Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning for Jobs OECD Reviews of Vocational Education and Training

For OECD member countries, high-level workplace skills are a key means of supporting economic growth. Systems of vocational education and training (VET), or career and technical education (CTE) in the United States, are now under intensive scrutiny to determine if they can deliver the skills required. Based on reviews in 16 countries, ‘Learning for jobs’ is an OECD study designed to help countries make their VET systems more responsive to labour market needs. In Texas, secondary level CTE is provided within a comprehensive high school framework. Texas has encouraged a wide variety of innovative learning initiatives, established good articulation between high school CTE and postsecondary programs and benefits from a strong network of community colleges and universities. At the same time the state faces some challenges in ensuring minimum quality standards in CTE, in providing sufficient good quality career advice, and providing an adequate range of workplace learning opportunities. The OECD review assesses the main challenges faced by the VET system and presents an interconnected package of policy recommendations. For each recommendation, the report describes the challenge, the recommendation itself, supporting arguments, and issues of implementation.
An examination of the organizational and cross-cultural challenges facing international hotel managers in Russia.

With the ever increasing globalization of the hospitality industry, an exploration of the issues facing international hotel managers in Russia is both timely and important. The ability to manage people in an international context is one of the greatest tests of managerial skill and, in many novel cultures, success is dependent not only on the managers’ technical and professional expertise but also on their understanding of the local culture and local staff. There are many important cultural factors that impact on the expectations and attitudes of Russians about work which are therefore essential for managers to appreciate and understand. The paper highlights the range of challenges that exist and illustrates those that arise from cross‐cultural differences between eastern and western expectations, as well as the range of diverse organizational cultures within Russia’s hotel industry. Managers must be aware of these differences and develop appropriate skills and competencies to help them adapt their style accordingly.
Where You Stand Is Where You Sit: An Academic Administrator's Handbook

The old aphorism "Where you stand is where you sit" rings true across many sectors of society. For academic administrators, be they provosts, vice-presidents or vice-chancellors, deans or directors, department chairs or heads, or administrative support professionals, the level and scope of responsibilities clearly influence perspectives. Yet, having a understanding of the higher-education enterprise is essential to ensuring professional success and advancement. Such understanding is at the heart of this work. This handbook addresses the three key responsibilities of academic officers: inspiration, evaluation, and representation. "Getting a Good Start" deals with the promise of a new position, communicating with supervisors, and "getting around. " "Offering Inspiration and Direction" looks at the integrated scholar and "academic intrapreneurs"; diversity; the joys, challenges, and failure of professional life; and dealing with tragedies. "Guidance to Various Academic Administrators and Support Staff" examines the development, roles, and responsibilities of academic officers and institutional planning and budgeting. Reviewing the state of the institution and its personnel is covered in "Assessments and Evaluations," and "Policies and Partnerships" deals with ethics-based policies, academic consortia and partnerships, and international outreach. Throughout this valuable handbook, Smith offers background, advice, and examples that will interest both the novice and seasoned administrator as he takes us on a tour of success stories, challenges, and foibles.
MapReduce based convolutional neural networks

Convolutional neural networks (CNNs) have gained global recognition in advancing the field of artificial intelligence and have had great successes in a wide array of applications including computer vision, speech and natural language processing. However, due to the rise of big data and increased complexity of tasks, the efficiency of training CNNs have been severely impacted. To achieve state-of-art results, CNNs require tens to hundreds of millions of parameters that need to be fine-tuned, resulting in extensive training time and high computational cost. To overcome these obstacles, this thesis takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. Close examination of the implementation of MapReduce based CNNs as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high accuracy in classification and improvements in speedup, scaleup and sizeup compared to the standard algorithm.
How Big are the Tax Benefits of Debt?

I integrate under firm‐specific benefit functions to estimate that the capitalized tax benefit of debt equals 9.7 percent of firm value (or as low as 4.3 percent, net of personal taxes). The typical firm could double tax benefits by issuing debt until the marginal tax benefit begins to decline. I infer how aggressively a firm uses debt by observing the shape of its tax benefit function. Paradoxically, large, liquid, profitable firms with low expected distress costs use debt conservatively. Product market factors, growth options, low asset collateral, and planning for future expenditures lead to conservative debt usage. Conservative debt policy is persistent.
Inequality of income, illiteracy and medical care as determinants of infant mortality in underdeveloped countries.

Abstract The primary purpose of the paper is to explain the differences in infant mortality rates for a sample of 46 underdeveloped countries. The study differs from earlier research in this field in the following respects. First, an attempt is made to include explanatory variables which have some bearing upon policy discussions. Secondly, considerable attention is devoted to the correct specification of the regression equation. Thirdly, an attempt is made to assess the sensitivity of the estimates to changes in (i) the composition of the sample and (ii) the observations on infant mortality. Finally, the data employed in the study appear to be somewhat less inaccurate than the figures which have been used elsewhere. The most satisfactory empirical results are obtained from a logarithmic model, in which the illiteracy rate for women (I), nurses per head of population (N), physicians per head (P), and the coefficient of variation (V) — a measure of inequality of incomes — appear as explanatory variables. Th.
Learning Cluster Structured Sparsity by Reweighting

Recently, the paradigm of unfolding iterative algorithms into finite-length feed-forward neural networks has achieved a great success in the area of sparse recovery. Benefit from available training data, the learned networks have achieved state-of-the-art performance in respect of both speed and accuracy. However, the structure behind sparsity, imposing constraint on the support of sparse signals, is often an essential prior knowledge but seldom considered in the existing networks. In this paper, we aim at bridging this gap. Specifically, exploiting the iterative reweighted $\ell_1$ minimization (IRL1) algorithm, we propose to learn the cluster structured sparsity (CSS) by rewegihting adaptively. In particular, we first unfold the Reweighted Iterative Shrinkage Algorithm (RwISTA) into an end-to-end trainable deep architecture termed as RW-LISTA. Then instead of the element-wise reweighting, the global and local reweighting manner are proposed for the cluster structured sparse learning. Numerical experiments further show the superiority of our algorithm against both classical algorithms and learning-based networks on different tasks.
Customizing Sequence Generation with Multi-Task Dynamical Systems

Dynamical system models (including RNNs) often lack the ability to adapt the sequence generation or prediction to a given context, limiting their real-world application. In this paper we show that hierarchical multi-task dynamical systems (MTDSs) provide direct user control over sequence generation, via use of a latent code $\mathbf{z}$ that specifies the customization to the individual data sequence. This enables style transfer, interpolation and morphing within generated sequences. We show the MTDS can improve predictions via latent code interpolation, and avoid the long-term performance degradation of standard RNN approaches.
Dynamic Spectrum Sharing for Load Balancing in Multi-Cell Mobile Edge Computing

Large-scale mobile edge computing (MEC) systems require scalable solutions to allocate communication and computing resources to the users. In this letter we address this challenge by applying dynamic spectrum sharing among the base stations (BSs), together with local resource allocation in the cells. We show that the network-wide resource allocation can be transformed into a convex optimization problem, and propose a distributed, hierarchical solution with limited information exchange among the BSs. Numerical results demonstrate that the proposed solution is superior to other baseline algorithms, when wireless and computing resource allocation is not jointly optimized, or the wireless resources allocated to the BSs are fixed.
Design and Prototyping of a Bio-Inspired Kinematic Sensing Suit for the Shoulder Joint: Precursor to a Multi-DoF Shoulder Exosuit

Soft wearable robots represent a promising new design paradigm for rehabilitation and active assistance applications. Their compliant nature makes them ideal for complex joints, but intuitive control of these robots require robust and compliant sensing mechanisms. In this work, we introduce the sensing framework for a multiple degrees-of-freedom shoulder exosuit capable of sensing the kinematics of the joint. The proposed sensing system is inspired by the body's embodied kinematic sensing, and the organisation of muscles and muscle synergies responsible for shoulder movements. A motion-capture-based evaluation study of the developed framework confirmed conformance with the behaviour of the muscles that inspired its routing. This validation of the tendon-routing hypothesis allows for it to be extended to the actuation framework of the exosuit in the future. The sensor-to-joint-space mapping is based on multivariate multiple regression and derived using an Artificial Neural Network. Evaluation of the derived mapping achieved root mean square error of <inline-formula><tex-math notation="LaTeX">$\approx\text{5.43}^{\circ }$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\approx\text{3.65}^{\circ }$</tex-math></inline-formula> for the azimuth and elevation joint angles measured over 29,500 frames (4+ minutes) of motion-capture data.
Evaluating Semantic Representations of Source Code

Learned representations of source code enable various software developer tools, e.g., to detect bugs or to predict program properties. At the core of code representations often are word embeddings of identifier names in source code, because identifiers account for the majority of source code vocabulary and convey important semantic information. Unfortunately, there currently is no generally accepted way of evaluating the quality of word embeddings of identifiers, and current evaluations are biased toward specific downstream tasks. This paper presents IdBench, the first benchmark for evaluating to what extent word embeddings of identifiers represent semantic relatedness and similarity. The benchmark is based on thousands of ratings gathered by surveying 500 software developers. We use IdBench to evaluate state-of-the-art embedding techniques proposed for natural language, an embedding technique specifically designed for source code, and lexical string distance functions, as these are often used in current developer tools. Our results show that the effectiveness of embeddings varies significantly across different embedding techniques and that the best available embeddings successfully represent semantic relatedness. On the downside, no existing embedding provides a satisfactory representation of semantic similarities, e.g., because embeddings consider identifiers with opposing meanings as similar, which may lead to fatal mistakes in downstream developer tools. IdBench provides a gold standard to guide the development of novel embeddings that address the current limitations.
KiDS+VIKING-450 and DES-Y1 combined: Mitigating baryon feedback uncertainty with COSEBIs

We present cosmological constraints from a joint cosmic shear analysis of the Kilo-Degree Survey (KV450) and the Dark Energy Survey (DES-Y1), which were conducted using Complete Orthogonal Sets of E/B-Integrals (COSEBIs). With COSEBIs, we isolated any B-modes that have a non-cosmic shear origin and demonstrate the robustness of our cosmological E-mode analysis as no significant B-modes were detected. We highlight how COSEBIs are fairly insensitive to the amplitude of the non-linear matter power spectrum at high k-scales, mitigating the uncertain impact of baryon feedback in our analysis. COSEBIs, therefore, allowed us to utilise additional small-scale information, improving the DES-Y1 joint constraints on S8 = σ8(Ωm/0.3)0.5 and Ωm by 20%. By adopting a flat ΛCDM model we find S8 = 0.755−0.021+0.019, which is in 3. 2σ tension with the Planck Legacy analysis of the cosmic microwave background.
Aff-Wild Database and AffWildNet

In the context of HCI, building an automatic system to recognize affect of human facial expression in real-world condition is very crucial to make machine interact naturallisticaly with a man. However, existing facial emotion databases usually contain expression in the limited scenario under well-controlled condition. Aff-Wild is currently the largest database consisting of spontaneous facial expression in the wild annotated with valence and arousal. The first contribution of this project is the completion of extending Aff-Wild database which is fulfilled by collecting videos from YouTube on which the videos have spontaneous facial expressions in the wild, annotating videos with valence and arousal ranging in [-1,1], detecting faces in frames using FFLD2 detector and partitioning the whole data set into train, validate and test set, with 527056, 94223 and 135145 frames. The diversity is guaranteed regarding age, ethnicity and values of valence and arousal. The ratio of male to female is close to 1. Regarding the techniques used to build the automatic system, deep learning is outstanding since almost all winning methods in emotion challenges adopt DNN techniques. The second contribution of this project is that an end-to-end DNN is constructed to have joint CNN and RNN block and gives the estimation on valence and arousal for each frame in sequential data. VGGFace, ResNet, DenseNet with the corresponding pre-trained model for CNN block and LSTM, GRU, IndRNN, Attention mechanism for RNN block are experimented aiming to find the best combination. Fine tuning and transfer learning techniques are also tried out. By comparing the CCC evaluation value on test data, the best model is found to be pre-trained VGGFace connected with 2 layers GRU with attention mechanism. The models test performance is 0.555 CCC for valence with sequence length 80 and 0.499 CCC for arousal with sequence length 70.
Global well-posedness for the non-linear Maxwell-Schrödinger system

In this paper we study the Cauchy problem associated to the Maxwell-Schrodinger system with a defocusing pure-power non-linearity. This system has many applications in physics, for instance in the description of a charged non-relativistic quantum plasma, interacting with its self-generated electro-magnetic potential. We show the global well-posedness at high regularity for the sub-cubic case, and we provide polynomial bounds for the growth of the Sobolev norm of the solutions, for a certain range of non-linearities. The main tools are suitable a priori estimates, obtained by means of Koch-Tzvetkov type bounds for the non-homogeneous Schrodinger equation, which overcome the lack of Strichartz estimates for the magnetic-Schrodinger flow. Then we use a classical argument from the Kato school involving modified energies, which combined with the a priori estimates allows us to control the non-linearity globally in time. As a byproduct of our analysis, we show that the Lorentz force associated to the electro-magnetic field is well defined for solutions slightly more regular than being finite energy. This aspect is of fundamental importance since all the related physical models require the observability of electro-magnetic effects. The well posedness of the Lorentz force still appears to be an open problem in the case of solutions of finite energy only.
Remote Control: A Simple Deadlock Avoidance Scheme for Modular Systems-on-Chip

Ever increasing performance demand and shrinking in the transistor size together result in complex and dense packing in large chips. That motivates designers to opt for many small specialized hardware modules in a chip to extract maximum performance benefits with relatively lower complexity and cost. These altogether opens up new directions for heterogeneous modular System-on-Chip (SoC) research, where a large system is built by assembling small independently designed chiplets (small chips). We focus on the communication aspect of such SoCs, especially the newly observed deadlock among chiplets. Even though deadlock is a classic problem in networks and many solutions are available, the modular SoC design demands customized solutions that preserves the design flexibility for chiplet designers. We propose Remote Control (RC), a simple routing oblivious deadlock avoidance scheme based on selective injection-control mechanism. Along with guarantee on deadlock freedom, RC aims to provide a methodology to make each independently designed chiplet seamlessly integrate in any modular SoCs. We achieve up to 56.34% throughput and 15.49% zero load latency improvements on synthetic traffic and up to 20% speedup on real workloads taken from vast range of benchmark suites, over the state-of-the-art turn restriction based technique applied in the modular SoC domain.
Arbitrarily long gaps between the values of positive‐definite cubic and biquadratic diagonal forms

For s=3,4 , we prove the existence of arbitrarily long sequences of consecutive integers none of which is a sum of s nonnegative s th powers. More generally, we study the existence of gaps between the values ⩽N of diagonal forms of degree s in s variables with positive integer coefficients. We find: (1) gaps of size ≫logN(loglogN)2 when s=3 ; (2) gaps of size ≫logloglogNloglogloglogN if s=4 and the form, up to permutation of the variables, is not equal to a(c1x1)4+b(c2x2)4+4a(c3x3)4+4b(c4x4)4 .
R-SQAIR: Relational Sequential Attend, Infer, Repeat

Traditional sequential multi-object attention models rely on a recurrent mechanism to infer object relations. We propose a relational extension (R-SQAIR) of one such attention model (SQAIR) by endowing it with a module with strong relational inductive bias that computes in parallel pairwise interactions between inferred objects. Two recently proposed relational modules are studied on tasks of unsupervised learning from videos. We demonstrate gains over sequential relational mechanisms, also in terms of combinatorial generalization.
Dependence of Light Curves on Phase Angle and Asteroid Shape

We investigate the phase angle dependence of asteroid light curves using numerical scattering models applied to simple body shapes. For simplicity, the Kaasalainen scattering parameters are obtained from the corresponding Hapke scattering laws for C-type, S-type, and V-type asteroids. The spectral types differ substantially in the role of multiple scattering (which is largely a function of their geometric albedos) but we find that the differences on the light curve versus phase relations are modest. Using a Kaasalainen scattering law, the amplitudes and axis ratios with respect to different phase angles from 0° to 140° are plotted for these types of asteroids based on a biaxial ellipsoid shape model. Additionally, we examine the relationship between amplitude and the axis ratio for a contact binary represented by identical biaxial ellipsoids, including the effects of shadowing of one component by the other. We compare the models with published high phase angle observations, and with interstellar object 1I/‘Oumuamua (Δm = 2.5 mag at a phase angle of α = 23°), finding an axis ratio of 5.2: 1 if represented as a single ellipsoid and 3.5: 1 for each component if represented as a nose-to-nose contact binary. While a detailed fit is not attempted, the comparison shows that the single ellipsoid model is better.
Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace

Affective computing has been largely limited in terms of available data resources. The need to collect and annotate diverse in-the-wild datasets has become apparent with the rise of deep learning models, as the default approach to address any computer vision task. Some in-the-wild databases have been recently proposed. However: i) their size is small, ii) they are not audiovisual, iii) only a small part is manually annotated, iv) they contain a small number of subjects, or v) they are not annotated for all main behavior tasks (valence-arousal estimation, action unit detection and basic expression classification). To address these, we substantially extend the largest available in-the-wild database (Aff-Wild) to study continuous emotions such as valence and arousal. Furthermore, we annotate parts of the database with basic expressions and action units. As a consequence, for the first time, this allows the joint study of all three types of behavior states. We call this database Aff-Wild2. We conduct extensive experiments with CNN and CNN-RNN architectures that use visual and audio modalities; these networks are trained on Aff-Wild2 and their performance is then evaluated on 10 publicly available emotion databases. We show that the networks achieve state-of-the-art performance for the emotion recognition tasks. Additionally, we adapt the ArcFace loss function in the emotion recognition context and use it for training two new networks on Aff-Wild2 and then re-train them in a variety of diverse expression recognition databases. The networks are shown to improve the existing state-of-the-art. The database, emotion recognition models and source code are available at this http URL.