Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Periodic Human Behaviour Models from Sparse Data for Crowdsourcing Aid Delivery in Developing Countries

In many developing countries, half the population lives in rural locations, where access to essentials such as school materials, mosquito nets, and medical supplies is restricted. We propose an alternative method of distribution (to standard road delivery) in which the existing mobility habits of a local population are leveraged to deliver aid, which raises two technical challenges in the areas optimisation and learning. For optimisation, a standard Markov decision process applied to this problem is intractable, so we provide an exact formulation that takes advantage of the periodicities in human location behaviour. To learn such behaviour models from sparse data (i.e., cell tower observations), we develop a Bayesian model of human mobility. Using real cell tower data of the mobility behaviour of 50,000 individuals in Ivory Coast, we find that our model outperforms the state of the art approaches in mobility prediction by at least 25% (in held-out data likelihood). Furthermore, when incorporating mobility prediction with our MDP approach, we find a 81.3% reduction in total delivery time versus routine planning that minimises just the number of participants in the solution path.
Low-Rank and Sparse Modeling of High-dimensional Vector Autoregressions

Network modeling of high-dimensional time series in presence of unobserved latent variables is an important problem in macroeconomics and finance. In macroeconomic policy making and forecasting, it is often impossible to observe and incorporate all the relevant series in the analysis. Failure to include these variables often results in spurious connectivity among the observed time series in structural analyses, which may have serious policy implications. In order to accurately estimate a network of Granger causal interactions after accounting for latent effects, we introduce a novel approach of low-rank and sparse vector autoregression (VAR). We argue that in presence of a few latent pervasive factors, the transition matrix of a misspecified VAR model among the observed series can be approximated as the sum of a low-rank and a sparse component. Exploiting this connection, we consider estimating low-rank plus sparse VAR models using a combination of nuclear norm and lasso penalties. We establish non-asymptotic upper bounds on the estimation error rates of the low-rank and the sparse components and demonstrate the advantage of the proposed methodology over ordinary and sparse VAR estimates via numerical experiments.
Message framing and interpersonal orientation at cultural and individual levels

On the basis of self-regulatory goals literature, this study predicts that persons with independent and interdependent self-concepts may respond differently to messages with promotion and prevention frames, though the effect may apply only to highly involved persons. The findings confirm these hypotheses at the cultural level (Experiment 1) and the individual level (Experiment 2), showing that greater product involvement increases the effectiveness of self-concept congruent but not incongruent message framing. In addition to the consequence valence explored in Experiment 1, prior research identifies compliance as another important dimension of message framing. Because people with a prevention-/promotion-oriented self-regulatory focus are sensitive to the presence and absence of negative/positive consequences, the superior effects of congruent message framing for highly involved persons should exist regardless of whether the messages emphasise compliance or noncompliance. Experiment 2 confirms these expectations in a collectivistic culture.
Affective word ratings for concatenative text-to-speech synthesis

This work explores affective word ratings as an auxiliary target cost for unit-selection-based concatenative speech synthesis. The method does not require task-specific crafted corpora, nor does it rely on additional annotations, making it ideal for found data. Following the general philosophy of our text-to-speech system, the approach does not enforce any explicit prosodic model, instead the affect information is implicitly modeled via its contribution to the unit-selection cost function. The auxiliary affective feature vector comprises of continuous ratings in three dimensions (valence, arousal and dominance), extracted at the word level via state-of-the-art sentiment analysis techniques. In this case study, speech data consists of several professionally-produced children's audiobooks totaling about 5 hours of speech. The affective dimensions are shown to correlate well with acoustic/prosodic features extracted from the speech data, highlighting their utility for the affective speech synthesis. This is further confirmed via a preference listening test between the baseline and the affective voice.
Energy-aware VM consolidation approach using combination of heuristics and migration control

Cloud based services are getting more and more eminent as they offer Pay-as-you-go service model. Increased need of computing and reliability are other factors of cloud based datacenters to be grown in number. However, energy consumption by Cloud based Datacenters draw negative attention as they consume considerable amount of energy while in operation. Researchers have proposed various techniques to improve the power consumption scenario of virtualized datacenters. Energy-aware dynamic Virtual Machine consolidation is one of the techniques where utilization of datacenters are monitored and underutilized datacenters are shutdown by migrating VM to other capable datacenters. In this research, we are proposing a new virtual machine consolidation algorithm by doing amalgamation of heuristic approach & migration control and compare it to other heuristic methods which are already proposed in literature. We have used Cloudsim toolkit to simulate our experiment and to evaluate the performance of the proposed algorithm, we have used real-world work load traces of Planet lab VMs. From the comparison with existing energy aware VM consolidation methods, it is found that performance of our algorithm is better than other proposed heuristic approaches.
Effects of course-long use of a program visualization tool

We studied the course-long use of a program visualization tool called ViLLE in high school in Finland. The study was conducted in three consecutive instances of the first programming course. In the first two instances of the course, the students did not utilize ViLLE -- except for a short session -- while in the last instance students did ViLLE exercises throughout the whole course. The students who used ViLLE got significantly better results from the course's final exam. This supports our hypothesis that program visualization can be an effective method in teaching programming, and indicates that we should continue developing program visualization methods to further enhance learning.
Prolonged pericardial drainage using a soft drain reduces pericardial effusion and need for additional pericardial drainage following orthotopic heart transplantation.

OBJECTIVES Pericardial effusion can cause haemodynamic compromise after heart transplantation. We identified the effects of soft drains on the development of pericardial effusion. METHODS We enrolled 250 patients ≥17 years of age who underwent heart transplantation between July 1999 and April 2012 and received two conventional tubes (n = 96; 32 French), or two tubes with a soft drain (n = 154; 4.8 mm wide). The development of significant pericardial effusion or the need for drainage procedure during 1 month after heart transplantation was compared with the use of the propensity score matching method to adjust for selection bias. RESULTS At 1 month after transplantation, 69 patients (27.6%) developed significant pericardial effusion. Among these, 13 patients (5.2%) underwent pericardial drainage. According to multivariate analysis, history of previous cardiac surgery [odds ratio (OR) = 0.162; 95% confidence interval (CI) = 0.046-0.565; P = 0.004] and placement of a soft drain (OR = 0.186; 95% CI = 0.100-0.346; P < 0.001) were significant factors that prevented pericardial effusion or the need for drainage during the early postoperative period. For the 82 propensity score matched pairs, patients receiving an additional soft drain were at a lower risk of the development of significant pericardial effusion or the need for a pericardial drainage procedure during 1 month (OR = 0.148; 95% CI = 0.068-0.318; P < 0.001) compared with those receiving only two conventional tubes. CONCLUSIONS Pericardial soft drainage is a simple and safe procedure that reduces pericardial effusion and decreases the need for pericardial drainage after heart transplantation.
Performance evaluation of feature detection in using subsampled images for image stitching

Image stitching is a technique being used to stitch multiple images together to form a stitched image with higher resolution and larger field of view. It has been adopted in many research areas such as computer vision and medical applications. The first step in stitching is to first determine the overlap region or similarity. This can be done using feature extraction, follow by comparing the feature points in the images. Once the similarity is determined, the images are transformed into the same coordinate system that agreed by each other. Finally, the images can be pasted together. In this paper, the performance of various type of feature detector in extracting feature points from the subsampled version of the original images will be evaluated. The advantage of using the subsampled images is to reduce the memory requirement, and processing time.
Reconceptualising transition to Higher Education with Deleuze and Guattari

ABSTRACT This article draws on the philosophy of Deleuze and Guattari to reconceptualise transition to Higher Education. In doing so it contributes a new theoretical approach to understanding transition to Higher Education which largely remains under-theorised, uncritical and taken-for-granted. Drawing on data from two projects, the article activates Deleuze and Guattari’s concepts of assemblage, rhizome and becoming to contest the established view of transition as a linear pathway or series of ‘critical incidents’. The article illuminates how Deleuze and Guattari’s concepts are of value both in theorising the multiplicity and heterogeneity of transition and in refocusing attention on the lived specificities of students’ experiences within a complex web of institutional and affective practices. The article ends with a consideration of how Deleuze and Guattari recast understandings of transitions theory and practice.
THE WEB AS A COMMON LANGUAGE TOWARDS SOFTWARE SUSTAINABILITY

In this paper, we explore the potential of the Web to constitute a common language in various computing domains towards increasing software sustainability. We discuss various benefits of Web development, which offer sustainable characteristics to software. We demonstrate two case studies, in which the Web can be used for software development. The first is a mobile application offering tourist guide services and the second is a real-world pervasive framework, targeting home automation in a smart home environment. Our experimental results show that both software operate in satisfactory performance, showing that the benefits of Web development can be acquired without significant compromises in response times.
On evaluating performance of exploration strategies for an autonomous mobile robot

— The performance of an autonomous mobile robot in mapping an unknown environment is strongly related to its exploration strategy. Evaluation and comparison of exploration strategies are still waiting for the deﬁnition of standard benchmark methodologies. In this paper, we contribute in this direction by discussing some critical issues we faced in experimentally evaluating exploration strategies.
Multiple axonal spike initiation zones in a motor neuron: serotonin activation

The lateral gastric (LG) motor neuron of the stomatogastric nervous system of the crab Cancer borealis has a large soma in the stomatogastric ganglion (STG). The LG motor neuron makes inhibitory synaptic connections within the neuropil of the STG, and also projects to the periphery, where it innervates a series of muscles that control the movements of the lateral teeth of the gastric mill. The LG motor neuron has a spike initiation zone close to its neuropilar integrative regions, from which spikes propagate orthodromically to the muscles. Additionally, under certain conditions, the LG neuron can initiate spikes at peripheral axonal sites that can be 0.5–2.0 cm from the STG. Peripherally initiated spikes propagate antidromically into the STG and also propagate to the muscle. The peripheral spike initiation zones are often active in combined preparations in which the muscles are left attached. When the muscles are removed, depolarization of the LG soma together with 5-HT applied to the motor nerve also evokes peripheral spike initiation. At a given 5-HT concentration, the duration of the trains of antidromic spikes can be controlled by current injection into the soma, suggesting the presence of a slow voltage-dependent conductance in the LG axon. The antidromic spikes contribute to lengthening of the duration of contraction in some of the muscles innervated by the LG, but do not evoke IPSPs onto LG follower neurons. Thus, the LG neuron can send different signals to its peripheral and central targets.
An Analysis of Upward Influence Strategies Using Speech Act Theory and Face Threatening Acts

This study borrows from sociolinguistic research, specifically Speech Act Theory (SAT), as a way to analyze and explain how UI strategies are performed. Based on SAT and considerations of FACE, as explained by Brown and Levinson (1978/1987), we have attempted to provide an explanation for the choice of strategies used by members within the organization. Through qualitative discourse analysis, we suggest that for effective upward influencing, professional communicators need to consider the verbalization of UI strategies.
Asynchronous nuclear division cycles in multinucleated cells

Synchronous mitosis is common in multinucleated cells. We analyzed a unique asynchronous nuclear division cycle in a multinucleated filamentous fungus, Ashbya gossypii. Nuclear pedigree analysis and observation of GFP-labeled spindle pole bodies demonstrated that neighboring nuclei in A. gossypii cells are in different cell cycle stages despite close physical proximity. Neighboring nuclei did not differ significantly in their patterns of cyclin protein localization such that both G1 and mitotic cyclins were present regardless of cell cycle stage, suggesting that the complete destruction of cyclins is not occurring in this system. Indeed, the expression of mitotic cyclin lacking NH2-terminal destruction box sequences did not block cell cycle progression. Cells lacking AgSic1p, a predicted cyclin-dependent kinase (CDK) inhibitor, however, showed aberrant multipolar spindles and fragmented nuclei that are indicative of flawed mitoses. We hypothesize that the continuous cytoplasm in these cells promoted the evolution of a nuclear division cycle in which CDK inhibitors primarily control CDK activity rather than oscillating mitotic cyclin proteins.
Relaying via hybrid coding

Motivated by the recently developed hybrid coding scheme for joint source-channel coding, this paper proposes a new coding scheme for noisy relay networks. The proposed coding scheme operates in a similar manner to the noisy network coding scheme, except that each relay node uses the hybrid coding interface to transmit a symbol-by-symbol function of the received sequence and its quantized version. This coding scheme unifies both amplify-forward and noisy network coding and can strictly outperform both. The potential of the hybrid coding interface for relaying is demonstrated through the diamond relay network and two-way relay channel examples.
Text Categorization and Support Vector Machines

Text categorization is used to automatically assign previously unseen documents to a predefined set of categories. This paper gives a short introduction into text categorization (TC), and describes the most important tasks of a text categorization system. It also focuses on Support Vector Machines (SVMs), the most popular machine learning algorithm used for TC, and gives some justification why SVMs are suitable for this task. After the short introduction some interesting text categorization systems are described briefly, and some open problems are presented.
Correction: Dawn and Dusk Set States of the Circadian Oscillator in Sprouting Barley (Hordeum vulgare) Seedlings

There is an error in Fig 2C. Specifically, the panels for Fig 2C showing expression data for HvTOC1, HvGI and HvPRR73 were duplicates of panels from Fig 1C. The duplication of these images occurred during figure preparation and was an error made by the corresponding author (B. Trevaskis). The correction does not alter the conclusions of the study. The authors apologise for this error and thank the diligent reader who brought this to our attention. Fig 2 Clock gene expression after light/dark transitions or with exogenous sucrose. Please see the complete, correct Fig 2 here.
New RFID's chip matching technique using Artificial Neural Networks

This paper proposes a new technique to modify the original design of a known antenna according to the requirement of a new chip with an input impedance different from the first design This methodology is based on the idea of creating a database with antenna simulations and use Artificial Neural Networks (ANN) as a tool for selection of the appropriated configurations necessary for a better performance.
BOUNDEDNESS IN LANGUAGES OF INFINITE

We define a new class of languages of ω-words, strictly extending ω-regular languages. One way to present this new class is by a type of regular expressions. The new expressions are an extension of ω-regular expressions where two new variants of the Kleene star L are added: L and L . These new exponents are used to say that parts of the input word have bounded size, and that parts of the input can have arbitrarily large sizes, respectively. For instance, the expression (ab) represents the language of infinite words over the letters a, b where there is a common bound on the number of consecutive letters a. The expression (ab) represents a similar language, but this time the distance between consecutive b’s is required to tend toward the infinite. We develop a theory for these languages, with a focus on decidability and closure. We define an equivalent automaton model, extending Büchi automata. The main technical result is a complementation lemma that works for languages where only one type of exponent—either L or L—is used. We use the closure and decidability results to obtain partial decidability results for the logic MSOLB, a logic obtained by extending monadic second-order logic with new quantifiers that speak about the size of sets.
Shared Ancestry of Symbionts? Sagrinae and Donaciinae (Coleoptera, Chrysomelidae) Harbor Similar Bacteria

When symbioses between insects and bacteria are discussed, the origin of a given association is regularly of interest. We examined the evolution of the symbiosis between reed beetles (Coleoptera, Chrysomelidae, Donaciinae) and intracellular symbionts belonging to the Enterobacteriaceae. We analyzed the partial sequence of the 16S rRNA to assess the phylogenetic relationships with bacteria we found in other beetle groups (Cerambycidae, Anobiidae, other Chrysomelidae). We discuss the ecology of each association in the context of the phylogenetic analysis. The bacteria in Sagra femorata (Chrysomelidae, Sagrinae) are very closely related to those in the Donaciinae and are located in similar mycetomes. The Sagrinae build a cocoon for pupation like the Donaciinae, in which the bacteria produce the material required for the cocoon. These aspects support the close relationship between Sagrinae and Donaciinae derived in earlier studies and make a common ancestry of the symbioses likely. Using PCR primers specific for fungi, we found Candida sp. in the mycetomes of a cerambycid beetle along with the bacteria.
Visual gyroscope: Integration of visual information with gyroscope for attitude measurement of mobile platform

In this paper, fusion of visual and inertial information is proposed for attitude measurement of remotely-operated mobile robots. The primary objective is to eliminate the drift error of the inertial sensors using visual information for exact and fast sensing of attitude. By this fusion each output is not only combined together but also helps the other yield better result. The environment is assumed unknown and corner features are utilized for rotation matrix calculation, for corners are better extracted in nature scene than lines. The measured information is to be used for stabilizing the image sent to the operator and for stabilizing the robot body itself in bumpy terrain. In the paper, the fusion algorithm based on Kalman filter is utilized, and its performance is evaluated by experiment. For the experiment, high performance camera is used with low-quality gyro sensors. The experimental results show that the vision algorithm well tracks the attitude change of the camera, and by fusing that with gyro sensor output, we could get high rate angle measurement without drift error.
Adapting deep belief nets to Chinese entity detection

This paper adapts deep belief networks (DBN) to detect entity mentions in Chinese documents. Our results exhibit how the depth of architecture and quantity of unit in hidden layer influence the performance. Different feature combinations are used to show their advantages and disadvantages in DBN for this task. Moreover, we combined Chinese word segmentation systems to alleviate word segmentation error. Token labels are produced independently by DBN which does not concerned what are the token labels before current word. Viterbi algorithm is a good solution to find the most likely probability label path to make DBN be more effective for entity detection. Furthermore, this paper demonstrates DBN is a proper model for our tasks and its results are better than Support Vector Machine (SVM), Artificial Neural Network (ANN) and Conditional Random Field (CRF).
No . TR-165 Learning with Transformation Invariant Kernels †

This paper considers kernels invariant to translation, rotation and dilation. We show that no non-trivial positive definite (p.d.) kernels exist which are radial and dilation invariant, only conditionally positive definite (c.p.d.) ones. Accordingly, we discuss the c.p.d. case and provide some novel analysis, including an elementary derivation of a c.p.d. representer theorem. On the practical side, we give a support vector machine (s.v.m.) algorithm for arbitrary c.p.d. kernels. For the thin-plate kernel this leads to a classifier with only one parameter (the amount of regularisation), which we demonstrate to be as effective as an s.v. m. with the Gaussian kernel, even though the Gaussian involves a second parameter (the length scale).