Miracle-dz/UniFAR-FTUs huggingface dataset:
A Spatiotemporal Statistical Shape Model of the Brain Surface during Human Embryonic Development

In this paper, we propose an algorithm to construct a spatiotemporal statistical shape model (SSM) of the brain surface of a human embryo. Our model is based on level sets and the model is constructed independently for each Carnegie stage (CS), in which feature space is constructed by spatially-weighted principal component analysis (PCA). The statistics of the shape variation were calculated under the assumption of q -Gaussian distribution to reduce the risk of overfitting for datasets of small sizes. To define the statistics of the shape distribution of the intermediate CS, we consider 18 interpolation methods, which are the combinations of three average interpolations (linear, B-spline and information geometry) and six covariance interpolation (rotation, affine-invariant, log-Euclidean, information geometry, Wasserstein geometry and tensor B-spline) techniques, which are exhaustively compared in the experiments. We also propose a method to allow interpolation between distributions with a mixed number of dimensions, i.e., the rank of the covariance matrix. The SSM was constructed and evaluated using 60 sets of brain labels acquired from human embryos from the Kyo-to collection. We found that the best interpolation method was a combination of the linear average interpolation method with an information geometry–based covariance interpolation method. We also found that use of the q -Gaussian distribution and selection of the number of dimensions are effective methods for improving the performance of the spatiotemporal SSM of the human embryo.
Wildfish++: A Comprehensive Fish Benchmark for Multimedia Research

In this paper, we develop a large-scale vision-language fish benchmark, namely WildFish++, for comprehensive studies in multimedia research. Concretely, WildFish++ consists of 2,348 fish categories with 103,034 images in the wild, and 3,817 fish descriptions with 213,858 words. Based on these distinct characteristics, we mainly introduce four challenging research tasks on WildFish++. (1) Fine-Grained Recognition with Comparison Texts. WildFish++ naturally contains subtle difference among fish categories, which leads to fine-grained classification. Most approaches resort to tackle this problem by capturing discriminative regions in the view of each image. However, this paradigm may be still far way from extracting the most distinct features when the context on visual difference is not available. In this case, we propose to introduce comparison fish descriptions, a unique corpus that can directly point out subtle difference between highly-confused species and naturally serve as a kind of valuable context information. With such texts, we creatively elaborate a multi-modal fish network, aiming at incorporating those comparison textual information as prior knowledge and consequently leveraging it to guide CNNs to find subtle yet distinct regions in the context of comparison texts. (2) Open-Set Classification. We often confront with unknown categories in practice, e.g., there may still exist unknown fishes in our planet. Hence, we creatively adapt WildFish++ for a novel open-set classification task, which aims at correctly assigning each test image into the unknown class or one of known classes. More importantly, we investigate a number of practical designs to boost accuracy of deep learning models in open-set scenarios. (3) Cross-Modal Retrieval. WildFish++ not only contains diversified fish images in the wild but also has rich fish descriptions about morphology diagnosis, biology information, etc. Hence, we design a challenging cross-modal retrieval task, which leverages three subtasks such as text-to-text, text-to-image, image-to-text retrieval in a unified end-to-end framework. (4) Automatic Fish Classification. Automatic fish classification is a long-term research in marine biology, while current studies are unsatisfactory due to the lack of large-scale data. In this case, we train a number of CNNs with WildFish++, and use its pre-trained models to boost fish classification on most existing benchmarks of wild fishes. We will release WildFish++ with codes/protocols (https://github.com/PeiqinZhuang/WildFish++). We believe it can promote relevant studies in multimedia and beyond.
Colonization, Early Settlers and Development: the Case of Latin America

In this paper, we document the long-run impact of the geographical heterogeneity in skills among the first settlers to Latin America. To this end, we compile administrative data on the early settlers in the Americas between 1492 and 1540 including, among others, name, city of origin, destination, and occupation. From a methodological perspective, a focus on the initial period of colonization in Latin America offers several advantages. First, differences in the geographical distribution of occupations among the first settlers are likely to be accidental. Second, a set-up that analyzes an area with a single colonizer (Spain) allows to hold constant formal institutions and legal origin. Our results show a relevant effect of the skills of first colonizers on long-run levels of development of the areas located around the original settlements. We find evidence of persistence in the form of market orientation and entrepreneurial spirit.
Why the long face? Static allometry in the sexually dimorphic phenotypes of Neotropical electric fishes

The evolution of sexually dimorphic traits is thought to have marked effects on underlying patterns of static allometry. These traits can negatively affect organismal survivability by creating trade-offs between trait size and performance. Here we use three-dimensional geometric morphometrics to study the static allometry of two species of sexually dimorphic electric fishes (Apteronotus rostratus and Compsaraia samueli) in which mature males grow elongate jaws used in agonistic male–male interactions. We also estimate jaw-closing performance between the sexes of both species to track changes in kinematic transmission associated with the development of sexual weaponry. We find significantly different patterns of static allometry between the sexes of both species, with males exhibiting more positive allometric slopes relative to females. We also find a negative relationship between skull shape and mandibular kinematic transmission in C. samueli, suggesting a trade-off where males with longer faces exhibit lower mechanical advantages, suggesting weaker jaw leverage. In contrast, males and females of A. rostratus exhibit no difference between sexes in mechanical advantage associated with facial elongation.
Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

The aim of this article is to develop a methodology for measuring the degree of unpredictability in dynamical systems with memory, i.e., systems with responses dependent on a history of past states. The proposed model is generic, and can be employed in a variety of settings, although its applicability here is examined in the particular context of an industrial environment: gas turbine engines. The given approach consists in approximating the probability distribution of the outputs of a system with a deep recurrent neural network; such networks are capable of exploiting the memory in the system for enhanced forecasting capability. Once the probability distribution is retrieved, the entropy or missing information about the underlying process is computed, which is interpreted as the uncertainty with respect to the system's behavior. Hence, the model identifies how far the system dynamics are from its typical response, in order to evaluate the system reliability and to predict system faults and/or normal accidents. The validity of the model is verified with sensor data recorded from commissioning gas turbines, belonging to normal and faulty conditions.
THE EFFECT OF SPATIAL CONCENTRATION ON THE BUSINESS PERFORMANCE IN VARIOUS TYPES OF RUSSIAN CITIES

: This paper empirically analyzes the effect of spatial concentration of economic activities on enterprise productivity, using Russian firm-level data. Panel data allows us to control for endogeneity biases associated with estimation of agglomeration economies, using fixed effects method. Our results show that Russian firms benefit from the share of similar enterprises in the total city revenue and urbanization, also that these advantages differ by city type. We also find a lack of connection between the level of wages and the revenues of firms for cities within agglomerations (while for other types of cities this effect is significant and positive). We assume that this is primarily due to the role of the agglomeration center, which determines the level of wages in all cities of the agglomeration. The results show that for the optimal development of territories it is necessary to pursue a diversified regional policy.
Scaling as a key conceptual tool for the interpretation of empirical and experimental patterns in ecology and biology

This thesis is concerned with regular patterns found in ecology and biology, their linkages and the statistical description of their fluctuations around average trends. Among such patterns, often conforming to power laws, well-known examples include the Species-Area relationship (SAR), quantifying the increase of the number of species S inhabiting an ecosystem with ecosystem area, and the scale-invariant body size spectrum, routinely observed, e.g., in aquatic ecosystems. In biology, Kleiber’s law is an allometric relationship describing how metabolic rates scale with an organism’s body size. While ecological laws have often been studied independently, simple heuristic reasonings show that they are linked. The need for a unifying effort in ecology, coherently synthesizing the vast and diverse set of empirical observations across scales, has been often voiced. However, a theoretical framework answering this need was still lacking. Furthermore, ecological variables are the result of the interplay between several stochastic ecological processes, and are therefore stochastic variables fluctuating around average values. Ecological and biological scaling laws typically make predictions for such averages, but the issue of fluctuations received scarce attention in the literature. Similarly, biological fluctuations have been typically neglected in the study of the size-scaling of metabolic rates, even though body sizes and metabolic rates may have a significant variability within a species. Fluctuations may be relevant to interpret empirical observations, judge the reliability of predictions and understand ecosystem dynamics. An hypothesis for the distribution of abundances and body sizes of species inhabiting an ecosystem of finite area is proposed here. The hypothesis is inspired by finite-size scaling and is used to derive macroecological patterns and their linkages within a coherent theoretical framework. Stochastic models of community dynamics are used to support the hypothesis, and the derived linkages are tested on empirical datasets. Several stochastic models of community dynamics are also used here to study the fluctuations of S and how they scale with the average S. The intra-specific variability of metabolic rates and body sizes is investigated experimentally using freshwater phytoplankton species by nanoscale secondary ion mass spectrometry (NanoSIMS). The linkages among ecological scaling laws predicted by the theoretical framework are verified in several empirical datasets. Theoretical generalizations including deviations from pure power-law behavior and heavy-tailed intra-specific size distributions are also addressed. The theoretical study of the relative scaling of the fluctuations of S with the mean 〈S〉 in various community dynamics models shows that different ecological processes predict radically
Overview of the application of computer vision technology in fish farming

The issues that are currently identified in Russia during the implementation of Digital Agriculture project are considered. The main issues that need to be addressed in development of modern digital technologies in the fish farming industry using natural and artificial reservoirs are highlighted. Aqua engineering trends and scientific works of a number of teams that conduct research and use the capabilities of deep machine learning, are analyzed. Particular attention was paid to specific tasks and research results that solve applied problems in the field of aquaculture and fish farming. Conclusions are made about the prospects for implementing these objectives in Russia. The conclusions of scientific teams and new tasks set as a result of scientific experiments are considered. The main directions in the area of commercial fish farming that need active adaptation of computer vision to deal with applied problems, are identified. Questions of efficiency in introduction of neural networks of deep learning are raised, and also conclusions are drawn on introduction of the term “selectivity” to determine the relation of a data set received by a digital method, referred to quantity of the same data which would be received at their collection by means of non-digital technologies.
What Makes a Weed a Weed? How Virus-mediated Reverse Genetics Can Help to Explore the Genetics of Weediness

Reverse genetics investigates what a gene does by testing how the plant responds when the specific gene is changed. These techniques have been in use for decades to assess whether a given gene underpins interesting phenotypes and gain insight into the function of gene networks and families. Weed science has only recently entered the “genomic era” in which genomic and reverse genetics approaches are used to address hypotheses. This review focuses on two reverse genetic techniques used on a variety of plants including agricultural weeds, virus-induced gene silencing (VIGS) and virus-mediated overexpression (VOX), explaining the biology behind them and highlighting how these tools may be used for gene function validation in weed species for which no other transgenic approaches have been developed.
Dengue Fever Complicated with Thrombotic Thrombocytopenic Purpura: A Case Report and Literature Review

Thrombotic thrombocytopenic purpura (TTP) is a hematological disorder with an incidence of around 6.5 cases per million every year. It is characterized by the clot formation in blood vessels and resulting thrombocytopenia due to widespread utilization in the process. Other clinical features include hemolytic anemia, neurological abnormalities, fever, and renal failure. The cause can be either congenital or acquired secondary to infections, the use of certain drugs, and autoimmune disorders. On the other hand, Dengue fever is a viral infection that can result in multiple systemic complications including disrupted coagulation, renal dysfunction, and inflammation in the liver, brain, and myocardial pathology. In this case report, we present a rare presentation of thrombotic thrombocytopenic purpura in a patient with dengue fever. The mortality rate, in untreated cases, has been reported by up to 90%. With timely diagnosis and management with plasmapheresis, this can be reduced to 10-20%. Currently, it is important to control such an unknown outbreak of Dengue fever, as it is a serious health care issue of disease control and prevention in Pakistan.
Computationally Reconstructing Cotranscriptional RNA Folding Pathways from Experimental Data Reveals Rearrangement of Non-Native Folding Intermediates

The series of RNA folding events that occur during transcription, or a cotranscriptional folding pathway, can critically influence the functional roles of RNA in the cell. Here we present a method, Reconstructing RNA Dynamics from Data (R2D2), to uncover details of cotranscriptional folding pathways by predicting RNA secondary and tertiary structures from cotranscriptional SHAPE-Seq data. We applied R2D2 to the folding of the Escherichia coli Signal Recognition Particle (SRP) RNA sequence and show that this sequence undergoes folding through non-native intermediate structures that require significant structural rearrangement before reaching the functional native structure. Secondary structure folding pathway predictions and all-atom molecular dynamics simulations of folding intermediates suggest that this rearrangement can proceed through a toehold mediated strand displacement mechanism, which can be disrupted and rescued with point mutations. These results demonstrate that even RNAs with simple functional folds can undergo complex folding processes during synthesis, and that small variations in their sequence can drastically affect their cotranscriptional folding pathways. Highlights - Computational methods predict RNA structures from cotranscriptional SHAPE-Seq data - The E. coli SRP RNA folds into non-native structural intermediates cotranscriptionally - These structures rearrange dynamically to form an extended functional fold - Point mutations can disrupt and rescue cotranscriptional RNA folding pathways
Event-Based Finite-Time Consensus Control of Second-Order Delayed Multi-Agent Systems

This brief considers the event-based finite-time consensus problems for second-order multi-agent systems (MASs) with input delay. By backstepping design method, the controllers are developed to ensure true velocity track virtual velocity within a fixed time. Once the true velocity tracks on the virtual velocity, all agents can reach the agreement within a finite time. Moreover, an improved event-based control scheme is proposed by discontinuous communication. Besides, no Zeno behavior occurs. The efficiency of the proposed algorithms is illustrated by a detailed numerical example.
Optimizing the mmWave Channel Estimation Duration by Rate Prediction

In millimeter-wave (mmWave) wireless communications, the duration of the channel estimation plays a major role to establish the links before data transmission. However, fixed or long channel estimation can substantially hinder the achievable transmit data rates. In this letter, we propose a new scheme that optimizes the channel estimation duration to establish the link between the base station (BS) and a mobile station (MS) in mmWave communications. Before pilot transmissions, the BS predicts the downlink effective rate that would be achieved after channel estimation with the pilot precoder and compares this predicted rate with the current rate, based on the current channel estimates. The proposed scheme optimizes the mmWave channel estimation duration by ending pilot transmissions when the predicted rate is lower than the current rate.
Isoenzyme activity in monitoring deterioration of Balfourodendron riedelianum seeds

In this study, we sought to identify efficient enzymes for use in monitoring the deterioration and loss of germination of Balfourodendron riedelianum (Engler) Engler seeds. The research was conducted at the Laboratorio de Tecnologia de Sementes e Mudas of the Universidade Estadual do Oeste do Parana, Campus Marechal Cândido Rondon, Parana State, Brazil. Seeds were harvested at three collection sites and later submitted for processing, drying, initial characterization and storage in airtight packaging under controlled and uncontrolled storage conditions and uncontrolled. Tests were conducted at 0, 120, 240, and 360 days using a germination test, germination speed index, accelerated ageing test and the activity of peroxidase, phenylalanine ammonia-lyase and β-1,3-glucanase. The experiment was a completely randomized design. The results were subjected to regression analysis at 5% probability. Ageing was different among sample sites and between different storage conditions. The decrease in viability of seeds of Balfourodendron riedelianum (Engler) Engler was detected based on the activity of peroxidase enzymes in seeds from the Diamante do Oeste and Missal sites and the enzyme β-1,3-glucanase in seeds from the Missal site after storage periods of 0, 120, and 240 days.
Lithostratigraphic Characterizationof the Subsurface in Ologbo Community using Wenner-Schlumberger Electrode Configuration of Electrical Resistivity Method

Wenner-Schlumberger electrode configuration of the electrical resistivity method of geophysical investigation was carried out in Ologbo Community Edo State Nigeria in order to characterize the subsurface geology in this location. The Petrozenith earth resistivity meter was used to measure the resistance offered by the subsurface in two different locations in this geophysical investigation. Sixty (60) Wenner-Schlumberger soundings were carried out in each of the areas. Data were collected and processed using Res2dinv software which produced an inverse model resistivity section which is the true resistivity of the subsurface and this was used to deduce the geological characteristics of the surveyed depth range. Interpretation of the inverse model resistivity section using knowledge of the geology of the study area and standard electrical resistivity of earth materials available in the literature indicates the presence of clayey sand, lateritic clay, laterite, sand and sandstone as the make-up of the subsurface geology of the study area. This study was carried out using Wenner – Schlumberger configuration of electrical resistivity method to characterize the lithostratigraphy of the subsurface in the study location. Res2dinv software was used to process the data from each of the surveyed profile. Geoelectric imaging consisting of three figures were obtained and are interpreted to generate the subsurface geologic characteristics. The analysis and interpretation of the results shows the presence of clayey sand, lateritic clay, laterite, sand and sandstone as the make-up of the subsurface of the study area. The litghostratigrafic units are predominantly sandy and lateritic.
A Stochastic Optimization Framework for Channel Bonding in Wireless LANs Under Demand Uncertainty

Channel bonding is one promising approach to cope with rising WLAN data demand, given scarce spectrum resources. An access point (AP) can aggregate multiple contiguous channels to satisfy demand. We discuss how to optimally utilize available frequency bands under uncertainty in AP demands using two stochastic optimization frameworks: a static scheme which minimizes the total occupied bandwidth while satisfying the demand of each AP with probability at least $\beta $ , and an adaptive scheme that allows adaptability of the bandwidth allocation in response to the AP demand variations. Given its complexity, we propose a novel framework to solve the adaptive stochastic optimization problem efficiently. The proposed framework exploits the special structure of the problem through decomposition into two subproblems. A particle swarm optimization (PSO)-based algorithm is tailored to the first-stage problem in order to obtain good solutions. The second-stage problem is further decomposed into several subproblems that can be solved independently in parallel. Our numerical results (i) demonstrate the advantages of stochastic compared to deterministic allocation, (ii) illustrate that the proposed framework reaches the optimal solution for the two-stage problem in few iterations, and (iii) explain the bandwidth–user satisfaction trade-off provided by the adaptive allocation approach.