Miracle-dz/UniFAR-FTUs huggingface dataset:
NEURAL NETWORKS BASED ADAPTIVE SMALL CELL BASE STATION TRANSMIT POWER CONTROL FOR INTERFERENCE MITIGATION IN HETEROGENEOUS NETWORKS

Heterogeneous networking of small cells such as pico/femtocells over the existing macrocell network is believed to augment the data rate requirement in forthcoming years. Closed access operation of femtocell base station (FBS) and shared spectrum assignment in the two-tier macro-femtocell network leads to unacceptable deterioration in achieved data rate of femtocell users. In this work, application of computationally efficient neural network to perform adaptive FBS transmit power control is proposed for mitigation of interference in two-tier heterogeneous network formed by macro-femtocells and to improve the Quality of Service perceived by femtocell users. A Neuro-controller is designed to regulate the FBS transmission power based on the channel quality indicator measurement report sent by user equipment. Since the proposed power control strategy employs the channel side information already available in the existing network, there would not be any signaling overhead to mitigate the co-tier interference. Simulation results validate the effectiveness of the proposed power control strategy which provides significant improvement in achieved data rate of femtocell users and prevents them from outage.
Restoration Techniques for Macrocystis pyrifera (Phaeophyceae) Populations at the Southern Limit of Their Distribution in México

Abstract Following the 1982–83 El Niño, Macrocystis pyrifera (L.) C. Agardh, forests disappeared throughout their range in Baja California. The giant kelp forests subsequently recovered within this range except at their extreme southern limit, a region encompassing 50 km of coastline with a former giant kelp standing stock of 28,000 wet tons. Two techniques were tested to restore these forests: juvenile transplantation and seeding with sporophylls. For transplanting, juvenile M. pyrifera sporophytes were attached to Eisenia arborea stumps seasonally over a two-year period. Average survival of transplants ranged from 7% in spring to 41% in winter. After two years, the average number of basal fronds per plant increased from 2 to 64 per plant and surface fronds from 0 to 34 per plant. Average frond growth rate of the transplants ranged from 8.1 cm day−1 in summer to 10.8 cm day−1 in winter. No significant differences in growth rate were found among treatments (seasons) for the transplants, but control plants showed a seasonal variation, with higher frond growth rates in winter (13.3 cm day−1) and spring (9.3 cm day−1) and lower in summer (4.4 cm day−1). The seeding technique was tested in a fully orthogonal-block design with three factors with two levels (factors: ± sporophylls addition, ± Eisenia arborea and ± understory algae). Macrocystis pyrifera recruitment occurred only in treatments with added sporophylls. The highest recruitment occurred where all algae were removed from the bottom, followed by the treatments without understory algae but with Eisenia arborea. This results suggest that a lack of spores and the presence of understory algae were the main factors inhibiting Macrocystis pyrifera recruitment in the area. Lower sea water temperatures and high nutrient concentrations occurred in spring and high temperatures and low nutrients in summer suggesting, as in southern California, an inverse relationship between these two factors. The results suggest a combined approach of transplanting juveniles and seeding during spring would be most effective for restoring the M. pyrifera forests.
AR in VR: Simulating augmented reality glass for image fusion

Developing an augmented reality (AR) system involves a multitude of interconnected algorithms such as image fusion, camera synchronization and calibration, and brightness control, each having diverse parameters. This abundance of features, while beneficial in nature for its applicability to different tasks, is detrimental to developers as they try to navigate different combinations and pick the most suitable configuration for their application. Additionally, the temporally inconsistent nature of the real world hinders the development of reproducible and reliable testing methods for AR systems. To help address these issues, we develop and test a virtual reality (VR) environment [1] that allows the simulation of variable AR configurations for image fusion. In this work, we improve our system with a more realistic AR glass model adhering to physical light and glass properties. Our implementation combines the incoming real-world background light and the AR projector light at the level of the AR glass.
Cepstral Coefficient Feature Extraction for Active Sonar Classification

The effectiveness of acoustic classification is highly dependent on the features that can be extracted from a given signal. It is possible to employ a range of features simultaneously during classification to improve accuracy and redundancy, though certain features can degrade performance when used together. Mel Frequency Cepstral Coefficient (MFCC) and Linear Predictive Cepstral Coefficient (LPCC) algorithms provide a method for acoustic feature extraction through the processing of the short term power spectrum of a signal. Features generated using these coefficients may enhance classification performance in active sonar applications due to their robustness against background noise in low frequency bandwidths (20-2000Hz). This paper discusses the integration of these features into the Binary Classification Research Tool (BCRT); a research tool for the testing of feature and classifier performance. The analysis examines their compatibility with established feature sets as well as their overall potential in the area of sonar classification. Through testing on a range of underwater signals, MFCC features were found to have strong isolated performance and to increase classification accuracy when combined with established feature sets. LPCC features had a poor performance in isolation but achieved the highest classification accuracy when combined with other feature sets.
Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm

Facial expression is a standout amongst the most imperative features of human emotion recognition. For demonstrating the emotional states facial expressions are utilized by the people. In any case, recognition of facial expressions has persisted a testing and intriguing issue with regards to PC vision. Recognizing the Micro-Facial expression in video sequence is the main objective of the proposed approach. For efficient recognition, the proposed method utilizes the optimal convolution neural network. Here the proposed method considering the input dataset is the CK+ dataset. At first, by means of Adaptive median filtering preprocessing is performed in the input image. From the preprocessed output, the extracted features are Geometric features, Histogram of Oriented Gradients features and Local binary pattern features. The novelty of the proposed method is, with the help of Modified Lion Optimization (MLO) algorithm, the optimal features are selected from the extracted features. In a shorter computational time it has the benefits of rapidly focalizing and effectively acknowledging with the aim of getting an overall arrangement or idea. Finally the recognition is done by Convolution Neural network (CNN). Then the performance of the proposed MFEOCNN method is analyzed in terms of false measures and recognition accuracy. This kind of emotion recognition is mainly used in medicine, marketing, E-learning, entertainment, law and monitoring. From the simulation, we know that the proposed approach achieves maximum recognition accuracy of 99.2% with minimum Mean Absolute Error (MAE) value. This results are compared with the existing for MicroFacial Expression Based Deep-Rooted Learning (MFEDRL), Convolutional Neural Network with Lion Optimization (CNN+LO) and Convolutional Neural Network (CNN) without optimization. The simulation of the proposed method is done in the working platform of MATLAB.
OP-NUTR150035 1..14

Very preterm infants (<32 weeks’ gestation) are at high risk for impaired skeletal development because of factors that limit the provision of extrauterine nutrients. Cumulative net deficiencies of calcium, phosphorus, docosahexaenoic acid (DHA), and arachidonic acid (ARA) are evident in these infants after prolonged administration of total parenteral nutrition (TPN). This is significant because minerals as well as metabolites of DHA and ARA are important modulators of bone cell differentiation, lengthening of bone, and bone matrix deposition. Furthermore, diets containing only precursors of DHA and ARA result in suboptimal skeletal growth. With the emergence of new intravenous lipid emulsions, it is important to understand the impact of fatty acids on bone metabolism in the third trimester in order to optimize the provision of TPN in very preterm infants. The purpose of this review is to evaluate current evidence regarding intravenous lipid emulsions and bone metabolism in very preterm infants receiving prolonged TPN and to identify areas of research needed.
Qualitative behavior of a SIRS epidemic model with vaccination on heterogeneous networks

This paper studies the dynamics of a SIRS epidemic model with varying population size and vaccination in a complex network. Using an analytical method, we mainly investigate the stability of the model according to the threshold R0. That is, if R0 is less than one, then the disease will die out. Alternatively, the system admits a unique endemic equilibrium which is globally asymptotically stable ifR0 > 1. Moreover, we investigate the case when R0 = 1. Finally, some numerical simulations are provided to illustrate the effectiveness of the theoretical results.
ON A STOCHASTIC INTEREST RATE MODEL FOR BONDS

Assuming a certain stochastic evolution of the interest rate Bt we consider the problem of how to prize a security whose payoff at maturity time T is a function which depends only on BT . We show how to generalize the theory of Cox et al [1] to a situation with time dependent coefficients and arbitrary payoff. The solution to this problem is given in terms of “killed” Brownian motion. AMS Subject Classification: 60H10, 60J60
Verbal Focus-of-Attention System for Learning-from-Demonstration

The Learning-from-Demonstration (LfD) framework aims to map human demonstrations to a robot to reduce programming effort. To this end, an LfD system encodes a human demonstration into a series of execution units for a robot, referred to as task models. Although previous research has proposed successful task-model encoders that analyze images and human body movements, the encoders have been designed in environments without noise. Therefore, there has been little discussion on how to guide a task-model encoder in a scene with spatio-temporal noises such as cluttered objects or unrelated human body movements. In human-to-human demonstrations, verbal instructions play a role in guiding an observer's visual attention. Inspired by the function of verbal instructions, we propose a verbal focus-of-attention (FoA) system (i.e., spatio-temporal filters) to guide a task-model encoder. For object manipulation, the encoder first recognizes a target-object name and its attributes from verbal instructions. The information serves as a where-to-look FoA filter to confine the areas where the target object existed in the demonstration. The encoder next detects the timings of grasp and release tasks that occur in the filtered area. The timings serve as a when-to-look FoA filter to confine the period when the demonstrator manipulated the object. Finally, the task-model encoder recognizes task models by employing the FoA filters. The contributions of this paper are: (1) to propose verbal FoA for LfD; (2) to design an algorithm to calculate FoA filters from verbal input; (3) to demonstrate the effectiveness of a verbal-driven FoA by testing an implemented LfD system in noisy environments.
GENETICS OF DYSTONIA

Recent discoveries in dystonia genetics have led to greater ability to provide genetic testing, as well promise for better, more focused therapeutic interventions. However, identified genes, such as DYT1, account for only a fraction of dystonia and mostly for early-onset forms. This chapter focuses on the phenotype-genotype relationships as phenotype drives gene discovery, and despite phenotypic heterogeneity, the phenotype is paramount in choice of genetic testing and sometimes treatment. Decisions about testing for DYT1 are made based primarily on age of onset of dystonia and family history. For dopa-responsive dystonia, the diagnosis may be made by the empiric response to levodopa rather than genetic testing, and diagnostic considerations for dopa-responsive dystonia are addressed. The tremendous strides that have recently been made in understanding the Neurogenetics of primary dystonia and dystonia-plus syndromes are also discussed. These include an increased understanding of factors that affect penetrance of genetic forms of dystonia, which are mostly autosomal dominant but are incompletely penetrant. The recent identification of a genetic modifier for DYT1 dystonia partially accounts for the decreased penetrance observed in DYT1, and maternal imprinting of the SGCE gene in myoclonus-dystonia explains decreased penetrance in this disorder. For other genetic forms of dystonia, such as dopa-responsive dystonia, the decreased penetrance is still not well understood.
Adjusted Expected Shortfall

We introduce and study the main properties of a class of convex risk measures that refine Expected Shortfall by simultaneously controlling the expected losses associated with different portions of the tail distribution. The corresponding adjusted Expected Shortfalls quantify risk as the minimum amount of capital that has to be raised and injected into a financial position $X$ to ensure that Expected Shortfall $ES_p(X)$ does not exceed a pre-specified threshold $g(p)$ for every probability level $p\in[0,1]$. Through the choice of the benchmark risk profile $g$ one can tailor the risk assessment to the specific application of interest. We devote special attention to the study of risk profiles defined by the Expected Shortfall of a benchmark random loss, in which case our risk measures are intimately linked to second-order stochastic dominance.
A Technical Report for VIPriors Image Classification Challenge

Image classification has always been a hot and challenging task. This paper is a brief report to our submission to the VIPriors Image Classification Challenge. In this challenge, the difficulty is how to train the model from scratch without any pretrained weight. In our method, several strong backbones and multiple loss functions are used to learn more representative features. To improve the models' generalization and robustness, efficient image augmentation strategies are utilized, like autoaugment and cutmix. Finally, ensemble learning is used to increase the performance of the models. The final Top-1 accuracy of our team DeepBlueAI is 0.7015, ranking second in the leaderboard.
A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum

Neighbor embeddings are a family of methods for visualizing complex high-dimensional datasets using kNN graphs. To find the low-dimensional embedding, these algorithms combine an attractive force between neighboring pairs of points with a repulsive force between all points. One of the most popular examples of such algorithms is t-SNE. Here we show that changing the balance between the attractive and the repulsive forces in t-SNE yields a spectrum of embeddings, which is characterized by a simple trade-off: stronger attraction can better represent continuous manifold structures, while stronger repulsion can better represent discrete cluster structures. We show that UMAP embeddings correspond to t-SNE with increased attraction; this happens because the negative sampling optimisation strategy employed by UMAP strongly lowers the effective repulsion. Likewise, ForceAtlas2, commonly used for visualizing developmental single-cell transcriptomic data, yields embeddings corresponding to t-SNE with the attraction increased even more. At the extreme of this spectrum lies Laplacian Eigenmaps, corresponding to zero repulsion. Our results demonstrate that many prominent neighbor embedding algorithms can be placed onto this attraction-repulsion spectrum, and highlight the inherent trade-offs between them.
A direct reconstruction algorithm for the anisotropic inverse conductivity problem based on Calderón’s method in the plane

A direct reconstruction algorithm based on Calderón’s linearization method for the reconstruction of isotropic conductivities is proposed for anisotropic conductivities in two-dimensions. To overcome the non-uniqueness of the anisotropic inverse conductivity problem, the entries of the unperturbed anisotropic tensors are assumed known a priori, and it remains to reconstruct the multiplicative scalar field. The quasi-conformal map in the plane facilitates the Calderón-based approach for anisotropic conductivities. The method is demonstrated on discontinuous radially symmetric conductivities of high and low contrast.
Fish Species Diversity in Lakes

Stepwise multiple regression was used to obtain preliminary insights into the environmental parameters which influence the number of fish species occurring in lakes. Results are summarized as follows: 1. For a sample of 70 lakes and inland seas from throughout the world, surface area and latitude account for about one-third of the variability in fish species diversity. 2. For a subsample of 14 North American lakes, latitude and surface area account for about 90% of the variability in species numbers. The large effect of latitude can be explained in terms of climatic severity and isolation from sources of colonization. 3. For a subsample of 14 African lakes, surface area, depth, and conductivity were primary variables affecting species diversity. 4. For 70 lakes of the world and 14 North American lakes, the slope of the species-area curve was low (z = 0.15-0.16). Apparently this reflects the fact that, whereas a relatively large number of species are available to colonize small lakes, larger ones are impoverished. Most of the latter are recent enough that there has not been time for endemic speciation to reach an equilibrium with extinction. We expect the large lakes of tropical Africa to be the closest to such an equilibrium, and it is encouraging that the slope of the species-area curve for the sample including these bodies of water is quite steep (z = 0.35). 5. On the basis of our data and analyses of species distribution in other insular habitats, four patterns of species diversity are distinguished.
Massive gravitons on the Landscape and the AdS Distance Conjecture

This work regards IIB string theory embeddings of massive and bi-metric $AdS_{4}$ gravity where the spectrum of the theory includes multiple massive gravitons. The corresponding geometry consists of multiple $AdS_{4}\times_{w}\mathcal{M}_{6}$ spacetimes coupled by Janus throats of different radii. In the second part of this work, the AdS distance conjecture is studied in the above context and it is shown that the scale of the predicted infinite tower of light states is related to the breakdown scale of the effective field theory.
Influence of sensor tilts on bio-inspired polarized skylight orientation determination

Many animals such as desert ants, honey bees and locusts have been reported to detect polarized light in the sky as a compass. Inspired by this, several polarized skylight orientation determination approaches have been proposed. However, almost all of these approaches always require polarization sensor pointing to the zenith of the sky dome. So, the influence of sensor tilts (not point to the sky zenith) on bio-inspired polarization orientation determination needs to be analyzed urgently. Aiming at this problem, a polarization compass simulation system is designed based upon solar position model, Rayleigh sky model, and hypothetical polarization imager. Then, the error characteristics of four typical orientation determination approaches are investigated in detail under only pitch tilt condition, only roll tilt condition, pitch and roll tilts condition respectively. Finally, simulation and field experiments all show that the orientation errors of four typical approaches are highly consistent when they are subjected to tilt interference, in addition, the errors are affected by not only the degree of inclination, but also the solar altitude angle and the relative position between the Sun and polarization sensor.
Asymptotics of lowest unitary SL(2,C) invariants on graphs

We describe a technique to study the asymptotics of SL(2,C) invariant tensors associated to graphs, with unitary irreps and lowest SU(2) spins, and apply it to the Lorentzian EPRL-KKL (Engle, Pereira, Rovelli, Livine; Kaminski, Kieselowski, Lewandowski) model of quantum gravity. We reproduce the known asymptotics of the 4-simplex graph with a different perspective on the geometric variables and introduce an algorithm valid for any graph. On general grounds, we find that critical configurations are not just Regge geometries, but a larger set corresponding to conformal twisted geometries. These can be either Euclidean or Lorentzian, and include curved and flat 4d polytopes as subsets. For modular graphs, we show that multiple pairs of critical points exist, and there exist critical configurations of mixed signature, Euclidean and Lorentzian in different subgraphs, with no 4d embedding possible.
Similarity quantification for linear stochastic systems as a set-theoretic control problem

For the formal verification and design of control systems, abstractions with quantified accuracy are crucial. Such similarity quantification is hindered by the challenging computation of approximate stochastic simulation relations. This is especially the case when considering accurate deviation bounds between a stochastic continuous-state model and its finite-state abstraction. In this work, we give a comprehensive computational approach and analysis for linear stochastic systems. More precisely, we develop a computational method that characterizes the set of possible simulation relations and optimally trades off the error contributions on the system's output with deviations in the transition probability. To this end, we establish an optimal coupling between the models and simultaneously solve the approximate simulation relation problem as a set-theoretic control problem using the concept of invariant sets. We show the variation of the guaranteed satisfaction probability as a function of the error trade-off in a case study where a formal specification is given as a temporal logic formula.
Privacy Engineering Meets Software Engineering. On the Challenges of Engineering Privacy ByDesign

Current day software development relies heavily on the use of service architectures and on agile iterative development methods to design, implement, and deploy systems. These practices result in systems made up of multiple services that introduce new data flows and evolving designs that escape the control of a single designer. Academic privacy engineering literature typically abstracts away such conditions of software production in order to achieve generalizable results. Yet, through a systematic study of the literature, we show that proposed solutions inevitably make assumptions about software architectures, development methods and scope of designer control that are misaligned with current practices. These misalignments are likely to pose an obstacle to operationalizing privacy engineering solutions in the wild. Specifically, we identify important limitations in the approaches that researchers take to design and evaluate privacy enhancing technologies which ripple to proposals for privacy engineering methodologies. Based on our analysis, we delineate research and actions needed to re-align research with practice, changes that serve a precondition for the operationalization of academic privacy results in common software engineering practices.