Miracle-dz/UniFAR-FTUs huggingface dataset:
Robotic Message Ferrying for Wireless Networks Using Coarse-Grained Backpressure Control

We formulate the problem of robots ferrying messages between statically-placed source and sink pairs that they can communicate with wirelessly. We first analyze the capacity region for this problem under ideal conditions. We indicate how robots could be scheduled optimally to satisfy any arrival rate in the capacity region, given prior knowledge about arrival rate. We then consider the setting where the arrival rate is unknown and present a coarse-grained backpressure message ferrying algorithm (CBMF) for it. In CBMF, the robots are matched to sources and sinks once every epoch to maximize a queue-differential-based weight. The matching controls both motion and transmission for each robot. We show through analysis and simulations the conditions under which CBMF can stabilize the network, and its corresponding delay performance. From a practical point of view, we propose a heuristic approach to adapt the epoch duration according to network conditions that can improve the end-to-end delay while guaranteeing the network stability at the same time. We also study the structural properties with its explicit delay performance of the CBMF algorithm in a homogeneous network.
Delay-based Back-Pressure scheduling in multi-hop wireless networks

Scheduling is a critical and challenging resource allocation mechanism for multi-hop wireless networks. It is well known that scheduling schemes that give a higher priority to the link with larger queue length can achieve high throughput performance. However, this queue-length-based approach could potentially suffer from large (even infinite) packet delays due to the well-known last packet problem, whereby packets may get excessively delayed due to lack of subsequent packet arrivals. Delay-based schemes have the potential to resolve this last packet problem by scheduling the link based on the delay for the packet has encountered. However, the throughput performance of delay-based schemes has largely been an open problem except in limited cases of single-hop networks. In this paper, we investigate delaybased scheduling schemes for multi-hop traffic scenarios. We view packet delays from a different perspective, and develop a scheduling scheme based on a new delay metric. Through rigorous analysis, we show that the proposed scheme achieves the optimal throughput performance. Finally, we conduct extensive simulations to support our analytical results, and show that the delay-based scheduler successfully removes excessive packet delays, while it achieves the same throughput region as the queue-length-based scheme.
Animal behaviour Dominance is not always an honest signal of male quality , but females may be able to detect the dishonesty

Females prefer dominant males as mating partners in numerous species. Male dominance rank is considered as an honest signal of male quality, because only healthy males in good condition are thought to be able to win fights with other males. Here, we tested whether activation of the immune system influences the success of males in male–male competition and mating in the field cricket, Gryllus integer. We activated the immune system of males with a nylon monofilament (to mimic a parasitoid larva), and arranged fights between male pairs to assess male dominance and associated mating success. Activation of the immune system with nylon monofilament substantially enhanced the fighting success of males during male–male competition but had no effect on mating success. However, sham-manipulation (a wound only) did not have any effect on fighting success although females mated more often with dominant males. Our study suggests that when male crickets meet an apparent survival threat they may behave more dominantly, probably owing to terminal investment. Male success during male–male competition is not always an honest signal of males’ quality, but females may be able to detect this dishonesty.
Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication

We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.
Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes

In this paper we propose a Bayesian nonparametric model for clustering partial ranking data. We start by developing a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an inﬁnite number of choice items. Our framework is based on the theory of random atomic measures, with prior speciﬁed by a completely random measure. We characterise the posterior distribution given data, and derive a simple and ef-fective Gibbs sampler for posterior simulation. We then develop a Dirichlet process mixture extension of our model and apply it to investigate the clustering of preferences for college degree programmes amongst Irish secondary school graduates. The existence of clusters of applicants who have similar preferences for degree programmes is established and we determine that subject matter and geographical location of the third level institution characterise these clusters.
On Finite Memory Universal Data Compression and Classi cation of Individual Sequences

Consider the case where consecutive blocks of N letters of a semi-in nite individual sequence X over a nite-alphabet are being compressed into binary sequences by some one-to-one mapping. No a-priori information about X is available at the encoder, which must therefore adopt a universal data-compression algorithm. It is known that if the universal LZ77 data compression algorithm is successively applied to N -blocks then the best error-free compression, for the particular individual sequence X is achieved as N tends to in nity. The best possible compression that may be achieved by any universal data compression algorithm for nite N -blocks is discussed. It is demonstrated that context tree coding essentially achieves it. Next, consider a device called classi er (or discriminator) that observes an individual training sequence X. The classi er's task is to examine individual test sequences of length N and decide whether the test N -sequence has the same features as those that are captured by the training sequence X, or is su ciently di erent, according to some appropriate criterion. Here again, it is demonstrated that a particular universal context classi er with a storage-space complexity that is linear in N , is essentially optimal. This may contribute a theoretical \individual sequence" justi cation for the Probabilistic Su x Tree (PST) approach in learning theory and in computational biology.
Feature Extraction Spatial Clustering Classifier Preprocessing Semantic Type Detection Feature Extraction Classifier Elevation Change Semantics Detection Stations Specific Semantics Detection

We present TransitLabel, a crowd-sensing system for automatic enrichment of transit stations indoor floorplans with different semantics like ticket vending machines, entrance gates, drink vending machines, platforms, cars’ waiting lines, restrooms, lockers, waiting (sitting) areas, among others. Our key observations show that certain passengers’ activities (e.g., purchasing tickets, crossing entrance gates, etc) present identifiable signatures on one or more cell-phone sensors. TransitLabel leverages this fact to automatically and unobtrusively recognize different passengers’ activities, which in turn are mined to infer their uniquely associated stations semantics. Furthermore, the locations of the discovered semantics are automatically estimated from the inaccurate passengers’ positions when these semantics are identified. We evaluate TransitLabel through a field experiment in eight different train stations in Japan. Our results show that TransitLabel can detect the fine-grained stations semantics accurately with 7.7% false positive rate and 7.5% false negative rate on average. In addition, it can consistently detect the location of discovered semantics accurately, achieving an error within 2.5m on average for all semantics. Finally, we show that TransitLabel has a small energy footprint on cell-phones, could be generalized to other stations, and is robust to different phone placements; highlighting its promise as a ubiquitous indoor maps enriching service.
Similar Part Rearrangement With Pebble Graphs

This work proposes a method for effectively computing manipulation paths to rearrange similar objects in a cluttered space. The solution can be used to place similar products in a factory floor in a desirable arrangement or for retrieving a particular object from a shelf blocked by similarly sized objects. These are challenging problems as they involve combinatorially large, continuous configuration spaces due to the presence of multiple moving bodies and kinematically complex manipulators. This work leverages ideas from algorithmic theory, multi-robot motion planning and manipulation planning to propose appropriate graphical representations for this challenge. These representations allow to quickly reason whether manipulation paths allow the transition between entire sets of objects arrangements without having to explicitly enumerate the path for each pair of arrangements. The proposed method also allows to take advantage of precomputation given a manipulation roadmap for transferring a single object in the same cluttered space. The resulting approach is evaluated in simulation for a realistic model of a Baxter robot and executed in open-loop on the real system, showing that the approach solves complex instances and is promising in terms of scalability and success ratio.
Collective response to the media coverage of COVID-19 Pandemic on Reddit and Wikipedia

Background: The exposure and consumption of information during epidemic outbreaks may alter risk perception, trigger behavioral changes, and ultimately affect the evolution of the disease. It is thus of the uttermost importance to map information dissemination by mainstream media outlets and public response. However, our understanding of this exposure-response dynamic during COVID-19 pandemic is still limited. Objective: The goal of this work is to provide a characterization of media coverage and online collective response to COVID-19 pandemic in four countries: Italy, United Kingdom, United States, and Canada. Methods: We collect a heterogeneous dataset including 227’768 online news articles and 13’448 YouTube videos published by mainstream media, 107’898 users posts and 3’829’309 comments on the social media platform Reddit, and 278’456’892 views to COVID-19 related Wikipedia pages. Results: Our results show that public attention, quantified as users activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage and declines rapidly, while news exposure and COVID-19 incidence remain high. Furthermore, by using an unsupervised, dynamical topic modeling approach, we show that while the attention dedicated to different topics by media and online users are in good accordance, interesting deviations emerge in their temporal patterns. Conclusions: Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on collective awareness, risk perception and thus on tendencies towards behavioural changes
Relative Generalized Hamming Weights of One-Point Algebraic Geometric Codes

Security of linear ramp secret sharing schemes can be characterized by the relative generalized Hamming weights of the involved codes. In this paper, we elaborate on the implication of these parameters and devise a method to estimate their value for general one-point algebraic geometric codes. As it is demonstrated, for Hermitian codes, our bound is often tight. Furthermore, for these codes, the relative generalized Hamming weights are often much larger than the corresponding generalized Hamming weights.
LEARNING WORD EMBEDDINGS FOR LOW RESOURCE LANGUAGES: THE CASE OF BURYAT

Word-vector representations have been extensively studied for rich resource languages with large text datasets. However, only a few studies analyze semantic representations of low resource languages, when only small corpus is available. In this study we introduce a methodology and compare techniques to learn semantic representations of low resource languages. The proposed methodology consists of defining accurate preprocessing steps, applying language-independent stemmer and learning word-vector representations. In addition, we propose a simple word embeddings evaluation scheme that can be easily adapted to any language. By using this methodology we learn word-vector representations for Buryat language. In order to promote further research we make the source code and the resulting word embeddings corpus publicly available.
Stochastic blockmodels with growing number of classes

Latent variable models are frequently used to identify structure in dichotomous network data, in part because they give rise to a Bernoulli product likelihood that is both well understood and consistent with the notion of exchangeable random graphs. In this article we propose conservative confidence sets that hold with respect to these underlying Bernoulli parameters as a function of any given partition of network nodes, enabling us to assess estimates of residual network structure, that is, structure that cannot be explained by known covariates and thus cannot be easily verified by manual inspection. We demonstrate the proposed methodology by analyzing student friendship networks from the National Longitudinal Survey of Adolescent Health that include race, gender, and school year as covariates. We employ a stochastic expectation-maximization algorithm to fit a logistic regression model that includes these explanatory variables as well as a latent stochastic blockmodel component and additional node-specific effects. Although maximumlikelihood estimates do not appear consistent in this context, we are able to evaluate confidence sets as a function of different blockmodel partitions, which enables us to qualitatively assess the significance of estimated residual network structure relative to a baseline, which models covariates but lacks block structure.
Rapid mixing of Gibbs sampling on graphs that are sparse on average

Gibbs sampling also known as Glauber dynamics is a popular technique for sampling high dimensional distributions defined on graphs. Of special interest is the behavior of Gibbs sampling on the Erdos-Renyi random graph G(n,d-n), where each edge is chosen independently with probability d-n and d is fixed. While the average degree in G(n,d-n) is d(1 - o(1)), it contains many nodes of degree of order log n-log log n. The existence of nodes of almost logarithmic degrees implies that for many natural distributions defined on G(n,p) such as uniform coloring (with a constant number of colors) or the Ising model at any fixed inverse temperature β, the mixing time of Gibbs sampling is at least n1+Ω(1-log log n). Recall that the Ising model with inverse temperature β defined on a graph G = (V,E) is the distribution over l±rVgiven by $ P(\sigma) = {1 \over Z} \exp (\beta \Sigma_{(v,u)\varepsilon E}\ \sigma(v)\sigma(u)) $. High degree nodes pose a technical challenge in proving polynomial time mixing of the dynamics for many models including the Ising model and coloring. Almost all known sufficient conditions in terms of β or number of colors needed for rapid mixing of Gibbs samplers are stated in terms of the maximum degree of the underlying graph. In this work, we show that for every d 0, such that for all β 1 where the parameters of the model do not depend on n. They also provide a rare example where one can prove a polynomial time mixing of Gibbs sampler in a situation where the actual mixing time is slower than npolylog(n). Our proof exploits in novel ways the local tree like structure of Erdos-Renyi random graphs, comparison and block dynamics arguments and a recent result of Weitz. Our results extend to much more general families of graphs which are sparse in some average sense and to much more general interactions. In particular, they apply to any graph for which every vertex v of the graph has a neighborhood N(v) of radius O(log n) in which the induced sub-graph is a tree union at most O(log n) edges and where for each simple path in N(v) the sum of the vertex degrees along the path is O(log n). Moreover, our result apply also in the case of arbitrary external fields and provide the first FPRAS for sampling the Ising distribution in this case. We finally present a non Markov Chain algorithm for sampling the distribution which is effective for a wider range of parameters. In particular, for G(n, d-n) it applies for all external fields and β < βd, where d tanh(βd) = 1 is the critical point for decay of correlation for the Ising model on G(n, d-n). © 2009 Wiley Periodicals, Inc. Random Struct. Alg. , 2009
Watermarking Of Images Using Hybrid Technique

Day by day, the number of computers that are integrated onto a network has reached a drastic level, thus making it easier and faster to distribute the digital content onto the real world. One of the major hindrances is the absence of an intellectual protection system which will reduce or eliminate the copying and distribution of digital content. Digital content can be image, video or audio. “Watermarking” is a process where information such as name of the creator, status, recipient, etc. is embedded into the host data in such a way that it will be invisible or undetectable. In this paper a watermarking technique which is a combination of basic techniques, is used. It is a hybrid combination of Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The technique is found to be more robust as compared to the watermarking techniques where only DCT and only DWT are used. The robustness is evaluated by calculating Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).
Comparison of stochastic parametrization approaches in a single-column model

We discuss and test the potential usefulness of single-column models (SCMs) for the testing of stochastic physics schemes that have been proposed for use in general circulation models (GCMs). We argue that although single-column tests cannot be definitive in exposing the full behaviour of a stochastic method in the full GCM, and although there are differences between SCM testing of deterministic and stochastic methods, SCM testing remains a useful tool. It is necessary to consider an ensemble of SCM runs produced by the stochastic method. These can be usefully compared with deterministic ensembles describing initial condition uncertainty and also with combinations of these (with structural model changes) into poor man's ensembles. The proposed methodology is demonstrated using an SCM experiment recently developed by the GCSS (GEWEX Cloud System Study) community, simulating transitions between active and suppressed periods of tropical convection.
Eğitimde Nitel Araştırmalar Dergisi-ENAD

The purpose of this study is to outline the nature of empirical base on transformational leadership in Turkey. The present study included a total of 109 graduate theses conducted on school principals’ transformational leadership behaviors between 2000 and 2019 and released in the Council of Higher Education’s (CoHE) database. Results revealed that an important body of research investigated the relationship between transformational leadership and job satisfaction and organizational commitment. Authors’ suggestions to policymakers and practitioners were often about enabling further in-service training activities and providing school principals with various opportunities to study for a master's degree. Suggestions from quantitative studies echoed the need for conducting relevant studies on different samples and expanding qualitative studies to inquiry into transformational leadership. Results, therefore, mirrored the fact that the empirical base on transformational leadership is lacking in terms of mediator and moderator effects of various variables along with the antecedents and results of transformational leadership.
The arbitrarily varying wiretap channel - secret randomness, stability and super-activation

We study the arbitrarily varying wiretap channel (AVWC) under average error criterion when external common randomness (CR) can be used between the legitimate parties. We consider three scenarios: In the first one the CR is known to the eavesdropper, in the second it is not known to her and in the third there is no CR available. For the second scenario, we prove a complete coding theorem. For the third scenario it is known that the capacity function is discontinuous. We prove that it is nonetheless stable in the sense of being continuous around its positivity points. We characterize the points of discontinuity in terms of continuous functions. We then give a complete characterization of those pairs of AVWCs whose capacity can be super-activated in the unassisted third case - in terms of the capacity function describing the first case.
RANDOM WALKS ON SIMPLICIAL COMPLEXES AND THE NORMALIZED

Modeling complex systems and data with graphs has been a mainstay of the applied mathematics community. The nodes in the graph represent entities and the edges model the relations between them. Simplicial complexes, a mathematical object common in topological data analysis, have emerged as a model for multi-nodal interactions that occur in several complex systems; for example, biological interactions occur between a set of molecules rather than just two, and communication systems can have group messages and not just person-to-person messages. While simplicial complexes can model multi-nodal interactions, many ideas from network analysis concerning dynamical processes lack a proper correspondence in the a simplicial complex model. In particular, diffusion processes and random walks, which underpin large parts of the network analysis toolbox including centrality measures and ranking, community detection, and contagion models, have so far been only scarcely studied for simplicial complexes. Here we develop a diffusion process on simplicial complexes that can serve as a foundation for making simplicial complex models more effective. Our key idea is to generalize the well-known relationship between the normalized graph Laplacian operator and random walks on graphs by devising an appropriate normalization for the Hodge Laplacian, the analog of the graph Laplacian for simplicial complexes. We demonstrate how our diffusion process can be used for system analysis by developing a generalization of PageRank for edges in simplicial complexes and analyzing a book co-purchasing dataset.