Miracle-dz/UniFAR-FTUs huggingface dataset:
ON-TRAC’ systems for the IWSLT 2021 low-resource speech translation and multilingual speech translation shared tasks

This paper describes the ON-TRAC Consortium translation systems developed for two challenge tracks featured in the Evaluation Campaign of IWSLT 2021, low-resource speech translation and multilingual speech translation. The ON-TRAC Consortium is composed of researchers from three French academic laboratories and an industrial partner: LIA (Avignon Université), LIG (Université Grenoble Alpes), LIUM (Le Mans Université), and researchers from Airbus. A pipeline approach was explored for the low-resource speech translation task, using a hybrid HMM/TDNN automatic speech recognition system fed by wav2vec features, coupled to an NMT system. For the multilingual speech translation task, we investigated the us of a dual-decoder Transformer that jointly transcribes and translates an input speech. This model was trained in order to translate from multiple source languages to multiple target ones.
Investigating the Impact of Gender Representation in ASR Training Data: a Case Study on Librispeech

In this paper we question the impact of gender representation in training data on the performance of an end-to-end ASR system. We create an experiment based on the Librispeech corpus and build 3 different training corpora varying only the proportion of data produced by each gender category. We observe that if our system is overall robust to the gender balance or imbalance in training data, it is nonetheless dependant of the adequacy between the individuals present in the training and testing sets.
Second Order WinoBias (SoWinoBias) Test Set for Latent Gender Bias Detection in Coreference Resolution

We observe an instance of gender-induced bias in a downstream application, despite the absence of explicit gender words in the test cases. We provide a test set, SoWinoBias, for the purpose of measuring such latent gender bias in coreference resolution systems. We evaluate the performance of current debiasing methods on the SoWinoBias test set, especially in reference to the method’s design and altered embedding space properties. See https://github.com/hillary-dawkins/SoWinoBias.
Improving Policing with Natural Language Processing

This article explores the potential for Natural Language Processing (NLP) to enable a more effective, prevention focused and less confrontational policing model that has hitherto been too resource consuming to implement at scale. Problem-Oriented Policing (POP) is a potential replacement, at least in part, for traditional policing which adopts a reactive approach, relying heavily on the criminal justice system. By contrast, POP seeks to prevent crime by manipulating the underlying conditions that allow crimes to be committed. Identifying these underlying conditions requires a detailed understanding of crime events - tacit knowledge that is often held by police officers but which can be challenging to derive from structured police data. One potential source of insight exists in unstructured free text data commonly collected by police for the purposes of investigation or administration. Yet police agencies do not typically have the skills or resources to analyse these data at scale. In this article we argue that NLP offers the potential to unlock these unstructured data and by doing so allow police to implement more POP initiatives. However we caution that using NLP models without adequate knowledge may either allow or perpetuate bias within the data potentially leading to unfavourable outcomes.
Personalized Response Generation with Tensor Factorization

Personalized response generation is essential for more human-like conversations. However, how to model user personalization information with no explicit user persona descriptions or demographics still remains under-investigated. To tackle the data sparsity problem and the huge number of users, we utilize tensor factorization to model users’ personalization information with their posting histories. Specifically, we introduce the personalized response embedding for all question-user pairs and form them into a three-mode tensor, decomposed by Tucker decomposition. The personalized response embedding is fed to either the decoder of an LSTM-based Seq2Seq model or a transformer language model to help generate more personalized responses. To evaluate how personalized the generated responses are, we further propose a novel ranking-based metric called Per-Hits@k which measures how likely are the generated responses come from the corresponding users. Results on a large-scale conversation dataset show that our proposed tensor factorization based models generate more personalized and higher quality responses compared to baselines.
SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning

This paper introduces the SemEval-2021 shared task 4: Reading Comprehension of Abstract Meaning (ReCAM). This shared task is designed to help evaluate the ability of machines in representing and understanding abstract concepts.Given a passage and the corresponding question, a participating system is expected to choose the correct answer from five candidates of abstract concepts in cloze-style machine reading comprehension tasks. Based on two typical definitions of abstractness, i.e., the imperceptibility and nonspecificity, our task provides three subtasks to evaluate models’ ability in comprehending the two types of abstract meaning and the models’ generalizability. Specifically, Subtask 1 aims to evaluate how well a participating system models concepts that cannot be directly perceived in the physical world. Subtask 2 focuses on models’ ability in comprehending nonspecific concepts located high in a hypernym hierarchy given the context of a passage. Subtask 3 aims to provide some insights into models’ generalizability over the two types of abstractness. During the SemEval-2021 official evaluation period, we received 23 submissions to Subtask 1 and 28 to Subtask 2. The participating teams additionally made 29 submissions to Subtask 3. The leaderboard and competition website can be found at https://competitions.codalab. org/competitions/26153. The data and baseline code are available at https://github.com/boyuanzheng010/SemEval2021-Reading-Comprehension-of-Abstract-Meaning.
Comparison of actin- and microtubule-based motility systems for application in functional nanodevices

Over the last 25 years, extensive progress has been made in developing a range of nanotechnological applications where cytoskeletal filaments and molecular motors are key elements. This includes novel, highly miniaturized lab on a chip systems for biosensing, nanoseparation etc but also new materials and parallel computation devices for solving otherwise intractable mathematical problems. For such approaches, both actin-based and microtubule-based cytoskeletal systems have been used. However, in accordance with their different cellular functions, actin filaments and microtubules have different properties and interaction kinetics with molecular motors. Therefore, the two systems obviously exhibit different advantages and encounter different challenges when exploited for applications. Specifically, the achievable filament velocities, the capability to guide filaments along nanopatterned tracks and the capability to attach and transport cargo differ between actin- and microtubule-based systems. Our aim here is to systematically elucidate these differences to facilitate design of new devices and optimize future developments. We first review the cellular functions and the fundamental physical and biochemical properties of actin filaments and microtubules. In this context we also consider their interaction with molecular motors and other regulatory proteins that are of relevance for applications. We then relate these properties to the advantages and challenges associated with the use of each of the motor-filament systems for different tasks. Finally, fundamental properties are considered in relation to some of the most interesting future development paths e.g. in biosensing and biocomputation.
Temperature Dependency of Non-dispersive Infrared Carbon-dioxide Gas Sensor

NDIR CO 2 gas sensor was prototyped with ASIC implemented thermopile sensor which included temperature sensor and White-Cell structure in this paper. The temperature dependency of dual infrared sensors (CO 2 and reference IR sensors) has been characterized and their output voltage ratios according to the temperature and gas concentration were presented in this paper for achieving temperature compensation algorithm. The initial output voltages of NDIR CO 2 gas and reference IR sensors showed 3 rd order polynomial and linear output voltages according to the variation of ambient temperatures from 253 K to 333 K, respectively. The output voltages of temperature sensor presented a linear dependency according to the ambient temperature and could be described with . The characteristics of output voltage ratios could be modeled with five parameters which are dependent upon the ambient temperatures and gas concentration. The estimated CO 2 concentrations showed relatively high error below 300 ppm (maximum 572 % at 7 ppm CO 2 concentration), however, as the concentration increased from 500 ppm to 2,000 ppm, the overall estimated errors of CO 2 concentrations were less than ±10% in this research.
US residential heat pumps: the private economic potential and its emissions, health, and grid impacts

To explore electrification as a climate change mitigation strategy, we study US residential heat pump adoption, given the current US housing stock. Our research asks (a) how the costs and benefits of heat pump adoption evolve with increased penetration, (b) what rate of heat pump adoption is economic given today’s housing stock, electric grid, energy prices, and heat pump technology, and (c) what effect changing policies, innovations, and technology improvements might have on heat pump adoption. We answer these research questions by simulating the energy consumption of 400 representative single-family houses in each of 55 US cities both before and after heat pump adoption. We use energy prices, CO2 emissions, health damages from criteria air pollutants, and changes in peak electricity demand to quantify the costs and benefits of each house’s heat pump retrofit. The results show that 32% of US houses would benefit economically from installing a heat pump, and 70% of US houses could reduce emissions damages by installing a heat pump. We show that the potential for heat pump adoption varies depending on electric grid, climate, baseline heating fuel, and housing characteristics. Based on these results we identify strategic, technology, and policy insights to stimulate high heat pump adoption rates and deep electrification of the US residential heating sector, which reduces CO2 emissions and the impacts of climate change.
Water Removal from Ethanol Vapor by Adsorption on Canola Meal after Protein Extraction

In this paper, biosorbents based on canola meal obtained after protein extraction were developed to adsorb water from ethanol–water vapor mixture in a pressure swing adsorption (PSA) process. It was demonstrated that through the process over 99 wt % fuel grade ethanol was achieved from lower grade feeds containing 80–95 wt % ethanol at 90–110 °C and 136–243 kPa. The breakthrough curve technique was used to investigate the adsorption dynamics and equilibrium. The effects of temperature, pressure, vapor feed concentration, and adsorbent particle size on water and ethanol uptake and separation factor were examined. External water mass transfer coefficients were also calculated. The model based on Dubinin–Polanyi potential theory fit the water adsorption isotherms reasonably well. The mean free energy of water adsorption was calculated to be 0.04 kJ/mol, and heat of adsorption was −35.81 kJ/mol, which indicated the physical nature of the adsorption. Water saturated canola meal biosorbents were regenerated at .
On the Influence of Pore Size and Pore Loading on Structural and Dynamical Heterogeneities of an Ionic Liquid Confined in a Slit Nanopore

Molecular dynamics simulations were performed to investigate the structural and dynamical properties of varying amounts of the ionic liquid (IL) [EMIM+][TFMSI–] confined inside slit-like graphitic pores of different widths, H. The ions distributed in layers inside the slit pores, with the number of layers depending on pore size. A reduction in pore loading leads to the formation of regions of high and low density of ions in the center of the pore. Variations in pore size and pore loading seem to induce only slight changes in the local liquid structure of [EMIM+][TFMSI–] in the different layers, as compared with the liquid structure of the bulk IL. This finding, when combined with our previous work for a different IL (Singh, R.; Monk, J.; Hung, F. R. J. Phys. Chem. C2011, 115, 16544–16554), suggests that confinement inside slit-like nanopores may or may not induce changes in the local liquid structure depending on the specific IL. However, pore size and pore loading have a marked effect on the dynamics of .
On the Shape and Velocity of Single Air Bubbles Rising in Various Liquids

The shape and the velocity of bubbles in various liquids listed in Table 1 were measured on photographs taken through a stroboscope.The shape of gas bubbles changed from spherical to ellipsoidal, and then to mushroomlike, as the bubble size increased. The experimental results showed that up to the Reynolds number of 2M-0.23, the bubble was almost spherical and traveled in a rectilinear path. In the range of 2 16.5, the bubble was of mushroom shape. These changes in shape are plotted in terms of d/a and ReM0.23 in Fig. 12, from which experimental equations, Eqs. (12)(15) are derived. These are applicable to bubbles in all solutions except for surface active agent solution. The bubbles in surface active agent solution remained spherical in much bigger size than those in pure water.Data on the terminal velocity of gas bubbles are shown in Fig. 6. Of spherical and ellipsoidal bubbles, the surface tension and the viscosity were found to be important factors determining the rate of rise. Mushroomlike bubbles rose independently of liquid properties. To correlate the results, dimensionless parameters, CD, Re and M were employed and experimental equations, Eqs. (9)(11), were obtained for all solutions except for surface active agent solution. These equations agree approximately well with the data of previous investigators summarized in Fig. 16. The presence of certain surface active substances in water served to increase the drag of bubbles, as was made clear by the comparison with the drag of bubbles in pure water.
The Light Environment Within Grapevine Canopies. I. Description and Seasonal Changes During Fruit Development

Seasonal changes in the light environment within Cabernet Sauvignon ( Vitis vinifera L.) grapevine canopies was examined in eight commercial vineyards in the North Coast of California. Photosynthetic photon flux density (PPFD), the ratio of red (R) to far-red (FR) light (R:FR) and percentage of canopy area receiving sunflecks were determined along a vertical transect in the center of the vine row and along a horizontal transect in the fruit zone at berry set, veraison and harvest. Leaf area density (m 2 leaf area per meter canopy or row length) was also determined at each site at each of the above mentioned stages of fruit development. PPFD, R:FR and sunflecks decreased markedly beneath the canopy surface, and continued to decline along the vertical transect from the top of the canopy to the fruit zone. PPFD, R:FR and sunflecks reached their lowest values near the fruit zone, then increased from the fruit zone to the ground. A close, positive relationship between PPFD (log % ambient) and R:FR within the canopy was found. PPFD in the fruit zone at harvest ranged from 7.7 (Site 1, leaf area density = 2.2 m 2 m -1 canopy length) to 0.3% (Site 8, leaf area density = 12.2 m 2 m -1 canopy length) of ambient, while R:FR in the fruit zone at harvest ranged from 0.49 (Site 1) to 0.05 (Site 6, leaf area density = 11.3 m 2 m -1 canopy length). The canopy surface in the fruit zone illuminated by sunflecks at harvest ranged from 30% (Site 1) to nil (Sites 6 and 8). Despite a near doubling of leaf area between berry set and harvest at some sites, only minimal changes in canopy light environment were observed during fruit development. The results indicated that PPFD in the canopy interior was reduced to 1% or less of ambient, while R:FR and sunflecks were reduced to 10% or less of ambient, when leaf area exceeded 8 m 2 m -1 canopy length. In contrast, PPFD, R:FR and sunflecks were 10% or more of their ambient values when leaf area was ≤ 4 m 2 m -1 canopy length.
Azole Resistance in Clinical and Environmental Aspergillus Isolates from the French West Indies (Martinique)

The emergence of azole resistant Aspergillus spp. , especially Aspergillus fumigatus, has been described in several countries around the world with varying prevalence depending on the country. To our knowledge, azole resistance in Aspergillus spp. has not been reported in the West Indies yet. In this study, we investigated the antifungal susceptibility of clinical and environmental isolates of Aspergillus spp. from Martinique, and the potential resistance mechanisms associated with mutations in cyp51A gene. Overall, 208 Aspergillus isolates were recovered from clinical samples (n = 45) and environmental soil samples (n = 163). They were screened for resistance to azole drugs using selective culture media. The Minimum Inhibitory Concentrations (MIC) towards voriconazole, itraconazole, posaconazole and isavuconazole, as shown by the resistant isolates, were determined using the European Committee on Antimicrobial Susceptibility Testing (EUCAST) microdilution broth method. Eight isolates (A. fumigatus, n = 6 and A. terreus, n = 2) had high MIC for at least one azole drug. The sequencing of cyp51A gene revealed the mutations G54R and TR34/L98H in two A. fumigatus clinical isolates. Our study showed for the first time the presence of azole resistance in A. fumigatus and A. terreus isolates in the French West Indies.
Surface characterization of plasma treated polymers for applications as biocompatible carriers

The objective of this work was to determine surface properties of polymer surfaces after plasma treatment with the aim of further cytocompatibility tests. Examined polymers were poly(ethyleneterephthalate) (PET), high-density poly- ethylene (HDPE), poly(tetrafluoro-ethylene) (PTFE) and poly(L-lactic acid) (PLLA). Goniometry has shown that the plasma treatment was immediately followed by a sharp decrease of contact angle of the surface. In the course of ageing the contact angle increased due to the reorientation of polar groups into the surface layer of polymer. Ablation of polymer sur- faces was observed during the degradation. Decrease of weight of polymer samples was measured by gravimetry. Surface morphology and roughness was studied by atomic force microscopy (AFM). The PLLA samples exhibited saturation of wettability (aged surface) after approximately 100 hours, while the PET and PTFE achieved constant values of contact angle after 336 hours. Irradiation by plasma leads to polymer ablation, the highest mass loss being observed for PLLA. The changes in the surface roughness and morphology were observed, a lamellar structure being induced on PTFE. Selected polymer samples were seeded with VSMC (vascular smooth muscle cells) and the adhesion and proliferation of cells was studied. It was proved that certain combination of input treatment parameters led to improvement of polymer cytocompatibility. The plasma exposure was confirmed to significantly improve the PTFE biocompatibility.
Alkaloids from twigs and leaves of Picrasma quassioides

Objective To study alkaloids from the twigs and leaves of Picrasma quassioides. MethodsCompounds were isolated and purified by column chromatography over Sephadex LH-20 and silica gel column. Their chemical structures were elucidated on the basis of physicochemical properties and spectral data. Results Sixteen alkaloids were isolated, purified, and identified as: 5-methoxycanthin-6-one (Ⅰ), 11-hydroxycanthin-6-one (Ⅱ), canthin-6-one (Ⅲ), 4, 5-dimethoxycanthin-6-one (Ⅳ), 4-methoxy-5-hydroxycanthin-6-one (Ⅴ), 3-methylcanthin-2, 6-dione (Ⅵ), 1-formyl-4-methoxy-β-carboline (Ⅶ), 1-methoxy-β-carboline (Ⅷ), 1-ethyl-4, 8-dimethoxy-β-carboline (Ⅸ), 1-methoxycarbonyl-4-hydroxyl-β-carboline (Ⅹ), 1-methyl-4-methoxy-β-carboline (Ⅺ), 1-ethoxycarbonyl-β-carboline (ⅩⅡ), 1-formyl-β-carboline (ⅩⅢ), 1-methoxycarbonyl-β-carboline (ⅩⅣ), 1-ethyl-4-methoxy-β-carboline (ⅩⅤ), and 1, 2, 3, 4-tetrahydro-1, 3, 4-trioxo-β-carboline (ⅩⅥ). Conclusion Compound Ⅺ is separated from the natural plant for the first time and compounds Ⅱ, Ⅷ, and ⅩⅤ are separated from plants of Picrasma Bl. for the first time.
Semantic-aware Image Similarity Search

—This paper seeks improvement on image similarity search. In addition to support a query image as the only input parameter, we will also support additional query attributes as combined search criteria. These additional attributes include supplemental text attributes and image native attributes. Supplemental text attributes are image’s title, caption or category, as a natural target of NLP. Image native attributes are attributes embedded in the image itself such as color. Combining text and image attributes injects semantic meaning from NLP to an image, enables composite search and semantic arithmetic operation on image data. Our result shows 84% accuracy of image search and 79% accuracy of image arithmetic operation.
Signal Processing Transformations in Scene Recognition from Satellite Imagery

We propose that traditional signal processing transformations, namely the Fourier Transform and Wavelet Trans-form, extract meaningful information from visual data for the purpose of image recognition. We compare convolutional neural network performance on raw visual data to performance on data including channels of transformed data. We apply this technique to the task of recognizing different types of forest land as in the ForestNet paper published by the Stanford ML group. We compare training time, rate of convergence, and accuracy between data types over multiple models. We ﬁnd that using the Fourier transform can improve performance drastically for simple models and speeds up training time for complex models. For the Wavelet transform, we ﬁnd that there are moderate improvements on simple models, but there is only a slight decrease in performance for complex models.
Awareness of Intrinsic Motivation in Leadership

Introduction Graves and Luciano tested a model linking the satisfaction of psychological need, autonomous motivation, and attitudinal outcomes (2013). From this research, they postulate that high-quality leader-member exchange fosters satisfaction of employees’ needs, which in consequence enhances autonomous motivation and the level of performance. Reinforcing this argument, the findings of Deci et al should be considered, as they discovered that if employees have autonomous motivation this will foster a high level of performance with good quality as well as the wellness of employees (Deci et al., 2017). Businesses are constantly forced to enhance their productivity in order to be competitive on markets. Therefore, they are constantly trying to be more effective and efficient, e.g. by generating high performance through increased pressure on employees.