Miracle-dz/UniFAR-FTUs huggingface dataset:
The Frequency of Use of Legislative and Non-Legislative Tools in Five Countries

This study analyzes the use of legislative and non-legislative tools, which has rarely been done simultaneously. I collected data about the frequency of use of legislative tools (presenting and passing legislation) and non-legislative tools (making one-minute speeches, written and oral parliamentary questions and motions for the agenda) in five countries: the US, the UK, Canada, Australia and Israel. The results confirm my three hypotheses. Legislators from Australia, the UK and Canada use fewer legislative tools because their use is more constrained than in the US and Israel. Legislators use more semi or unconstrained tools that involve publicity than those that simply appear on the record. Finally, opposition members use more non-legislative tools while government members use more legislative tools. However, the degree of constraint on the use of the tool moderates this finding. The study provides a comprehensive understanding of the legislators' strategic use of legislative and non-legislative tools.
Fast linear response algorithm for differentiating chaos

We devise the fast linear response algorithm for differentiating fractal invariant measures of chaos with respect to perturbations of governing equations. We first derive the first computable expansion formula for the unstable divergence, then a `fast' formula by renormalized second-order tangent equations. The algorithm's cost is solving only one orbit and $u$, the unstable dimension, many first-order and second-order tangent equations; the main error is the sampling error of the orbit. We demonstrate it on an numerical example which is difficult for previous methods.
Channelized Axial Attention - considering Channel Relation within Spatial Attention for Semantic Segmentation

Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases. In this paper, we propose Channelized Axial Attention (CAA) to seamlessly integrate channel attention and spatial attention into a single operation with negligible computation overhead. Specifically, we break down the dot-product operation of the spatial attention into two parts and insert channel relation in between, allowing for independently optimized channel attention on each spatial location. We further develop grouped vectorization, which allows our model to run with very little memory consumption without slowing down the running speed. Comparative experiments conducted on multiple benchmark datasets, including Cityscapes, PASCAL Context, and COCO-Stuff, demonstrate that our CAA outperforms many state-of-the-art segmentation models (including dual attention) on all tested datasets.
Absence of Luther-Emery superconducting phase in the three-band model for cuprate ladders

Correlated-electron theories of superconductivity in layered cuprates often start from the premise of a gapped spin-liquid phase proximate to the superconducting state. This assumption is justified based on analytical and numerical demonstrations of a superconducting Luther-Emery phase in the doped 2-leg one-band Hubbard ladder, and the perceived analogy between coupled ladders and the two dimensional CuO2 layer. We demonstrate from accurate density matrix renormalization group studies the absence of the superconducting Luther-Emery phase in the doped 2-leg three-band ladder consisting of both copper and oxygen, even as the spin gap is large in the undoped three-band ladder. For realistic oxygen-oxygen hopping and Hubbard repulsion on the oxygen atoms, density-density rather than pairing correlations are dominant at long range. This result is equally valid whether or not the oxygens outside the ladder proper, over and above the rung and leg oxygens, are included in the computation. These results demonstrate the critical importance of oxygen orbitals, and raise disturbing questions about the applicability of many of the existing correlated-electron theories of superconductivity.
Deep One-Class Classification via Interpolated Gaussian Descriptor

One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a one-class Gaussian anomaly classifier trained with adversarially interpolated training samples. The Gaussian anomaly classifier differentiates the training samples based on their distance to the Gaussian centre and the standard deviation of these distances, offering the model a discriminability w.r. t. the given samples during training. The adversarial interpolation is enforced to consistently learn a smooth Gaussian descriptor, even when the training data is small or contaminated with anomalous samples. This enables our model to learn the data description based on the representative normal samples rather than fringe or anomalous samples, resulting in significantly improved normality description. In extensive experiments on diverse popular benchmarks, including MNIST, Fashion MNIST, CIFAR10, MVTec AD and two medical datasets, IGD achieves better detection accuracy than current state-of-the-art models. IGD also shows better robustness in problems with small or contaminated training sets.
Tight FPT Approximation for Socially Fair Clustering

In this work, we study the socially fair $k$-median/$k$-means problem. We are given a set of points $P$ in a metric space $\mathcal{X}$ with a distance function $d(.,.)$. There are $\ell$ groups: $P_1,\dotsc,P_{\ell} \subseteq P$. We are also given a set $F$ of feasible centers in $\mathcal{X}$. The goal in the socially fair $k$-median problem is to find a set $C \subseteq F$ of $k$ centers that minimizes the maximum average cost over all the groups. That is, find $C$ that minimizes the objective function $\Phi(C,P) \equiv \max_{j} \Big\{ \sum_{x \in P_j} d(C,x)/|P_j| \Big\}$, where $d(C,x)$ is the distance of $x$ to the closest center in $C$. The socially fair $k$-means problem is defined similarly by using squared distances, i.e., $d^{2}(.,.)$ instead of $d(.,.)$. The current best approximation guarantee for both the problems is $O\left( \frac{\log \ell}{\log \log \ell} \right)$ due to Makarychev and Vakilian [COLT 2021]. In this work, we study the fixed parameter tractability of the problems with respect to parameter $k$. We design $(3+\varepsilon)$ and $(9 + \varepsilon)$ approximation algorithms for the socially fair $k$-median and $k$-means problems, respectively, in FPT (fixed parameter tractable) time $f(k,\varepsilon) \cdot n^{O(1)}$, where $f(k,\varepsilon) = (k/\varepsilon)^{{O}(k)}$ and $n = |P \cup F|$. Furthermore, we show that if Gap-ETH holds, then better approximation guarantees are not possible in FPT time.
Strong Numerical Methods of Orders 2.0, 2.5, and 3.0 for Ito Stochastic Differential Equations Based on the Unified Stochastic Taylor Expansions and Multiple Fourier-Legendre Series

Abstract. The article is devoted to the construction of explicit one-step strong numerical methods with the orders of convergence 2.0, 2,5, and 3.0 for Ito stochastic differential equations with multidimensional non-commutative noise. We consider the numerical methods based on the unified Taylor–Ito and Taylor–Stratonovich expansions. For numerical modeling of iterated Ito and Stratonovich stochastic integrals of multiplicities 1 to 6 we appling the method of multiple Fourier–Legendre series converging in the sense of norm in Hilbert space L2([t, T ] ), k = 1, . , 6. The article is addressed to engineers who use numerical modeling in stochastic control and for solving the non-linear filtering problem. The article can be interesting for the mathematicians who working in the field of high-order strong numerical methods for Ito stochastic differential equations.
Stabilizing Deep Tomographic Reconstruction

Tomographic image reconstruction with deep learning is an emerging field, but a recent landmark study reveals that several deep reconstruction networks are unstable for computed tomography (CT) and magnetic resonance imaging (MRI). Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missing in a deeply reconstructed image, and (3) decreased imaging performance with increased input data. On the other hand, compressed sensing (CS) inspired reconstruction methods do not suffer from these instabilities because of their built-in kernel awareness. For deep reconstruction to realize its full potential and become a mainstream approach for tomographic imaging, it is thus critically important to meet this challenge by stabilizing deep reconstruction networks. Here we propose an Analytic Compressed Iterative Deep (ACID) framework to address this challenge. ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement. Our study demonstrates that the deep reconstruction using ACID is accurate and stable, and sheds light on the converging mechanism of the ACID iteration under a Bounded Relative Error Norm (BREN) condition. In particular, the study shows that ACID-based reconstruction is resilient against adversarial attacks, superior to classic sparsity-regularized reconstruction alone, and eliminates the three kinds of instabilities. We anticipate that this integrative data-driven approach will help promote development and translation of deep tomographic image reconstruction networks into clinical applications.
Cooperative Multi-Agent Fairness and Equivariant Policies

We study fairness through the lens of cooperative multi-agent learning. Our work is motivated by empirical evidence that naive maximization of team reward yields unfair outcomes for individual team members. To address fairness in multi-agent contexts, we introduce team fairness, a group-based fairness measure for multi-agent learning. We then prove that it is possible to enforce team fairness during policy optimization by transforming the team's joint policy into an equivariant map. We refer to our multi-agent learning strategy as Fairness through Equivariance (Fair-E) and demonstrate its effectiveness empirically. We then introduce Fairness through Equivariance Regularization (Fair-ER) as a soft-constraint version of Fair-E and show that it reaches higher levels of utility than Fair-E and fairer outcomes than non-equivariant policies. Finally, we present novel findings regarding the fairness-utility trade-off in multi-agent settings; showing that the magnitude of the trade-off is dependent on agent skill.
Robust Max Entrywise Error Bounds for Sparse Tensor Estimation via Similarity Based Collaborative Filtering

Consider the task of estimating a 3-order n × n × n tensor from noisy observations of randomly chosen entries in the sparse regime. We introduce a similarity based collaborative ﬁltering algorithm for sparse tensor estimation and argue that it achieves sample complexity that nearly matches the conjectured computationally eﬃcient lower bound on the sample complexity for the setting of low-rank tensors. Our algorithm uses the matrix obtained from the ﬂattened tensor to compute similarity, and estimates the tensor entries using a nearest neighbor estimator. We prove that the algorithm recovers a low rank tensor with maximum entry-wise error (MEE) and mean-squared-error (MSE) decaying to 0 as long as each entry is observed independently with probability p = Ω( n − 3 / 2+ κ ) for any arbitrarily small κ > 0. More generally, we establish robustness of the estimator, showin that when arbitrary noise bounded by ε ≥ 0 is added to each observation, the estimation error with respect to MEE and MSE degrades by poly ( ε ). Consequently, even if the tensor may not have ﬁnite rank but can be approximated within ε ≥ 0 by a ﬁnite rank tensor, then the estimation error converges to poly ( ε ). Our analysis sheds insight into the conjectured sample complexity lower bound, showing that it matches the connectivity threshold of the graph used by our algorithm for estimating similarity between coordinates.
Mass and Rate of Hierarchical Black Hole Mergers in Young, Globular and Nuclear Star Clusters

Hierarchical mergers are one of the distinctive signatures of binary black hole (BBH) formation through dynamical evolution. Here, we present a fast semi-analytic approach to simulate hierarchical mergers in nuclear star clusters (NSCs), globular clusters (GCs) and young star clusters (YSCs). Hierarchical mergers are more common in NSCs than they are in both GCs and YSCs because of the different escape velocity. The mass distribution of hierarchical BBHs strongly depends on the properties of first-generation BBHs, such as their progenitor’s metallicity. In our fiducial model, we form black holes (BHs) with masses up to ∼103 M⊙ in NSCs and up to ∼102 M⊙ in both GCs and YSCs. When escape velocities in excess of 100 km s−1 are considered, BHs with mass >103 M⊙ are allowed to form in NSCs. Hierarchical mergers lead to the formation of BHs in the pair instability mass gap and intermediate-mass BHs, but only in metal-poor environments. The local BBH merger rate in our models ranges from ∼10 to ∼60 Gpc−3 yr−1; hierarchical BBHs in NSCs account for ∼10−2–0.2 Gpc−3 yr−1, with a strong upper limit of ∼10 Gpc−3 yr−1. When comparing our models with the second gravitational-wave transient catalog, we find that multiple formation channels are favored to reproduce the observed BBH population.
An overview of $\eta_c(1S)$, $\eta_c(2S)$ and $h_c(1P)$ physics at BESIII

With the help of the largest data samples of $J/\psi$ and $\psi(2S)$ events ever produced in $e^+e^-$ annihilations, the three singlet charmonium states, $\eta_c(1S)$, $\eta_c(2S)$ and $h_c(1P)$, have been extensively studied at the BESIII experiment. In this review, a survey on the most recent results, including a series of precision measurements and observations of their new decay modes, is presented, which indicates the further investigations on their decays are needed to understand their decay mechanisms and have precision tests of the theoretical models. At present, about eight times larger data samples of 10 billion $J/\psi$ events and 3 billion $\psi(3686)$ events were collected with the BESIII detector, and thus the prospects for the study of these three charmonium states is discussed extensively.
PROFILE AND ANTIMICROBIAL SUSCEPTIBILITY PATTERN OF URINARY BACTERIAL ISOLATES AT A TERTIARY CARE HOSPITAL IN CENTRAL INDIA

Aim: to study the antimicrobial susceptibility pattern in urinary bacterial isolates among patients admitted in a tertiary care hospital. Methods: A retrospective analysis was done for the susceptibility pattern of urinary bacterial isolates was done over a period of three years (April 2007 – March 2010) in a tertiary care hospital. Results: Among total 3759 urinary culture positive samples, commonest isolates were E. coli (61.59%) followed by Klebsiella spp. (25.46%), Pseudomonas spp. (3.72%), and Proteus spp. (0.85%) in gram negative whereas Staphylococcus aureus (5.69%) and Enterococcus spp. (1.33%) in gram positive organisms. Antibiotic susceptibility pattern of these isolates revealed that Amikacin susceptibility in gram negative isolates was more than 60% except for Proteus spp. Sensitivity to Imipenem was maximum (above 90%) while Co-trimoxazole, Norfloxacin, Cefotaxime and Nalidixic acid were not found to be effective. Among gram positive organisms, maximum susceptibility was seen to Nitrofurantoin and Vancomycin. Conclusion: Routine urine culture and susceptibility before therapy should be encouraged and periodic evaluation of predominant organisms and their antimicrobial susceptibility pattern should be studied for appropriate selection of antibiotic for effective management UTI cases.
Consortium Blockchains: Overview, Applications and Challenges

—The Blockchain technology has recently attracted increasing interests worldwide because of its potential to disrupt existing businesses and to revolutionize the way applications will be built, operated, consumed and marketed in the near future. While this technology was initially designed as an immutable and distributed ledger for preventing the double spending of cryptocurrencies, it is now foreseen as the core backbone of enterprises by enabling the interoperability and collaboration between organizations. In this context, consortium blockchains emerged as an interesting architecture concept that beneﬁts from the transactions’ efﬁciency and privacy of private blockchains, while leveraging the decentralized governance of public blockchains. Although many studies have been made on the blockchain technology in general, the concept of consortium blockchains has been very little addressed in the literature. To bridge this gap, this article provides a detailed analysis of consortium blockchains, in terms of architectures, technological components and applications. In particular, the underlying consensus algorithms are analyzed in details, and a general taxonomy is discussed. Then, a practical case study that focuses on the consortium blockchain technology Ethermint is performed in order to highlight its main advantages and limitations. Finally, various research challenges and opportunities are discussed.
Effects of Some Ethno Medicinal Plant Extracts on Botryodiplodia theobromae the Causal Organism of Yam (Dioscorea rotundata Poir) Rot

Post-harvest deterioration and rot caused by diverse microorganisms is the single most important factor militating against yam production in Nigeria. In an approach towards the development of ecofriendly antifungal compound in controlling yam rot, ethanol and aqueous extracts of six commonly available plants: Vernonia amygdalina, Ocimum gratissimum, Azadirachta indica, Cymbopogon citratus, Carica papaya and Citrus sinensis were tested in combination for their synergistic effect against Botryodiplodia theobromae. Four different extract concentrations (2.5%, 5.0%, 7.5% and 10%) were obtained from each extract mixture. Cold solvent extraction method was used for the extraction of plant materials while food poisoning technique was used for in vitro screening of plant extracts against rot inducing fungal organism. Botryodiplodia theobromae was tested to be pathogenic on healthy yam tubers with rot incidence of 80 mm. The synergistic effects of the combined extract varied with the plants combined, solvent of extraction, concentration of Original Research Article Chikwendu et al.; SAJRM, 6(4): 17-26, 2020; Article no.SAJRM. 57025 18 extract and test fungi. Ethanol extract showed greater effect in the inhibition of the entire organism than aqueous. All the extract combinations that had Neem as a component did better than other combinations in aqueous extracts. Inhibition of fungal growth was best observed at 7.5% and 10% extract concentrations. The highest inhibitory effect on the test organism (Botryodiplodia theobromae) was by the combination of Neem/Bitter leaf and Orange/Scent leaf, with values of 98.40±0.095% and 94.24±0.583% respectively for aqueous while the highest inhibition for ethanol extract was observed from Scent leaf/Bitter leaf and Pawpaw/Lemon, with values of 99.80±0.000% and 98.83±0.619% respectively these were significantly (P<0.05) better than other combinations. With respect to the synergistic activity between the plants materials combined in this study, all the combinations showed great synergism against the test organisms. This is likely to be a means of achieving pragmatic, effective control and prevention of food spoilage, since the development of new classes of antifungal agents is of paramount importance. The crude aqueous and ethanolic extracts of all the six plant extracts showed potential synergy on organisms responsible for yam rot, hence they are recommended for the control of rot inducing microbial organisms.
Reclassification of pholcodine to restricted medicine

Introduction In November 2018, the Medicines Classification Committee (MCC) reviewed the classification of pholcodine and other opiate-related cough and cold medicines due to concerns about their potential for abuse. The MCC considered that pholcodine has limited abuse potential and agreed the current classification as a pharmacy medicine is appropriate. However, the MCC noted that pholcodine is a ‘grandfathered’ medicine and that there is a possible link between pholcodine and anaphylaxis to neuromuscular blocking agents (NMBAs). The MCC therefore recommended that Medsafe should review the balance of benefits and risks for pholcodine.
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

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INFLUENCE OF DIFFERENT TYPES OF GRANULATED SUBSTRATE ON CORDYCEPS MILITARIS MUSHROOM MYCELIUM GROWTH INFLUENŢA DIFERITELOR TIPURI DE SUBSTRAT GRANULAT ASUPRA CREŞTERII MICELIULUI CIUPERCII CORDYCEPS MILITARIS

Mushroom mycelium is a biological preparate that is obtained under sterile laboratory conditions and which, in an optimal microclimate, can reproduce the mushrooms from which it originates. Mushroom mycelium is used to inoculate the prepared substrates. This inoculum consists of a supportive material completely colonized by fungal mycelium. The type of support material varies depending on the cultivated mushroom species, although rye is the choice of most mycelium producers. In our experience, we have been looking at how to grow Cordyceps militaris mushroom mycelium on various substrates of cereal grains. The growth was followed for 10 days under laboratory conditions, and the most intense increase was recorded on millet grains with an average increase of 1.65 mm/day and the worst growth was recorded in the case of rye with an increase average of 1.05 mm/day.