Miracle-dz/UniFAR-FTUs huggingface dataset:
What Makes Brain Drain More Likely?

This paper studies the effects of migration on the schooling investment of heterogeneous households. I use an IV-discrete choice model of schooling investment, which distinguishes between migration attempts and actual migrations to (partially) identify the net effect of migration on schooling investments. Looking at emigration from Senegal to Europe, I find a negative net effect of migration on the enrollment in upper secondary education for many sub-groups of households in Senegal. Interestingly, there is a gender difference in the causes of these negative signs: positively skill-biased migration leads to the negative net effect observed on women, whereas disincentives to invest in education drive the negative net effect observed on men. Furthermore, the analysis suggests that financially constrained households substitute an investment in migration to an investment in education.
Veriﬁed Compilation of Weak Call-by-Value Lambda-Calculus into Combinators and Closures

In this thesis, we relate the weak call-by-value λ -calculus L to two other systems: a call-by-value combinatory logic called SKv and a variant of L with closures, called LC. All proofs are carried out in the proof assistant Coq. We show that L can be fully embedded into SKv by giving an injective mapping from L to SKv that preserves term equivalence and irreducibility in both directions. We show that LC reﬁnes the semantics of L: It is sound and complete with respect to L, while allowing to postpone certain parts of computations until they become necessary. We give a sound and complete interpreter for LC that can be used to interpret L.
A Fuzzy Controlled High Voltage Boosting Converter Based On Boost Inductors and Capacitors

A novel high voltage-boosting converter topology is explained in this paper. By changing the connection position of the anode of the diode two types of converters are obtained-Type1 and Type2.These converters are constructed based on boost inductors and capacitors and have different voltage conversion ratios. Furthermore, although there are three switches in each converter, only one half-bridge gate driver and one low-side gate driver are needed, but no isolated gate driver would be needed. In this paper, closed loop control of type2 converter using fuzzy logic controller is explained. Some simulated results are provided to verify the effectiveness of the proposed converter.
The Application of a New PID Autotuning Method for the Steam/Water Loop in Large Scale Ships

In large scale ships, the most used controllers for the steam/water loop are still the proportional-integral-derivative (PID) controllers. However, the tuning rules for the PID parameters are based on empirical knowledge and the performance for the loops is not satisfying. In order to improve the control performance of the steam/water loop, the application of a recently developed PID autotuning method is studied. Firstly, a ‘forbidden region’ on the Nyquist plane can be obtained based on user-defined performance requirements such as robustness or gain margin and phase margin. Secondly, the dynamic of the system can be obtained with a sine test around the operation point. Finally, the PID controller’s parameters can be obtained by locating the frequency response of the controlled system at the edge of the ‘forbidden region’. To verify the effectiveness of the new PID autotuning method, comparisons are presented with other PID autotuning methods, as well as the model predictive control. The results show the superiority of the new PID autotuning method.
Improving the Classification of Emotions by Studying Facial Feature

—This work aims to recognize the six basic emotions using facial expression and improve the classification in term of time and space memory. We started with the idea that emotions can be absolutely distinctive and from a single feature we can recognize emotion and therefore we save time in learning data. We also noted that the similarity between the characteristics of two emotions can cause a lot of errors. So we concluded that it is necessary to specify the characteristics for each emotion to improve classification. The study of various facial features (eyes, eyebrows and mouth) of each emotion has helped us to find a new use of face features and ultimately lead to a faster classification.
Ry ū gaku: Explorations in Study Abroad

This paper documents a pilot study that tracked the anxiety levels of 32 private university Japanese students who used smartphone-based virtual reality (VR) tasks to prepare for an 8-week study abroad program. First, the paper discusses background information concerning study abroad anxiety, current VR technology, VR technology in language learning, and the role of Cognitive Behavioral Therapy as a theoretical basis for using VR in language learning. Second, the paper describes the methodology and analysis used. Last the findings and limitations and study are discussed and addressed. Overall, data in the form of quantitative surveys and qualitative comments suggest that VR-based tasks have the potential to reduce students’ feelings of anxiety towards studying abroad in a foreign country.
Brucella species circulating in wildlife in Serengeti ecosystem, Tanzania

Background Brucellosis is a bacterial zoonosis of public health and economic importance world-wide. It affects a number of domestic animals, wildlife and humans. This study was carried out to determine circulating Brucella species in wildlife in Serengeti ecosystem using molecular techniques. Methodology A total of 189 samples including EDTA blood, serum and amniotic fluid from buffalos, lions, wildebeest, impala, zebra and hyena that were collected in relation to different cross-sectional studies conducted in the Serengeti ecosystem in Tanzania were used. Multiplex polymerase chain reaction AMOS-PCR and quantitative Real-Time PCR (qPCR) targeting the genus specific surface protein bcsp31 gene and the insertion sequence IS711 element downstream of the alkB gene for B. abortus and BMEI1162 gene for B. melitensis were employed on the samples. Results Results indicated that out of 189 samples examined, 12 (6.4%) and 22 (11.6%) contained Brucella DNA as detected by AMOS-PCR and qPCR, respectively. Most of the positive samples were from lions (52.6%) and buffaloes (19.6%). Other animals that were positive included wildebeest, impala, zebra and hyena. Out of 22 positive samples, 16 (66.7%) were identified as B. abortus and the rest were B. melitensis. Conclusion Detection of zoonotic Brucella species in wildlife suggests that livestock and humans at the interface areas where there is high interaction are at risk of acquiring the infection. Therefore, public education to interrupt risky transmission practices is needed. The findings also shed light on the transmission dynamics around interface areas and the role of wildlife in transmission and maintenance of Brucella infection in the region.
Does place of residence affect patient satisfaction with hospital health care?

INTRODUCTION AND OBJECTIVE Measuring the level of patient satisfaction is a useful tool in delivering quality care that is responsive to consumer preferences. Various socio-demographic factors might be considered as potential predictors of patient satisfaction. The aim of the study was to assess whether place of residence (rural/urban) affects patient satisfaction with hospital health care. MATERIAL AND METHODS Data were obtained using face-to-face questionnaire, administered in five large, urban hospitals in Podlaskie Province, north-eastern Poland, during 2014-2018. The study sample comprised of 1,624 participants (585 rural, 1039 urban) who assessed satisfaction with 28 hospital health care items. The means and standard deviations were presented to compare 28 satisfaction items between the rural and the urban samples. Regression analysis was used to determine whether location difference (rural vs urban) affected patient satisfaction with various domains of hospital health care. RESULTS The mean results of 28 satisfaction items on the 1-5 scale were similar among the rural and the urban samples, and generally skewed towards positive experiences. In the univariate analysis, significant associations between place of residence and patient satisfaction were identified with regard to three components of inpatient care: 1) hospital settings and staff care, 2) doctors' professional skills, and 3) hospitalization outcomes. After adjusting for socio-demographic variables, the association remained significant only with respect to satisfaction with hospitalization outcomes (b = 0.121; SE = 0.055; p = 0.028). CONCLUSIONS Some evidence for differences in patient satisfaction by place of residence was found. Study findings may be helpful in implementing care quality improvement programmes.
Balancing Cost and Benefit with Tied-Multi Transformers

We propose a novel procedure for training multiple Transformers with tied parameters which compresses multiple models into one enabling the dynamic choice of the number of encoder and decoder layers during decoding. In training an encoder-decoder model, typically, the output of the last layer of the N-layer encoder is fed to the M-layer decoder, and the output of the last decoder layer is used to compute loss. Instead, our method computes a single loss consisting of NxM losses, where each loss is computed from the output of one of the M decoder layers connected to one of the N encoder layers. Such a model subsumes NxM models with different number of encoder and decoder layers, and can be used for decoding with fewer than the maximum number of encoder and decoder layers. Given our flexible tied model, we also address to a-priori selection of the number of encoder and decoder layers for faster decoding, and explore recurrent stacking of layers and knowledge distillation for model compression. We present a cost-benefit analysis of applying the proposed approaches for neural machine translation and show that they reduce decoding costs while preserving translation quality.
No-Regret and Incentive-Compatible Online Learning

We study online learning settings in which experts act strategically to maximize their influence on the learning algorithm's predictions by potentially misreporting their beliefs about a sequence of binary events. Our goal is twofold. First, we want the learning algorithm to be no-regret with respect to the best fixed expert in hindsight. Second, we want incentive compatibility, a guarantee that each expert's best strategy is to report his true beliefs about the realization of each event. To achieve this goal, we build on the literature on wagering mechanisms, a type of multi-agent scoring rule. We provide algorithms that achieve no regret and incentive compatibility for myopic experts for both the full and partial information settings. In experiments on datasets from FiveThirtyEight, our algorithms have regret comparable to classic no-regret algorithms, which are not incentive-compatible. Finally, we identify an incentive-compatible algorithm for forward-looking strategic agents that exhibits diminishing regret in practice.
iSEGAN: Improved Speech Enhancement Generative Adversarial Networks

Popular neural network-based speech enhancement systems operate on the magnitude spectrogram and ignore the phase mismatch between the noisy and clean speech signals. Conditional generative adversarial networks (cGANs) show promise in addressing the phase mismatch problem by directly mapping the raw noisy speech waveform to the underlying clean speech signal. However, stabilizing and training cGAN systems is difficult and they still fall short of the performance achieved by the spectral enhancement approaches. This paper investigates whether different normalization strategies and one-sided label smoothing can further stabilize the cGAN-based speech enhancement model. In addition, we propose incorporating a Gammatone-based auditory filtering layer and a trainable pre-emphasis layer to further improve the performance of the cGAN framework. Simulation results show that the proposed approaches improve the speech enhancement performance of cGAN systems in addition to yielding improved stability and reduced computational effort.
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems

We consider the problem of learning stabilizable systems governed by nonlinear state equation $h_{t+1}=\phi(h_t,u_t;\theta)+w_t$. Here $\theta$ is the unknown system dynamics, $h_t $ is the state, $u_t$ is the input and $w_t$ is the additive noise vector. We study gradient based algorithms to learn the system dynamics $\theta$ from samples obtained from a single finite trajectory. If the system is run by a stabilizing input policy, we show that temporally-dependent samples can be approximated by i.i. d. samples via a truncation argument by using mixing-time arguments. We then develop new guarantees for the uniform convergence of the gradients of empirical loss. Unlike existing work, our bounds are noise sensitive which allows for learning ground-truth dynamics with high accuracy and small sample complexity. Together, our results facilitate efficient learning of the general nonlinear system under stabilizing policy. We specialize our guarantees to entry-wise nonlinear activations and verify our theory in various numerical experiments
Compositional Neural Machine Translation by Removing the Lexicon from Syntax

The meaning of a natural language utterance is largely determined from its syntax and words. Additionally, there is evidence that humans process an utterance by separating knowledge about the lexicon from syntax knowledge. Theories from semantics and neuroscience claim that complete word meanings are not encoded in the representation of syntax. In this paper, we propose neural units that can enforce this constraint over an LSTM encoder and decoder. We demonstrate that our model achieves competitive performance across a variety of domains including semantic parsing, syntactic parsing, and English to Mandarin Chinese translation. In these cases, our model outperforms the standard LSTM encoder and decoder architecture on many or all of our metrics. To demonstrate that our model achieves the desired separation between the lexicon and syntax, we analyze its weights and explore its behavior when different neural modules are damaged. When damaged, we find that the model displays the knowledge distortions that aphasics are evidenced to have.
Designing Asymmetric Shift Operators for Decentralized Subspace Projection

A large number of applications in wireless sensor networks include projecting a vector of noisy observations onto a subspace dictated by prior information about the field being monitored. In general, accomplishing such a task in a centralized fashion, entails a large power consumption, congestion at certain nodes, and suffers from robustness issues against possible node failures. Computing such projections in a decentralized fashion is an alternative solution that solves these issues. Recent works have shown that this task can be done via the so-called graph filters where only local inter-node communication is performed in a distributed manner using a graph shift operator. Existing methods designed the graph filters for symmetric topologies. However, in this paper, the design of the graph shift operators to perform decentralized subspace projection for asymmetric topologies is studied.
Fast and Regularized Reconstruction of Building Façades from Street-View Images using Binary Integer Programming

Abstract. Regularized arrangement of primitives on building façades to aligned locations and consistent sizes is important towards structured reconstruction of urban environment. Mixed integer linear programing was used to solve the problem, however, it is extremely time consuming even for state-of-the-art commercial solvers. Aiming to alleviate this issue, we cast the problem into binary integer programming, which omits the requirements for real value parameters and is more efficient to be solved. Firstly, the bounding boxes of the primitives are detected using the YOLOv3 architecture in real-time. Secondly, the coordinates of the upper left corners and the sizes of the bounding boxes are automatically clustered in a binary integer programming optimization, which jointly considers the geometric fitness, regularity and additional constraints; this step does not require a priori knowledge, such as the number of clusters or pre-defined grammars. Finally, the regularized bounding boxes can be directly used to guide the façade reconstruction in an interactive environment. Experimental evaluations have revealed that the accuracies for the extraction of primitives are above 0.82, which is sufficient for the following 3D reconstruction. The proposed approach only takes about 10% to 20% of the runtime than previous approach and reduces the diversity of the bounding boxes to about 20% to 50%.
Photorealistic Lip Sync with Adversarial Temporal Convolutional Networks

Lip sync has emerged as a promising technique to generate mouth movements on a talking head. However, synthesizing a clear, accurate and human-like performance is still challenging. In this paper, we present a novel lip-sync solution for producing a high-quality and photorealistic talking head from speech. We focus on capturing the specific lip movement and talking style of the target person. We model the seq-to-seq mapping from audio signals to mouth features by two adversarial temporal convolutional networks. Experiments show our model outperforms traditional RNN-based baselines in both accuracy and speed. We also propose an image-to-image translation-based approach for generating high-resolution photoreal face appearance from synthetic facial maps. This fully-trainable framework not only avoids the cumbersome steps like candidate-frame selection in graphics-based rendering methods but also solves some existing issues in recent neural network-based solutions. Our work will benefit related applications such as conversational agent, virtual anchor, tele-presence and gaming.
Emergent criticality and universality class of the finite-temperature charge-density-wave transition in lattice Bose gases within optical cavities

We investigate the finite temperature charge density wave (CDW) transition of lattice Bose gases within optical cavities in the deep Mott-insulator limit. We find a new critical regime emerges at a temperature around one-half of the on-site interaction energy, where the first order CDW transition at low temperatures terminates at a critical point and changes to a second order one. By directly calculating the critical exponents and constructing the effective theory in the corresponding critical regime, we find the emergent criticality belongs to the five-dimensional Ising universality class. Direct experimental observation of the emergent criticality can be readily performed by current experimental set-ups operated in the temperature regime around half the on-site interaction energy.
Learn to Design the Heuristics for Vehicle Routing Problem

This paper presents an approach to learn the local-search heuristics that iteratively improves the solution of Vehicle Routing Problem (VRP). A local-search heuristics is composed of a destroy operator that destructs a candidate solution, and a following repair operator that rebuilds the destructed one into a new one. The proposed neural network, as trained through actor-critic framework, consists of an encoder in form of a modified version of Graph Attention Network where node embeddings and edge embeddings are integrated, and a GRU-based decoder rendering a pair of destroy and repair operators. Experiment results show that it outperforms both the traditional heuristics algorithms and the existing neural combinatorial optimization for VRP on medium-scale data set, and is able to tackle the large-scale data set (e.g., over 400 nodes) which is a considerable challenge in this area. Moreover, the need for expertise and handcrafted heuristics design is eliminated due to the fact that the proposed network learns to design the heuristics with a better performance. Our implementation is available online.
Diversity sampling is an implicit regularization for kernel methods

Kernel methods have achieved very good performance on large scale regression and classification problems, by using the Nystrom method and preconditioning techniques. The Nystrom approximation -- based on a subset of landmarks -- gives a low rank approximation of the kernel matrix, and is known to provide a form of implicit regularization. We further elaborate on the impact of sampling diverse landmarks for constructing the Nystrom approximation in supervised as well as unsupervised kernel methods. By using Determinantal Point Processes for sampling, we obtain additional theoretical results concerning the interplay between diversity and regularization. Empirically, we demonstrate the advantages of training kernel methods based on subsets made of diverse points. In particular, if the dataset has a dense bulk and a sparser tail, we show that Nystrom kernel regression with diverse landmarks increases the accuracy of the regression in sparser regions of the dataset, with respect to a uniform landmark sampling. A greedy heuristic is also proposed to select diverse samples of significant size within large datasets when exact DPP sampling is not practically feasible.
The minimal genus of homology classes in a
finite 2–complex

We study surface representatives of homology classes of finite complexes which minimize certain complexity measures, including its genus and Euler characteristic. Our main result is that up to surgery at nullhomotopic curves minimizers are homotopic to cellwise coverings to the 2-skeleton. From this we conclude that the minimizing problem is in general algorithmically undecidable, but can be solved for 2-dimensional CAT(-1)-complexes.
Contact (+1)-surgeries on rational homology 3-spheres

In this paper, sufficient conditions for contact $(+1)$-surgeries along Legendrian knots in contact rational homology 3-spheres to have vanishing contact invariants or to be overtwisted are given. They can be applied to study the contact $(\pm1)$-surgeries along Legendrian links in the standard contact 3-sphere. We also obtain a sufficient condition for contact $(+1)$-surgeries along Legendrian two-component links in the standard contact 3-sphere to be overtwisted via their front projections.
Continuous Influence-Based Community Partition for Social Networks

Community partition is of great importance in social networks because of the rapid increasing network scale, data and applications. We consider the community partition problem under Linear Threshold (LT) model in social networks, which is a combinatorial optimization problem that divides the social network to disjoint <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> communities. Our goal is to maximize the sum of influence propagation within each community. As the influence propagation function of community partition problem is supermodular under LT model, we use the method of Lov<inline-formula><tex-math notation="LaTeX">${\acute{a}}$</tex-math></inline-formula>sz Extension to relax the target influence function and transfer our goal to maximize the relaxed function over a matroid polytope. Next, we propose a continuous greedy algorithm using the properties of the relaxed function to solve our problem, which needs to be discretized in concrete implementation. Then, random rounding technique is used to convert the fractional solution to the integer solution. We present a theoretical analysis with <inline-formula><tex-math notation="LaTeX">$1-1/e$</tex-math></inline-formula> approximation ratio for the proposed algorithms. Extensive experiments are conducted to evaluate the performance of the proposed continuous greedy algorithms on real-world online social networks datasets. The results demonstrate that continuous community partition method can improve influence spread and accuracy of the community partition effectively.