Miracle-dz/UniFAR-FTUs huggingface dataset:
Absence of renormalization group pathologies in some critical Dyson-Ising ferromagnets

The Dyson-Ising ferromagnet is a one-dimensional Ising model with a power law interaction. When the power is between -1 and -2, the model has a phase transition. Van Enter and Le Ny proved that at sufficiently low temperatures the decimation renormalization group transformation is not defined in the sense that the renormalized measure is not a Gibbs measure. We consider a modified model in which the nearest neighbor couplings are much larger than the other couplings. For a family of Hamiltonians which includes critical cases, we prove that the first step of the renormalization group transformation can be rigorously defined for majority rule and decimation.
Emerging Biometrics: Deep Inference and Other Computational Intelligence

This paper aims at identifying emerging computational intelligence trends for the design and modeling of complex biometric-enabled infrastructure and systems. Biometric-enabled systems are evolving towards deep learning and deep inference using the principles of adaptive computing, - the front tides of the modern computational intelligence domain. Therefore, we focus on intelligent inference engines widely deployed in biometrics. Computational intelligence applications that cover a wide spectrum of biometric tasks using physiological and behavioral traits are chosen for illustration. We highlight the technology gaps that must be addressed in future generations of biometric systems. The reported approaches and results primarily address the researchers who work towards developing the next generation of intelligent biometric-enabled systems.
Composição florística e análise fitossociológica do componente arbustivo-arbóreo de um remanescente florestal no Agreste Paraibano

A floristic and phytosociological survey was conducted in a caatinga area in the Agreste (sub-humid) region of Paraiba state. All trees and shrubs ³ taller than 1m and larger than ³ 3cm stem diameter found in 30 plots, 10x20m each, were identified and measured. Standing dead plants were also included. A total of 1952 plants were registered, belonging to 22 families, 38 genera and 54 species. Families with most species were Mimosaceae (6 species), Euphorbiaceae (6), Caesalpiniaceae (5), and Rubiaceae (5). Total plant density and stem basal area were 3253 plant ha-1 and 34.77m2 ha-1. Maximum diameter and height were 63cm and 15m. About half of the plants (45.7%) had a stem diameter between 3 and 6cm. Thiloa glaucocarpa (Mart.) Eichl. was the most important species (highest IVI value). The flora included mostly species already registered in other caatinga areas but also species which are typical from more mesic formations, specially the humid forests of high altitudes in Northeast Brazil.
Influence of abiotic and biotic factors on microcystin content in Microcystis aeruginosa cells in a eutrophic temperate reservoir

The Sulejow Reservoir (Poland) was sampled on a weekly basis between May and September in 2003 and 2004 to examine changes in weight-specific microcystin content of Microcystis aeruginosa and to examine which abiotic or biotic factors may be the key factors governing microcystin content. The variables examined in this study included: temperature; total and dissolved inorganic phosphorus and nitrogen; and Daphnia biomass. Comparing summertime from both years, despite the similar levels of cyanobacterial biomass, a significant difference between microcystin concentrations was observed which resulted from a difference in microcystin content. Differences in weight-specific microcystin content were discussed in relation to different Daphnia biomass (r ¼ 0.34, n ¼ 40, P , 0.05). It is possible that exposure to Daphnia and/or chemical signals released by predators may have resulted in increased microcystin content of M. aeruginosa. Nevertheless, the influence of weight-specific microcystin content on microcystin concentrations (r ¼ 0.56, n ¼ 40, P , 0.05) was less than that of cyanobacterial biomass (r ¼ 0.82, n ¼ 40, P , 0.05), which was strongly correlated with temperature and phosphorus ability. This study indicated that not only abiotic factors, but also the presence of herbivorous zooplankton, may determine microcystin content of M. aeruginosa.
Attention-based quantum tomography

With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. There has been a growing interest in approaching the problem of quantum state reconstruction using generative neural network models. Here we propose the ‘attention-based quantum tomography’ (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. AQT is based on the model proposed in ‘Attention is all you need’ by Vaswani et al (2017 NIPS) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing (NLP) models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. We speculate the success of the AQT stems from its ability to model quantum entanglement across the entire quantum system much as the attention model for NLP captures the correlations among words in a sentence.
CONTRACTILE PROTEINS IN PLATELET ACTIVATION AND CONTRACTION *

Our role in this annal is to review recent progress in characterizing platelet contractile proteins and to suggest how contractile proteins may participate in platelet activation and clot retraction. There has been a rapid expansion of knowledge about the molecular basis of cell motility since 1970, and platelets have been one of the important model systems. Readers interested in the exciting advances made in other aspects of the motility question can refer to recent books and reviews 1-3 on the subject.
A possible polar origin for the FRB associated with a Galactic magnetar

Fast Radio Bursts (FRBs) are millisecond-long radio pulses of extragalactic origin with peak luminosities far exceeding any Milky Way sources. The prevalent invocation for the FRB origin involves magnetars: young, magnetically powered neutron stars with the strongest magnetic fields in the Universe. A magnetar-defining signature is the emission of bright, hard X-ray bursts of sub-second duration. These occur in isolation or during a burst storm, when several hundred are observed within minutes to hours. On April 27th 2020, the Galactic magnetar SGR J1935+2154 entered an active period, emitting hundreds of X-ray bursts in a few hours. Remarkably, only one of these temporally coincided with an FRB-like radio burst. Here we report on the spectral and temporal analyses of 24 X-ray bursts emitted 13 hours prior to the FRB and seen simultaneously with NASA NICER and Fermi/GBM missions in their combined energy range. We demonstrate that the FRB-associated X-ray burst is very similar temporally, albeit strikingly different spectrally, from the 24 NICER/GBM bursts. If the FRB-associated burst were drawn from this magnetar burst population, its occurrence rate would be at most around 1 in 7000. This rarity combined with the unusual X-ray burst spectrum is perhaps indicative of an uncommon locale for the origin of the FRB-associated burst. We suggest that this unique event originated in quasi-polar open or closed magnetic field lines extending to high altitudes where radio emission can be generated, possibly from a collimated plasma flow.
A Study of the Biology of the Wild Rabbit in Climatically Different Regions in Eastern Australia V*. The Use of Aerial Surveys to Map the Distribution of Rabbit Warrens over Large Areas of Semiarid North-western New South Wales

Aerial surveys can be used to measure the distribution of rabbit warrens over large areas of arid and semiarid Australia. Clumped patterns of distribution of rabbit warrens found by ground surveys are also detected in aerial surveys, which further show that the highest density of warrens occurs in habitats containing shales or other fine-grained rocks overlain by recent sediments, or those closely related to drainage systems. A good picture of rabbit distribution is obtained when about 25% of the area has been overed by the aircraft. * Part IV, Aust. J. Zool. , 1971, 19, 355–70.
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning

Multi-agent reinforcement learning (MARL) achieves significant empirical successes. However, MARL suffers from the curse of many agents. In this paper, we exploit the symmetry of agents in MARL. In the most generic form, we study a mean-field MARL problem. Such a mean-field MARL is defined on mean-field states, which are distributions that are supported on continuous space. Based on the mean embedding of the distributions, we propose MF-FQI algorithm that solves the mean-field MARL and establishes a non-asymptotic analysis for MF-FQI algorithm. We highlight that MF-FQI algorithm enjoys a "blessing of many agents" property in the sense that a larger number of observed agents improves the performance of MF-FQI algorithm.
Pathological Studies of A / Chicken / Tehran / ZMT - 173/99 (H9N2) Influenza Virus in Commercial Broiler Chickens of Iran

Avian influenza (AI) outbreaks due to H9N2 subtype of avian influenza virus (AIV) occurred in poultry industry in Iran, in 1998 and caused serious economic losses. The aim of this study was to investigate the pathogenesis, clinical signs, gross and histopathological findings of the chickens experimentally inoculated with A/Chicken/Tehran/ZMT-173/99 (H9N2) influenza virus, isolated from the kidney of the broiler chickens with 40% mortality. Two groups of 30-day-old, forty Mycoplasma gallisepticum positive (Mg+) and forty negative (Mg-) commercial broiler chickens were used. Each group subdivided into 10-membered two experimental and two control subgroups. One experimental subgroup inoculated intravenously (IV) and the other both IV and oculonasally (ON) with 10 ELD50. Clinically, depression, crouching, huddling, ruffled 7.5 feathers, coughing, sneezing, and sometimes gasping were observed. Mortality rate was 10% in each Mg+ experimental subgroup. The gross lesions in dead birds included exudative (fibrinous) casts in trachea l bifurcation, pulmonary congestion, thickened air sacs, swollen kidney with urate deposition, enlarged congested and hemorrhagic bursa of fabricious and thymus, petechial hemorrhages in epicardial fat and general congestion in the carcasses. The visceral organs were congested and edematous. Fibrinous tracheal casts in bifurcation were only observed in a chicken inoculated via both IV and ON routs . Histopathologic examination revealed extensive pulmonary hyperemia with infiltration of mononuclea r inflammatory cells in lamina propria of bronchi and lungs, mild lymphocytolysis and atrophy of lymphoi d follicles of the bursa of fabricious. Severe congestion, depletion of lymphocyte population and focal necrosis were seen in thymus lobules. Severe congestion, urate deposition and nonsuppurative focal interstitial nephritis was the predominant histological lesions. The results of the present study suggest renal lesions and tracheal casts are the principal causes of mortality and simultaneous inoculation play an important role in formation of tracheal casts. Also, immunosuppression due to depletion of lymphoid organs as well as concurrent infections such as M. gallisepticum might increase the pathogenic potential of H9N2 subtype to chicken.
Paying more Attention to Snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation

Network pruning is one of the most dominant methods for reducing the heavy inference cost of deep neural networks. Existing methods often iteratively prune networks to attain high compression ratio without incurring significant loss in performance. However, we argue that conventional methods for retraining pruned networks (i.e., using small, fixed learning rate) are inadequate as they completely ignore the benefits from snapshots of iterative pruning. In this work, we show that strong ensembles can be constructed from snapshots of iterative pruning, which achieve competitive performance and vary in network structure. Furthermore, we present simple, general and effective pipeline that generates strong ensembles of networks during pruning with large learning rate restarting, and utilizes knowledge distillation with those ensembles to improve the predictive power of compact models. In standard image classification benchmarks such as CIFAR and Tiny-Imagenet, we advance state-of-the-art pruning ratio of structured pruning by integrating simple l1-norm filters pruning into our pipeline. Specifically, we reduce 75-80% of total parameters and 65-70% MACs of numerous variants of ResNet architectures while having comparable or better performance than that of original networks. Code associate with this paper is made publicly available at this https URL.
Origin of universality in the onset of superdiffusion in Lévy walks

Superdiffusion arises when complicated, correlated and noisy motion at the microscopic scale conspires to yield peculiar dynamics at the macroscopic scale. It ubiquitously appears in a variety of scenarios, spanning a broad range of scientific disciplines. The approach of superdiffusive systems towards their long-time, asymptotic behavior was recently studied using the Levy walk of order $1<\beta<2$, revealing a universal transition at the critical $\beta_{c}=3/2$. Here, we investigate the origin of this transition and identify two crucial ingredients: a finite velocity which couples the walker's position to time and a corresponding transition in the fluctuations of the number of walks $n$ completed by the walker at time $t$.
Physical-Virtual Collaboration Modeling for Intra- and Inter-Station Metro Ridership Prediction

Due to the widespread applications in real-world scenarios, metro ridership prediction is a crucial but challenging task in intelligent transportation systems. However, conventional methods either ignore the topological information of metro systems or directly learn on physical topology, and cannot fully explore the patterns of ridership evolution. To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs. Specifically, a physical graph is directly built based on the realistic topology of the studied metro system, while a similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation. These complementary graphs are incorporated into a Graph Convolution Gated Recurrent Unit (GC-GRU) for spatial-temporal representation learning. Further, a Fully-Connected Gated Recurrent Unit (FC-GRU) is also applied to capture the global evolution tendency. Finally, we develop a Seq2Seq model with GC-GRU and FC-GRU to forecast the future metro ridership sequentially. Extensive experiments on two large-scale benchmarks (e.g., Shanghai Metro and Hangzhou Metro) well demonstrate the superiority of our PVCGN for station-level metro ridership prediction. Moreover, we apply the proposed PVCGN to address the online origin-destination (OD) ridership prediction and the experiment results show the universality of our method. Our code and benchmarks are available at https://github.com/HCPLab-SYSU/PVCGN.
Differentially Private Convex Optimization with Feasibility Guarantees

This paper develops a novel differentially private framework to solve convex optimization problems with sensitive optimization data and complex physical or operational constraints. Unlike standard noise-additive algorithms, that act primarily on the problem data, objective or solution, and disregard the problem constraints, this framework requires the optimization variables to be a function of the noise and exploits a chance-constrained problem reformulation with formal feasibility guarantees. The noise is calibrated to provide differential privacy for identity and linear queries on the optimization solution. For many applications, including resource allocation problems, the proposed framework provides a trade-off between the expected optimality loss and the variance of optimization results.
The impact of water buffalo on the monsoon forest ecosystem in Kakadu National Park

The intensity of use of forested country in the Kakadu National Park area by feral Asiatic water buffalo can be largely predicted from the area of river floodplain in the vicinity of the site, and tree canopy cover on the site. Thus some areas of monsoon forest appear to have sustained intensive use by buffalo for over a century whereas other areas have probably been little used. With sites exhibiting little buffalo use, mean DBH of trees increased with foliage height diversity (FHD). However, the sites more intensively used by buffalo had a lower canopy and did not exhibit any increase in mean DBH with a pronounced increase in FHD. Other vegetational relationships displayed by the two groups of sites are discussed. Comparison of the monsoon forest sites suggests that compaction of soil by buffalo has caused the death of large trees due to poor recharge of groundwater. The two groups of monsoon forest sites exhibited a wide variety of faunal relationships with FHD. Some species appear to be favoured while others are disadvantaged to various degrees by the habitat changes caused by the buffalo. The most intensively used site appears to have exceeded a threshold in that a number of faunal trends were reversed on that site. The impact of buffalo on some monsoon forest sites is much more fundamental than anticipated. But other sites which appear to be naturally protected by their location have experienced much less change due to buffalo.
Genetic analyses of lamb survival in Rambouillet and Finnsheep flocks by linear and threshold models

Data on lamb survival from birth to weaning of Rambouillet and Finnsheep were analysed with sire-maternal grandsire linear (LM) and threshold (TM) models. Models for the Rambouillet included the effects of year, type of birth, age of dam and sex as fixed effects and sire, maternal grandsire and residual as random effects. For the Finnsheep, fixed effects were year-age of dam combination, type of birth-rearing and sex, and random effects were sire, maternal grandsire and residual. Restricted maximum likelihood (REML) and marginal maximum likelihood (MML) estimates of variance and covariance components were obtained under LM and TM, respectively. The performance of LM and TM was assessed in terms of goodness of fit and predictive ability. Within the Rambouillet breed, heritabilities of additive direct (0·06), additive maternal (0·04) and total effects (0·13) obtained with TM were 2, 1·3 and 1·9 times greater than those obtained with LM, respectively. For the Finnsheep data, estimated heritabilities of direct (0·17), maternal (0·26) and total (0·34) effects using TM were 1·9, 1·4 and 1·6 times greater than the estimates using LM, respectively. Estimated genetic correlation between direct and maternal effects was 0·14 for the Finnsheep with both the LM and TM. Higher genetic correlations between direct and maternal effects (0·44 and 0·62 with LM and TM, respectively) were obtained for the Rambouillet data. Goodness of fit and predictive ability of the models used with the Finnsheep data were better than for the models used with the Rambouillet data, but within breed, no sizable or significant differences were detected between LM and TM. Results indicate that maternal effects were important in lamb survival to weaning; especially in the more prolific Finnsheep breed.
The hair cuticle of mammals and its relationship to functions of the hair coat

Based on a computer-assisted method and regression analysis several structural parameters of the hair cuticle pattern of primary hairs of 94 mammalian species from seven orders were evaluated and compared between the different groups studied. Relevant relationships could only be demonstrated for directly scale-dependent parameters (scale area, scale perimeter, ratio of scale width to height). However, the species-specific shape and size of hair cuticle scales in mammals may be of certain value for biological interpretations of hair coat structure and function. This seems to be especially true for the Canidae and the Felidae, and even more for small mammals (insectivores, chiropterans, small rodents). In the latter group relationships between scale-dependent parameters and hair coat density indicate a homogeneity of hair coat structure which reflects effective insulation qualities.
Determination of pathotypes and physiological races in Ascochyta rabiei, the agent of ascochyta blight in chickpea (Cicer arietinum L.) in Algeria

Pathogenic determination of sixteen Ascochyta rabies isolates obtained from seven different provinces of western north of Algeria was the aim of this study. The pathotypes and physiological races were determined using seven differential chickpea lines (ILC1929, F8, ICC1903, ILC247, ILC482, ILC3279 and ICC3996). All isolates were classified into three pathotypes and six physiological races according to their aggressiveness and virulence, respectively. We found only one isolate (6.25%) from pathotype I (the least aggressive), 12 isolates (75%) from pathotype II (moderate aggressive) and three isolates (18.75%) from pathotype III (highly aggressive). Four races of A. rabiei were determined in this region (races 1, 4, 5 and 6). Races 1 and 2 were established in pathotype I, race 4 was represented by the pathotype II, and pathotype III included the two races 5 and 6, which were virulent isolates.
Freeze and Chaos: NTK views on DNN Normalization, Checkerboard and Boundary Artifacts

We analyze architectural features of Deep Neural Networks (DNNs) using the so-called Neural Tangent Kernel (NTK), which describes the training and generalization of DNNs in the inﬁnite-width setting. In this setting, we show that for fully-connected DNNs, as the depth grows, two regimes appear: freeze (or order ), where the (scaled) NTK converges to a constant, and chaos , where it converges to a Kronecker delta. Extreme freeze slows down training while extreme chaos hinders generalization. Using the scaled ReLU as a nonlinearity, we end up in the frozen regime. In contrast, Layer Normalization brings the network into the chaotic regime. We observe a similar effect for Batch Normalization (BN) applied after the last nonlinearity. We uncover the same freeze and chaos modes in Deep Deconvolutional Networks (DC-NNs). Our analysis explains the appearance of so-called checkerboard patterns and border artifacts. Moving the network into the chaotic regime prevents checkerboard patterns; we propose a graph-based parametrization which eliminates border artifacts; ﬁnally, we introduce a new layer-dependent learning rate to improve the convergence of DC-NNs. We illustrate our ﬁndings on DCGANs: the frozen regime leads to a collapse of the generator to a checkerboard mode, which can be avoided by tuning the nonlinearity to reach the chaotic regime. As a result, we are able to obtain good quality samples for DCGANs without BN.