Miracle-dz/UniFAR-FTUs huggingface dataset:
Point-cloud multi-contact planning for humanoids: Preliminary results

We present preliminary results in porting our multi-contact non-gaited motion planning framework to operate in real environments where the surroundings are acquired using an embedded camera together with a depth map sensor. We consider the robot to have no a priori knowledge of the environment, and propose a scheme to extract the information relevant for planning from an acquired point cloud. This yield the basis of an egocentric on-the-fly multi-contact planner. We then demonstrate its capacity with two simulation scenarios involving an HRP-2 robot in various environment before discussing some issues to be addressed in our quest to achieve a close loop between planning and execution in an environment explored through embedded sensors.
Networks and the Size of the Gender Gap in Politician Performance Across Job Duties∗

The share of women in legislatures has increased dramatically in the past decade. Yet female politicians continue to face barriers that undermine their performance relative to men. We argue that those barriers have different implications across job duties, which can result in performance gender gaps of different magnitudes across duties. In particular, where female politicians are excluded in politician networks, duties requiring interaction with fellow politicians (e.g., legislative activities) may exhibit larger gender gaps as compared to duties (e.g., constituency services) that can be undertaken independently. We find support for this argument when comparing women and men politicians’ performance across 50 subnational Ugandan legislatures (where 1/3 of seats are reserved for women). Using original network data, we find that women are significantly more peripheral in professional networks, and that this network peripherality drives gender gaps in duties requiring more interaction with fellow politicians, but not independently-performed duties.
Experimental characterization of single droplets size and composition in liquid- liquid extraction systems with rainbow diffractometry

This paper is focused on the analysis of the rainbow phenomenon produced by droplets with a low relative refractive index with the aim to characterize their size distribution and composition in liquid-liquid two-phase flows. The Lorenz-Mie theory, geometrical optics and physical approximations are used to bring insight on the specificities of the corresponding scattering patterns : near-forward location, reverse polarization and lower modulation compared to conventional rainbows. Material and methods to retrieve the parameters of the dispersed phase are reported when the rainbow diffractometry technique is operated as a single particle counting technique. Experimental results show that an accuracy better than 5% on the diameters and 4.10-4 on the refractive index (i.e. 0.8% in mass fraction of Marcol in hydrogenated tetrapropylene) can be achieved. These results open up perspectives for liquid–liquid extraction studies and more generally, mixing studies in two-phase flows.
Quantifying Adaptability of Building Operations

The proliferation of building automation systems, smart and predictive control strategies can improve buildings’ adaptability to occupancy variations. However, no holistic metrics currently exist to evaluate these technologies nor to quantify this aspect of building performance. To this end, we present a technology-neutral approach to define adaptability as a building performance attribute and propose metrics to quantify it. To demonstrate this approach, a case-study based on simulating a single-story office was used to compare several control strategies with regards to their effect on improving adaptability, especially under variable occupancy scenarios. The proposed metrics enable designers and operators to quickly evaluate the effect of different control strategies and design decisions on building adaptability.
Speed-Optimized Simulation Models for Rapid Performance Evaluation of Heating and Energy Management Systems

State-of-the-art building simulation programs require detailed user input and long simulation times to capture the dynamic behavior of the building envelope and its integrated energy systems. A simplistic model will often be chosen for its speed but at a cost of reduced accuracy. This study uses Support Vector Regression to generate a trained model that captures the complexity of system models created in Modelica, but yields faster results. This work is unique because it attempts to capture the non-linear dynamics that occur due to feedback loops present in a sys-tem with multiple technologies and controllers. In this paper the results of three case studies using single zone buildings are presented. An annual heating proﬁle at 15 min resolution, was predicted 13x faster than the detailed simulation, with an RMSE of 2.5kW and overall error of 0.12%; however the performance of the trained model relied on the quality and relevance of the input data. This paper communicates the process and considerations of using Support Vector Regression to predict the performance proﬁle of a building using a model trained on the results of detailed simulation.
Modeling Long-Distance Dependencies with Second-Order LSTMs

A second-order LSTM is a modiﬁed LSTM architecture that conditionally selects one of multiple weight matrices to apply to each input, based on that input. Existing research on second-order RNNs focuses on character-level language modeling. Our task is to develop a second-order LSTM for word-level language models. Speciﬁcally, we test the hypothesis that second-order LSTM architectures have the improved ability (relative to a simple LSTM) to learn long distance dependencies within an input. We ﬁnd that, although second-order models don’t exhibit quantitative improvement, they can be trained in fewer epochs and are able to recover from unexpected inputs more quickly
High-Density Electromyography Based Control of Robotic Devices: On the Execution of Dexterous Manipulation Tasks

Electromyography (EMG) based interfaces have been used in various robotics studies ranging from teleoperation and telemanipulation applications to the EMG based control of prosthetic, assistive, or robotic rehabilitation devices. But most of these studies have focused on the decoding of user’s motion or on the control of the robotic devices in the execution of simple tasks (e.g., grasping tasks). In this work, we present a learning scheme that employs High Density Electromyography (HD-EMG) sensors to decode a set of dexterous, in-hand manipulation motions (in the object space) based on the myoelectric activations of human forearm and hand muscles. To do that, the subjects were asked to perform roll, pitch, and yaw motions manipulating two different cubes. The first cube was designed to have a center of mass coinciding with the geometric center of the cube, while for the second cube the center of mass was shifted 14 mm to the right (off-centered design). Regarding the acquisition of the myoelectric data, custom HD-EMG electrode arrays were designed and fabricated. Using these arrays, a total of 89 EMG signals were extracted. The object motion decoding was formulated as a regression problem using the Random Forests (RF) technique and the muscle importances were studied using the inherent feature variables importance calculation procedure of the RF. The muscle importance results show that different subjects use different strategies to execute the same motions on same object when the weight is off-centered. Finally, the decoded motions were used to control a five fingered robotic hand in a proof-of-concept application.
Robustness of Building Design Integrating Phase Change Materials in Nordic and Mediterranean Climates

The aim of the research presented in this paper is to evaluate, through a simulation study, the “robustness”, of the use of Phase Change Materials (PCMs) in the building construction, when boundary conditions that differ from the ones used in the design/planning phase appear. The methodology is based on the extensive use of the simulation environment EnergyPlus, applied to a case study building (a small-medium office building), located in two different climates (a Nordic climate and a Mediterranean climate), and through a comparative analysis (one case with PCM incorporated in the envelope and indoor partitions, and one reference case without integrated PCM). Different boundary conditions are parametrically studied, and the impact of these variations on the total energy use (for heating and cooling) of the building assessed. The results show that the use of PCM leads to a more stable energy use than the reference case (i.e. the deviation from the estimated energy use in case of no variation of the boundary conditions), in almost all the cases, and regardless of the climate. This technology can therefore represent a “safe” and “robust” solution in terms of expected performance, even if its behaviour is not always characterised by linear behaviours.
Development of transposon based tools for the investigation of virulence factors in Clostridium difficile

C. difficile is GI pathogen which under certain conditions colonises the colon leading CDI. During passage through the GI tract the bacterium in either vegetative or spore form encounters innate antimicrobial defence mechanisms evolved to prevent colonisation by microorganisms. One of these natural defences is the production of a potent digestive secretion known as bile. The major role of bile is to emulsify fats from ingested material to aid digestion. However, it is also an effective antimicrobial. The tolerance of C. difficile and its interaction with bile has not yet been fully investigated. The results obtained here describe the tolerance levels of two pathogenic strains, 630△erm and R20291, to bovine and ovine bile. Significant differences were seen in the tolerance of R20291 compared to 630△erm, with R20291 showing tolerance of a much higher concentration of bile. Analysis of genome-wide transcription levels using a microarray revealed differences in the expression of genes in 630△erm in response to bile exposure, although these were not statistically significant. These preliminary investigations do however provide a foundation for further elucidation of the interaction of C. difficile with bile.
The interrelationships of nutrition and feather pecking in the domestic fowl – A review Ernährung und Federpicken beim Geflügel – Eine Literaturstudie

Feather pecking and cannibalism are generally considered to be caused by a complex of genetical and environmental factors. Among the environmental factors nutrition and feeding behaviour play a prominent role. Nutritionists and ethologists approach the problem of feather pecking in different ways. While nutritionists assume that feather pecking is caused by nutrient deficiencies or imbalances, ethologists search the primary causes of feather pecking in fundamental drives related to feed intake. There are numerous studies which showed the influence of raw materials, crude protein or single nutrients, such as amino acids and minerals on feather pecking. The first nutritional experiments have been reported as of the 1930th. During the last years there was an increasing interest in the effect of fibre and feathers on feather pecking and feed-related behaviours. While most experiments have been carried out with layer-type chickens, the problem of cannibalism exists in meat-type chickens, turkeys and other domestic poultry species, such as quail, muscovy ducks and ostriches. This paper reviews the main publications on nutrition and behavioural aspects related to nutritional aspects and feeding behaviour in fowls.
Building a Persistent Workforce on Mechanical Turk for Multilingual Data Collection

Traditional methods of collecting translation and paraphrase data are prohibitively expensive, making the construction of large, new corpora difficult. While crowdsourcing offers a cheap alternative, quality control and scalability can become problematic. We discuss a novel annotation task that uses videos as the stimulus which discourages cheating. In addition, our approach requires only monolingual speakers, thus making it easier to scale since more workers are qualified to contribute. Finally, we employ a multi-tiered payment system that helps retain good workers over the long-term, resulting in a persistent, high-quality workforce. We present the results of one of the largest linguistic data collection efforts to date using Mechanical Turk, yielding 85K English sentences and more than 1k sentences for each of a dozen more languages.
Word Error Rate Estimation for Speech Recognition: e-WER

Measuring the performance of automatic speech recognition (ASR) systems requires manually transcribed data in order to compute the word error rate (WER), which is often time-consuming and expensive. In this paper, we propose a novel approach to estimate WER, or e-WER, which does not require a gold-standard transcription of the test set. Our e-WER framework uses a comprehensive set of features: ASR recognised text, character recognition results to complement recognition output, and internal decoder features. We report results for the two features; black-box and glass-box using unseen 24 Arabic broadcast programs. Our system achieves 16.9% WER root mean squared error (RMSE) across 1,400 sentences. The estimated overall WER e-WER was 25.3% for the three hours test set, while the actual WER was 28.5%.
LEADERSHIP STYLES PRACTISED BY FEMALES IN LEADERSHIP POSITIONS IN THE UNIVERSITY OF CAPE COAST

The study investigated the coping strategies of female leaders in tertiary education administration particularly in the University of Cape Coast. A qualitative perspective was adopted in conducting the study. Out of the various approaches to qualitative studies, I chose to adopt the case study approach for this research. Case study, as argued by Cohen, Manion and Morrison (2007), aims at studying a single instance in action. It provides a unique example of real people in real situations in order for people to understand ideas more clearly than simply presenting them with abstract theories or principles. I adopted this particular approach for my study in order to have an in-depth knowledge about females in leadership. I examined factors such as the leadership styles of females, gender stereotyping in Ghanaian culture, gender-based leadership, females in leadership positions, challenges facing women in leadership position, and leadership strategies used by women leaders. This approach allowed me to obtain in-depth information concerning the current status of women leadership in University of Cape Coast. Ten female leaders participated in the study and in-depth interviews were conducted with the participants at separate times at the participants’ convenient times. An introductory letter was obtained from the Institute for Educational Planning and Administration (IEPA) which encouraged the participants to grant me interviews. One month was used to collect the data, and another one and half months for data transcribing, analysis and report writing. The participants revealed that they employed opened-door leadership strategies more than autocratic style of leading their colleagues and subordinates. It is recommended that the University Management should initiate an educative programme that will help females in leadership positions to cope with challenges relating to the combination of family duties with their leadership responsibilities.
Morphometric analysis of European clam Ruditapes decussatus in Morocco

A morphometric analysis was performed in the European clam Ruditapes decussatus from six localities on the Moroccan coast. 240 individuals (40 individuals per site) were collected from Nador (2°86W 35°17’N), Sidi Moussa (8°74’W 32°98’N), Moulay Bouselham (6°27’W 34°85’N) and Oualidia (9°03’W32°73N) lagoon, Bouregreg (6°50’W 34°N) estuarine and Boutalha (15°48W 23°50N) (Dakhela bay). The comparison of variance of four morphometric index (elongation index (height/lenght), compactness index (width/length), convexity index (width/height) and weight linear ratio (weight/length) using Fisher test revealed a significant morphometric difference between the six populations of R. decussatus (p<0.05). The principal component analysis and the hierarchical clustering revealed three population groups, suggesting that each group has a defined morphometric characteristic. The generalized additive models (GAMs) showed a significant influence of SST (sea surface temperature) and Chl-a (Chlorophyll a) on the four-morphometric index. The observed morphological variations are likely the consequences of the environmental and ecological conditions.
Synthesis, Vibrational Spectroscopy and Non-Isothermal Kinetic Decomposition of Fe(H2PO4)2#2H2O ก%:(<1%)+<%=&>?4 5@@4?4A?0B*1?&.=?6C?) Fe(H2PO4)2#2H2O

Fe(H2PO4)2 2H2O nanoparticle was synthesized by a simple and cost-effective method using iron metal and phosphoric acid as starting chemicals. The thermal transformation products from the synthesized Fe(H2PO4)2 2H2O according to the thermal treatment at 150 and 400 oC were found to be FeH2P2O7 and Fe2P4O12, respectively. The activation energies of decomposition reaction of Fe(H2PO4)2 2H2O were calculated through the isoconversional methods of Ozawa and Kissinger-Akahira-Sunose (KAS), and the possible conversion functions have been estimated through Coats-Redfern method. The synthesized Fe(H2PO4)2 2H2O and its thermal transformation products were characterized by scanning electron microscopy (SEM), X-ray powder diffraction (XRD), and FTIR spectroscopy. Room temperature magnetization results showed a ferromagnetic behavior of the Fe(H2PO4)2 2H2O and its thermal transformation products, having the hysteresis loops in the range of -10000 Oe < H < +10000 Oe with the strong specific magnetization values in the range of 32.2596. 28 emu/g at 10 kOe.
Virtual and Augmented Reality in Mental Health Treatment

Medical and technological organizations have recently developed therapy and assistance solutions that venture beyond what is considered conventional for individuals with various mental health conditions and behavioral disorders such as autism, Down syndrome, Alzheimer’s disease, anxiety disorders, phobias, and learning difficulties. Through the use of virtual and augmented reality, researchers are working to provide alternative therapy methods to treat these conditions, while studying the long-term effects the treatment has on patients.
Time-resolved description of a flame front propagation toward an inclined wall-The effect of local stretch on flame speed

Time-resolved PIV was used to analyse the propagation of a laminar flame front toward a wall in laminar flow conditions. Forced instabilities were applied to the flame so simple working conditions of Darrieus-Landau instabilities could be simulated in flames interacting with the wall in limited regions making possible to be captured time dependent processes that occur randomly in the wall. High speed PIV was used to acquire the flame propagation and all reactants velocities during the total process. Specific software was developed to determine flame speed in laboratory and flame coordinates. Combined with flow velocities at the same instant it was possible to separate flow transport effects in the determination of effective flame speed relative to reactants. Curvature and flow stretch for all instant positions acquired of the flame front are determined, being possible to identify various effects of the wall in the flame and direct influence of stretch on the flame. Results were analysed and show to be a direct influence of the wall on the flame behaviour. The change of flow patterns due to the wall are strongly affected and flame front is mainly influenced by it.
Robots and Labour: Implications for Inflation Dynamics

This summary investigates how robots affect several variables related to labour, and vice versa. In order to evaluate the causal relationship, we use quality-adjusted robot stock data and labour market data from 26 industries in 33 countries. According to the results obtained from three estimated models – cross-sectional regressions in line with previous studies, panel data regressions and structural panel VAR models – an increase in robot stocks results in higher labour productivity, but has only an ambiguous effect on total employment. Wage increases, but not significantly. Thus, to date, robots should not have exerted a significant influence on inflation dynamics. On the other hand, improvements in labour market conditions lead to significant decline in robot investment. An important lesson obtained in this summary is that when testing whether robots take jobs away from the human workforce, one must also consider the reverse causality from the labour market to robots. <br><br>Full Publication: <a href="https://ssrn.com/abstract=3575884">Inflation Dynamics in Asia and the Pacific</a>
Active Multitask Learning Using Both Latent and Supervised Shared Topics

Multitask learning (MTL) via a shared representation has been adopted to alleviate problems with sparsity of labeled data across diﬀerent learning tasks. Active learning, on the other hand, reduces the cost of labeling examples by making informative queries over an unlabeled pool of data. Therefore, a uniﬁcation of both of these approaches can potentially be useful in settings where labeled information is expensive to obtain but the learning tasks or domains have some common char-acteristics. This paper introduces two such models – Active Doubly Supervised Latent Dirichlet Allocation (Act-DSLDA) and its non-parametric variation (Act-NPDSLDA) that integrate MTL and active learning in the same framework. These models make use of both latent and supervised shared topics to accomplish multitask learning. Experimental results on both document and image classiﬁcation show that integrating MTL and active learning along with shared latent and supervised topics is superior to other methods which do not employ all of these components.
Statistical Script Learning with Recurrent Neural Networks

Statistical Scripts are probabilistic models of sequences of events. For example, a script model might encode the information that the event “Smith met with the President” should strongly predict the event “Smith spoke to the President. ” We present a number of results improving the state of the art of learning statistical scripts for inferring implicit events. First, we demonstrate that incorporating multiple arguments into events, yielding a more complex event representation than is used in previous work, helps to improve a co-occurrence-based script system’s predictive power. Second, we improve on these results with a Recurrent Neural Network script sequence model which uses a Long Short-Term Memory component. We evaluate in two ways: first, we evaluate systems’ ability to infer held-out events from documents (the “Narrative Cloze” evaluation); second, we evaluate novel event inferences by collecting human judgments. We propose a number of further extensions to this work. First, we propose a number of new probabilistic script models leveraging recent advances in Neural Network training. These include recurrent sequence models with different hidden unit structure and Convolutional Neural Network models. Second, we propose integrating more lexical and linguistic information into events. Third, we propose incorporating discourse relations between spans of text into event co-occurrence models, either as output by an off-the-shelf discourse parser or learned automatically. Finally, we propose investigating the interface between models of event co-occurrence and coreference resolution, in particular by integrating script information into general coreference systems.