a period of 14 days. Morphometry of beta cell and its nucleus was studied. Diabetic control group showed a significant (P<0.05) fall in cellular and nuclear diameter of beta cell, while the diabetic rats treated with different doses of ASECB showed a significant increase in cellular and nuclear diameter. In normal treated rats, the cellular and nuclear size of beta cell more or less similar to control rats indicating that the plant extract has no side effects. The higher dose of 400mg is more effective than 200mg. It is evident from the present histological study that ASECB can ameliorate and restore the cellular and nuclear diameter of beta cell to normalcy. Thus ASECB could serve as a good healing agent.
The Antioxidant Effect of p-Coumaric Acid Against Toluene-Induced Oxidative Stress in Rats

This research aimed to investigate the protective effect of p-CA, a derivative of phenolic acid, against toluene-induced oxidative damage. A total of 32 Sprague-Dawley male rats, 8 in each group, were used. A total of 4 groups were formed as control, toluene, p-CA and toluene+pCA. Animals in the control group, toluene group and p-CA group were given 0.9 % NaCl, 0.9 mg kg-1 b.w toluene and 100 mg kg-1 b.w p-CA orally for 21 days, respectively. The animals in toluene+p-CA group were received p-CA for 3 days and from day 4, toluene and p-CA were applied together daily until day 25. On the 25th day, the study was terminated, and blood samples were collected. Catalase (CAT) and glutathione peroxidase (GSH-Px) activities and malondialdehyde (MDA) and glutathione (GSH) levels in the erythrocytes and superoxide dismutase (SOD) activity in plasma samples were determined. In this study, CAT and SOD activities and MDA level significantly (p <0.05) increased, GSH level and GSH-Px activity significantly (p <0.05) decreased in the blood samples of toluene group compared to the control group. In toluene+p-CA group, significant (p <0.05) increases in GSH level and GSH-Px activity and significant (p <0.05) decreases in MDA level and SOD and CAT activities were detected compared to the toluene group. It has been determined that toluene caused oxidative stress and lipid peroxidation in blood tissue and against this oxidative damage, p-CA had a protective effect. Research Article Article History Received : 06.07. 2020 Accepted : 16.09. 2020
CMAR_A_258689 6177..6185

Feifei Sun * Suying Lu* Zijun Zhen Jia Zhu 1,2 Juan Wang Junting Huang 1,2 Yu Zhang Hui Li Ruiqing Cai Meiling Liu Liuhong Wu Xiaofei Sun Yizhuo Zhang 1State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China; 2Department of Pediatric Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China; 3Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China; 4Department of Imaging, Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China
Antecedents of value from inter-organisational collaboration

Collaboration is widely identified as a force for good, with a wide range of benefits attributed to it, but considerable variations exist in descriptions of exactly what constitutes collaboration and how it is undertaken in practice. In the face of this diversity it becomes difficult to understand how effectively the process of collaboration is being undertaken. In this research, value concept principles are adopted in an exploration of collaboration processes in order to derive a better understanding of how organisations establish new collaborations, and to gain insights into the situational factors and human behaviour that may lead to improved collaboration effectiveness. The research design featured a constructivist version of grounded theory used in conjunction with the complementary techniques of Situational Analysis. This design is particularly suited to the research context in which social processes feature prominently and in which the objectives include theory development. A topology of eight generic categories of process is presented in the findings as part of a central category that links temporal, behavioural and situational factors to collaboration outcomes. The identification of social capital and human capital as intermediate forms of value, located in individual actors and their social relationships, is used to highlight the importance of recognising and developing these soft capital forms, if more tangible physical and financial capital is to be generated. Collectively the data emphasise that organisations do not collaborate, people do. Collaboration is fundamentally a social rather than business process. Three dimensions of collaborative compatibility are discussed that recognise the importance of involving competent individuals that are socially compatible if relationships between potentially compatible organisations are to thrive. Organisational compatibility alone will not lead to effective collaboration. This study makes four contributions to existing knowledge. A 3-dimensional model of collaborative compatibility, interpreted in the context of a new typology of collaboration processes, extends existing collaboration theory with insights into the way collaboration relationships form and perform, and the way they are affected by individual and social factors. The recognition of latency in intermediate forms of value makes a contribution to a recognised shortfall in understanding of the temporal dimension of the value literature. In the third, a contribution is made to literature on coopetition and the coordination of inter-firm groups, through the recognition of effective practices in 3rd party brokering organisations. Finally, an incremental contribution is made to the extensive body of knowledge and learning literature through insights into the social factors driving knowledge transfer in inter-organisational groups, and the implications these have for organisational knowledge absorption. These four avenues each have practical implications for how organisations and policy makers plan initiatives to increase economic activity through inter-firm collaboration.
A NUMERICAL INVESTIGATION ON THE UNSTABLE FLOW IN A SINGLE STAGE OF AN AXIAL COMPRESSOR

An unsteady two-dimensional finite-volume solver was developed based on Van Leer’s flux splitting algorithm in conjunction with “Monotonic Upstream Scheme for Conservation Laws (MUSCL)” limiters to improve the order of accuracy and the two-layer Baldwin-Lomax turbulence model was also implemented. Two test cases were prepared to validate the solver. The computed results were compared with the experimental data and a good agreement validated the solver. Finally, the solver was used for the flow through a multi-blade stage of an axial compressor in its off-design condition. The computed results showed a rotating stall-like instability with a periodic behavior. To investigate the flow properties during the instability condition, the flow pattern, Vortex properties and the axial velocity were studied. It was concluded that the instability vortices in the multi-blade cascade do not have the same generation history of the separated vortices over a single body.