Miracle-dz/UniFAR-FTUs huggingface dataset:
Wage inequality in international perspective: Effects of location, sector, and gender differentiated

This paper uses the well-known Oaxaca-Blinder decomposition technique to understand the determinants of wage-gaps between men and women, between urban and rural workers, and between those employed in the rural agricultural versus the rural non-agricultural sectors, for the 14 developing and transition economies in the RIGA-L dataset. The unexplained male female wage gaps (i.e. the gaps that remain after controlling for a host of observable characteristics of the job and the worker) provide estimates of labor market discrimination against women that are consistent with prior estimates from other countries, and are generally similar in rural and urban areas. We argue that countries with large unexplained urban-rural gaps, such Tajikistan and Malawi, are those in which rural to urban migration is likely to persist even in face of high urban unemployment rates. Furthermore, we find that large unexplained wage gaps in favor of non-farm employment, versus paid labor in farming, exist in Tajikistan (53%), Ecuador (44%), Nepal (36%), Nicaragua (32%), and Nigeria (30%); these would then appear to be the countries for which a shift of existing workers, with their current attributes, from the farm to the non-farm sector would have the largest impact on rural incomes.
Application of proton radiography to medical imaging

The use of charged particles for radiographic applications has been considered for some time, but progress has been impeded by the cost and availability of suitable accelerators. However, recent developments in technology could overcome these problems. A review is presented of the physical principles leading to an improvement in mass resolution per unit of absorbed dose for charged particle radiography relative to x-ray radiography. The quantitative comparisons between x-ray and proton radiographs presented here confirm this advantage. The implications of proton radiography on cancer detection, as well as future plans for developing a proton tomographic system, are discussed.
Rostering ambulance services

This paper developed a model for rostering ambulance crew in order to maximise the coverage throughout a planning horizon and minimise the number of ambulance crew. Rostering Ambulance Services is a complex task, which considers a large number of conflicting rules related to various aspects such as limits on the number of consecutive work hours, the number of shifts worked by each ambulance staff and restrictions on the type of shifts assigned. The two-stage models are developed using nonlinear integer programming technique to determine the following sub-problems: the shift start times; the number of staff required to work for each shift; and a balanced schedule of ambulance staff. At the first stage, the first two sub-problems have been solved. At the second stage, the third sub-problem has been solved using the first stage outputs. Computational experiments with real data are conducted and the results of the models are presented.
CMOS Interfaces for Internet-of-Wearables Electrochemical Sensors: Trends and Challenges

Smart wearables, among immediate future IoT devices, are creating a huge and fast growing market that will encompass all of the next decade by merging the user with the Cloud in a easy and natural way. Biological fluids, such as sweat, tears, saliva and urine offer the possibility to access molecular-level dynamics of the body in a non-invasive way and in real time, disclosing a wide range of applications: from sports tracking to military enhancement, from healthcare to safety at work, from body hacking to augmented social interactions. The term Internet of Wearables (IoW) is coined here to describe IoT devices composed by flexible smart transducers conformed around the human body and able to communicate wirelessly. In addition the biochemical transducer, an IoW-ready sensor must include a paired electronic interface, which should implement specific stimulation/acquisition cycles while being extremely compact and drain power in the microwatts range. Development of an effective readout interface is a key element for the success of an IoW device and application. This review focuses on the latest efforts in the field of Complementary Metal–Oxide–Semiconductor (CMOS) interfaces for electrochemical sensors, and analyses them under the light of the challenges of the IoW: cost, portability, integrability and connectivity.
IMNTPU Dialogue System Evaluation at the NTCIR-16 DialEval-2 Dialogue Quality and Nugget Detection

A surge in interest in the evaluation of the quality of chatbot conversation has been observed in recent years. We performed Dialogue Quality (DQ) and Nugget Detection (ND) subtasks in Chinese and English. However, the majority of existing conventional approaches are based on the long short-term memory (LSTM) model. The paper suggests a method for assisting customers in resolving problems. This subtask aims to automatically determine the status of dialogue sentences in a dialogue system’s logs. In conversation tasks, we developed fine-tuning methodologies for the transformer model. To evaluate and show the concept, we created a wide framework for testing and displaying the XLM-RoBERTa model’s performance on conversational texts. Finally, the experimental findings of the two subtasks demonstrated the efficacy of our strategy. The experimental findings for the DialEval-2 task showed that the suggested method’s performance is reasonably equal to that of the LSTM-based baseline model. The main contribution of our study is our suggestion of two crucial elements, namely, tokenization methods and fine-tuning procedures, to increase the conversation quality and nugget identification subtasks in dialogue assessment.
Advanced wavefront sensing and control demonstration with MagAO-X

The search for exoplanets is pushing adaptive optics systems on ground-based telescopes to their limits. Currently, we are limited by two sources of noise: the temporal control error and non-common path aberrations. First, the temporal control error of the AO system leads to a strong residual halo. This halo can be reduced by applying predictive control. We will show and described the performance of predictive control with the 2K BMC DM in MagAO-X. After reducing the temporal control error, we can target non-common path wavefront aberrations. During the past year, we have developed a new model-free focal-plane wavefront control technique that can reach deep contrast (<1e-7 at 5 λ/D) on MagAO-X. We will describe the performance and discuss the on-sky implementation details and how this will push MagAO-X towards imaging planets in reflected light. The new data-driven predictive controller and the focal plane wavefront controller will be tested on-sky in April 2022.
MoCapAct: A Multi-Task Dataset for Simulated Humanoid Control

Simulated humanoids are an appealing research domain due to their physical capabilities. Nonetheless, they are also challenging to control, as a policy must drive an unstable, discontinuous, and high-dimensional physical system. One widely studied approach is to utilize motion capture (MoCap) data to teach the humanoid agent low-level skills (e.g., standing, walking, and running) that can then be re-used to synthesize high-level behaviors. However, even with MoCap data, controlling simulated humanoids remains very hard, as MoCap data offers only kinematic information. Finding physical control inputs to realize the demonstrated motions requires computationally intensive methods like reinforcement learning. Thus, despite the publicly available MoCap data, its utility has been limited to institutions with large-scale compute. In this work, we dramatically lower the barrier for productive research on this topic by training and releasing high-quality agents that can track over three hours of MoCap data for a simulated humanoid in the dm_control physics-based environment. We release MoCapAct (Motion Capture with Actions), a dataset of these expert agents and their rollouts, which contain proprioceptive observations and actions. We demonstrate the utility of MoCapAct by using it to train a single hierarchical policy capable of tracking the entire MoCap dataset within dm_control and show the learned low-level component can be re-used to efficiently learn downstream high-level tasks. Finally, we use MoCapAct to train an autoregressive GPT model and show that it can control a simulated humanoid to perform natural motion completion given a motion prompt. Videos of the results and links to the code and dataset are available at https://microsoft.github. io/MoCapAct.
Benchmarking Validation Methods for Unsupervised Domain Adaptation

This paper compares and ranks 11 UDA validation methods. Validators estimate model accuracy, which makes them an essential component of any UDA train-test pipeline. We rank these validators to indicate which of them are most useful for the purpose of selecting optimal models, checkpoints, and hyperparameters. In addition, we propose and compare new effective validators and signiﬁcantly improved versions of existing validators. To the best of our knowledge, this large-scale benchmark study is the ﬁrst of its kind in the UDA ﬁeld.
SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Sampling

Reconstructing a 3D shape based on a single sketch image is challenging due to the large domain gap between a sparse, irregular sketch and a regular, dense 3D shape. Existing works try to employ the global feature extracted from sketch to directly predict the 3D coordinates, but they usually suffer from losing fine details that are not faithful to the input sketch. Through analyzing the 3D-to-2D projection process, we notice that the density map that characterizes the distribution of 2D point clouds (i.e., the probability of points projected at each location of the projection plane) can be used as a proxy to facilitate the reconstruction process. To this end, we first translate a sketch via an image translation network to a more informative 2D representation that can be used to generate a density map. Next, a 3D point cloud is reconstructed via a two-stage probabilistic sampling process: first recovering the 2D points (i.e., the x and y coordinates) by sampling the density map; and then predicting the depth (i.e., the z coordinate) by sampling the depth values at the ray determined by each 2D point. Extensive experiments are conducted, and both quantitative and qualitative results show that our proposed approach significantly outperforms other baseline methods.
BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer

A recent trend in binary code analysis promotes the use of neural solutions based on instruction embedding models. An instruction embedding model is a neural network that transforms sequences of assembly instructions into embedding vectors. If the embedding network is trained such that the translation from code to vectors partially preserves the semantic, the network effectively represents an assembly code model. In this paper we present BinBert, a novel assembly code model. BinBert is built on a transformer pre-trained on a huge dataset of both assembly instruction sequences and symbolic execution information. BinBert can be applied to assembly instructions sequences and it is fine-tunable, i.e. it can be re-trained as part of a neural architecture on task-specific data. Through fine-tuning, BinBert learns how to apply the general knowledge acquired with pre-training to the specific task. We evaluated BinBert on a multi-task benchmark that we specifically designed to test the understanding of assembly code. The benchmark is composed of several tasks, some taken from the literature, and a few novel tasks that we designed, with a mix of intrinsic and downstream tasks. Our results show that BinBert outperforms state-of-the-art models for binary instruction embedding, raising the bar for binary code understanding.
Attention mechanism based intelligent channel feedback for mmWave massive MIMO systems

The potential advantages of intelligent wireless communications with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) are based on the availability of instantaneous channel state information (CSI) at the base station (BS). However, no existence of channel reciprocity leads to the difficult acquisition of accurate CSI at the BS in frequency division duplex (FDD) systems. Many researchers explored effective architectures based on deep learning (DL) to solve this problem and proved the success of DL-based solutions. However, existing schemes focused on the acquisition of complete CSI while ignoring the beamforming and precoding operations. In this paper, we propose an intelligent channel feedback architecture using eigenmatrix and eigenvector feedback neural network (EMEVNet). With the help of the attention mechanism, the proposed EMEVNet can be considered as a dual channel auto-encoder, which is able to jointly encode the eigenmatrix and eigenvector into codewords. Simulation results show great performance improvement and robustness with extremely low overhead of the proposed EMEVNet method compared with the traditional DL-based CSI feedback methods.
IRL with Partial Observations using the Principle of Uncertain Maximum Entropy

The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible while constrained to match empirically estimated feature expectations. However, in many real-world applications that use noisy sensors computing the feature expectations may be challenging due to partial observation of the relevant model variables. For example, a robot performing apprenticeship learning may lose sight of the agent it is learning from due to environmental occlusion. We show that in generalizing the principle of maximum entropy to these types of scenarios we unavoidably introduce a dependency on the learned model to the empirical feature expectations. We introduce the principle of uncertain maximum entropy and present an expectation-maximization based solution generalized from the principle of latent maximum entropy. Finally, we experimentally demonstrate the improved robustness to noisy data offered by our technique in a maximum causal entropy inverse reinforcement learning domain.
Task Oriented Video Coding: A Survey

Video coding technology has been continuously improved for higher compression ratio with higher resolution. However, the state-of-the-art video coding standards, such as H.265/HEVC and Versatile Video Coding, are still designed with the assumption the compressed video will be watched by humans. With the tremendous advance and maturation of deep neural networks in solving computer vision tasks, more and more videos are directly analyzed by deep neural networks without humans' involvement. Such a conventional design for video coding standard is not optimal when the compressed video is used by computer vision applications. While the human visual system is consistently sensitive to the content with high contrast, the impact of pixels on computer vision algorithms is driven by specific computer vision tasks. In this paper, we explore and summarize recent progress on computer vision task oriented video coding and emerging video coding standard, Video Coding for Machines.
A Unified Causal View of Domain Invariant Representation Learning

Machine learning methods can be unreliable when deployed in domains that differ from the domains on which they were trained. To address this, we may wish to learn representations of data that are domain-invariant in the sense that we preserve data structure that is stable across domains, but throw out spuriously-varying parts. There are many representation-learning approaches of this type, including methods based on data augmentation, distributional invariances, and risk invariance. Unfortunately, when faced with any particular real-world domain shift, it is unclear which, if any, of these methods might be expected to work. The purpose of this paper is to show how the different methods relate to each other, and clarify the real-world circumstances under which each is expected to succeed. The key tool is a new notion of domain shift relying on the idea that causal relationships are invariant, but non-causal relationships (e.g., due to confounding) may vary.
Heterogeneity extends criticality

Criticality has been proposed as a mechanism for the emergence of complexity, life, and computation, as it exhibits a balance between order and chaos. In classic models of complex systems where structure and dynamics are considered homogeneous, criticality is restricted to phase transitions, leading either to robust (ordered) or fragile (chaotic) phases for most of the parameter space. Many real-world complex systems, however, are not homogeneous. Some elements change in time faster than others, with slower elements (usually the most relevant) providing robustness, and faster ones being adaptive. Structural patterns of connectivity are also typically heterogeneous, characterized by few elements with many interactions and most elements with only a few. Here we take a few traditionally homogeneous dynamical models and explore their heterogeneous versions, finding evidence that heterogeneity extends criticality. Thus, parameter fine-tuning is not necessary to reach a phase transition and obtain the benefits of (homogeneous) criticality. Simply adding heterogeneity can extend criticality, making the search/evolution of complex systems faster and more reliable. Our results add theoretical support for the ubiquitous presence of heterogeneity in physical, biological, social, and technological systems, as natural selection can exploit heterogeneity to evolve complexity “for free”. In artificial systems and biological design, heterogeneity may also be used to extend the parameter range that allows for criticality.
Single-Frame Attitude Determination Methods for Nanosatellites

Single-frame methods of determining the attitude of a nanosatellite are compared in this study. The methods selected for comparison are: Single Value Decomposition (SVD), q method, Quaternion ESTimator (QUEST), Fast Optimal Attitude Matrix (FOAM) − all solving optimally the Wahba’s problem, and the algebraic method using only two vector measurements. For proper comparison, two sensors are chosen for the vector observations on-board: magnetometer and Sun sensors. Covariance results obtained as a result of using those methods have a critical importance for a non-traditional attitude estimation approach; therefore, the variance calculations are also presented. The examined methods are compared with respect to their root mean square (RMS) error and variance results. Also, some recommendations are given.
Superconducting phase transition reveals electromagnetic coupling to a scalar field potential

Pressure-induced, spontaneous diamagnetism associated with critical behaviour is demonstrated experimentally in a polar dielectric fluid containing nanoscale, clathrate hydrate cage structures. As with Type II superconductivity, Abrikosov vortices come to penetrate the external diamagnetic field such that it reduces to zero for particular values of the magnet flux. The external magnetic field is thus revealed to be the order parameter that signifies a phase transition between Type II superconducting behaviour and a dual of Type I superconducting behaviour. This phase transition is described by a distinctive universality class of critical exponents. The Abrikosov vortices are interpreted as magnetic monopole defects associated with the non-equilibrium, geometrically frustrated system. Deconfinement of the fractionalized, magnetic monopole charges is consistent with bulk exclusion of the electric field and positive paramagnetism which correspond to a dual of Type I superconductivity. The magnitude of the spontaneous Type I response is consistent with exponential coupling of the spontaneous magnetism with an external scalar field potential made accessible through inertia and hyperbolic geometry. Under this interpretation, magnetic monopole defects act as inhomogeneous nucleation sites, able to expand or contract the volume of the system, in an analogue of cosmological inflation. The quantum vacuum origin of the scalar field is thereby harnessed to generate mechanical work, so representing a potentially unlimited source of zero-emissions energy.
Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis

Spatial statistical analysis of multivariate volumetric data can be challenging due to scale, complexity, and occlusion. Advances in topological segmentation, feature extraction, and statistical summarization have helped overcome the challenges. This work introduces a new spatial statistical decomposition method based on level sets, connected components, and a novel variation of the restricted centroidal Voronoi tessellation that is better suited for spatial statistical decomposition and parallel efficiency. The resulting data structures organize features into a coherent nested hierarchy to support flexible and efficient out-of-core region-of-interest extraction. Next, we provide an efficient parallel implementation. Finally, an interactive visualization system based on this approach is designed and then applied to turbulent combustion data. The combined approach enables an interactive spatial statistical analysis workflow for large-scale data with a top-down approach through multiple-levels-of-detail that links phase space statistics with spatial features.
Genetic algorithms for the resource-constrained project scheduling problem in aircraft heavy maintenance

Due to complex sets of interrelated activities in aircraft heavy maintenance (AHM), many airlines have to deal with substantial aircraft maintenance downtime. The scheduling problem in AHM is regarded as an NP-hard problem. Using exact algorithms can be time-consuming or even infeasible. This article proposes genetic algorithms for solving the resource-constrained project scheduling problem (RCPSP) in AHM. The objective of the study was to minimise the makespan of the maintenance plan. The proposed algorithms applied five heuristic dispatching rules to generate an initial population based on activity list formation. Resource allocation methods for RCPSP – earliest start time (EST) and workgroup and earliest start time (WEST) – were used to evaluate the fitness value. The elitist and roulette wheel methods were applied in the selection process. The sequences of the activity lists were then iteratively improved by crossover and mutation operations. The results show that the proposed algorithms perform efficiently compared to the existing solutions in terms of computational time and resource allocation.
UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV

—Unmanned Aerial Vehicle (UAV) has gained sig- niﬁcant traction in the recent years, particularly the context of surveillance. However, video datasets that capture violent and non-violent human activity from aerial point-of-view is scarce. To address this issue, we propose a novel, baseline simulator which is capable of generating sequences of photo-realistic synthetic images of crowds engaging in various activities that can be categorized as violent or non-violent. The crowd groups are annotated with bounding boxes that are automatically computed using semantic segmentation. Our simulator is capable of generating large, randomized urban environments and is able to maintain an average of 25 frames per second on a mid-range computer with 150 concurrent crowd agents interacting with each other. We also show that when synthetic data from the proposed simulator is augmented with real world data, binary video classiﬁcation accuracy is improved by 5% on average across two different models.