Miracle-dz/UniFAR-FTUs huggingface dataset:
CI-AVSR: A Cantonese Audio-Visual Speech Datasetfor In-car Command Recognition

With the rise of deep learning and intelligent vehicles, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities. In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. However, there is a data scarcity issue for low resource languages, hindering the development of research and applications. In this paper, we introduce a new dataset, Cantonese In-car Audio-Visual Speech Recognition (CI-AVSR), for in-car command recognition in the Cantonese language with both video and audio data. It consists of 4,984 samples (8.3 hours) of 200 in-car commands recorded by 30 native Cantonese speakers. Furthermore, we augment our dataset using common in-car background noises to simulate real environments, producing a dataset 10 times larger than the collected one. We provide detailed statistics of both the clean and the augmented versions of our dataset. Moreover, we implement two multimodal baselines to demonstrate the validity of CI-AVSR. Experiment results show that leveraging the visual signal improves the overall performance of the model. Although our best model can achieve a considerable quality on the clean test set, the speech recognition quality on the noisy data is still inferior and remains an extremely challenging task for real in-car speech recognition systems. The dataset and code will be released at https://github.com/HLTCHKUST/CI-AVSR.
Robust graphical lasso based on multivariate Winsorization

We propose the use of a robust covariance estimator based on multivariate Winsorization in the context of the Tarr–Müller–Weber framework for sparse estimation of the precision matrix of a Gaussian graphical model. Likewise Croux–Öllerer’s precision matrix estimator, our proposed estimator attains the maximum finite sample breakdown point of 0.5 under cellwise contamination. We conduct an extensive Monte Carlo simulation study to assess the performance of ours and the currently existing proposals. We find that ours has a competitive behavior, regarding the the estimation of the precision matrix and the recovery of the graph. We demonstrate the usefulness of the proposed methodology in a real application to breast
Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data

The relationship between Facebook posts and the corresponding reaction feature is an interesting subject to explore and understand. To achieve this end, we test state-of-the-art Sinhala sentiment analysis models against a data set containing a decade worth of Sinhala posts with millions of reactions. For the purpose of establishing benchmarks and with the goal of identifying the best model for Sinhala sentiment analysis, we also test, on the same data set configuration, other deep learning models catered for sentiment analysis. In this study we report that the 3 layer Bidirectional LSTM model achieves an F1 score of 84.58% for Sinhala sentiment analysis, surpassing the current state-of-the-art model; Capsule B, which only manages to get an F1 score of 82.04%. Further, since all the deep learning models show F1 scores above 75% we conclude that it is safe to claim that Facebook reactions are suitable to predict the sentiment of a text.
Electric vehicle charge scheduling with flexible service operations

Operators who deploy large fleets of electric vehicles often face a challenging charge scheduling problem. Specifically, time-ineffective recharging operations limit the profitability of charging during service operations such that operators recharge vehicles off duty at a central depot. Here, high investment cost and grid capacity limit available charging infrastructure such that operators need to schedule charging operations to keep the fleet operational. In this context, flexible service operations, that is, allowing delayed or expedited vehicle departures, can potentially increase charger utilization. Beyond this, jointly scheduling charging and service operations promises operational cost savings through better utilization of time-of-use energy tariffs and carefully crafted charging schedules designed to minimize battery wear. Against this background, we study the resulting joint charging and service operations scheduling problem accounting for battery degradation, nonlinear charging, and time-of-use energy tariffs. We propose an exact branch-and-price algorithm, leveraging a custom branching rule and a primal heuristic to remain efficient during the branch-and-bound phase. Moreover, we develop an exact labeling algorithm for our pricing problem, constituting a resource-constrained shortest path problem that considers variable energy prices and nonlinear charging operations. We benchmark our algorithm in a comprehensive numerical study and show that it can solve problem instances of realistic size with computational times below one hour, thus enabling its application in practice. Additionally, we analyze the benefit of jointly scheduling charging and service operations. We find that our integrated approach lowers the amount of charging infrastructure required by up to 57% besides enabling operational cost savings of up to 5%. Funding: This work was supported by the German Federal Ministry for Economic Affairs and Energy [Grant 01MV21020B]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022. 0272 .
Resource recommender system performance improvement by exploring similar tags and detecting tags communities

Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. On the other hand, using thesauruses and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses the mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users’ interests over time this article have considered the time of tag assignments in co-occurrence tags for determined similarity of tags. Then the graph is created based on these similarities. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been done using two criteria of precision and recall based on evaluations with “Delicious” dataset. The evaluation results show that, the precision and recall of the proposed method have significantly improved, compared to the other methods.
Quantum criticality and universality in the 
p
-wave-paired Aubry-André-Harper model

We investigate the quantum criticality and universality in Aubry-Andr\'{e}-Harper (AAH) model with $p$-wave superconducting pairing $\Delta$ in terms of the generalized fidelity susceptibility (GFS). We show that the higher-order GFS is more efficient in spotlighting the critical points than lower-order ones, and thus the enhanced sensitivity is propitious for extracting the associated universal information from the finite-size scaling in quasiperiodic systems. The GFS obeys power-law scaling for localization transitions and thus scaling properties of the GFS provide compelling values of critical exponents. Specifically, we demonstrate that the fixed modulation phase $\phi=\pi$ alleviates the odd-even effect of scaling functions across the Aubry-Andr\'{e} transition with $\Delta=0$, while the scaling functions for odd and even numbers of system sizes with a finite $\Delta$ cannot coincide irrespective of the value of $\phi$. A thorough numerical analysis with odd number of system sizes reveals the correlation-length exponent $\nu \simeq 1.000$ and the dynamical exponent $z$ $\simeq$ 1.388 for transitions from the critical phase to the localized phase,suggesting the unusual universality class of localization transitions in the AAH model with a finite $p$-wave superconducting pairing lies in a different universality class from the Aubry-Andr\'{e} transition. The results may be testified in near term state-of-the-art experimental settings.
A Generalized Grover/Zeta Correspondence

We introduce a generalized Grover matrix of a graph and present an explicit formula for its characteristic polynomial. As a corollary, we give the spectra for the generalized Grover matrix of a regular graph. Next, we define a zeta function and a generalized zeta function of a graph G with respect to its generalized Grover matrix as an analog of the Ihara zeta function and present explicit formulas for their zeta functions for a vertextransitive graph. As applications, we express the limit on the generalized zeta functions of a family of finite vertex-transitive regular graphs by an integral. Furthermore, we give the limit on the generalized zeta functions of a family of finite tori as an integral expression.
RISE & Shine: Language-Oriented Compiler Design

The trend towards specialization of software and hardware - fuelled by the end of Moore's law and the still accelerating interest in domain-specific computing, such as machine learning - forces us to radically rethink our compiler designs. The era of a universal compiler framework built around a single one-size-fits-all intermediate representation (IR) is over. This realization has sparked the creation of the MLIR compiler framework that empowers compiler engineers to design and integrate IRs capturing specific abstractions. MLIR provides a generic framework for SSA-based IRs, but it doesn't help us to decide how we should design IRs that are easy to develop, to work with and to combine into working compilers. To address the challenge of IR design, we advocate for a language-oriented compiler design that understands IRs as formal programming languages and enforces their correct use via an accompanying type system. We argue that programming language techniques directly guide extensible IR designs and provide a formal framework to reason about transforming between multiple IRs. In this paper, we discuss the design of the Shine compiler that compiles the high-level functional pattern-based data-parallel language RISE via a hybrid functional-imperative intermediate language to C, OpenCL, and OpenMP. We compare our work directly with the closely related pattern-based Lift IR and compiler. We demonstrate that our language-oriented compiler design results in a more robust and predictable compiler that is extensible at various abstraction levels. Our experimental evaluation shows that this compiler design is able to generate high-performance GPU code.
Patients’ evaluation based on digital motion acquisition

The paper explores the possibility of using low-cost motion capture technologies to automatically evaluate patient’s condition concerning his/her walking condition. Two different technologies, optical markerless and inertial, are used to track the gait to be adopted in a doctor’s office or at patient’s home. The data acquired are elaborated using commercial and in-house developed tools with the aim of creating, in a near future, a simple environment for medical staff and people non highly skilled in IC technology. The paper shows the feasibility of an automatic detection of a set of gait abnormalities affecting people having a lower limb prosthesis. This constitutes a robust support for orthopedic technicians work and foresees the use of such technology for larger surveys and early detection of gait deviations.
ExBrainable: An Open-Source GUI for CNN-based EEG Decoding and Model Interpretation

We have developed a graphic user interface (GUI), ExBrainable, dedicated to convolutional neural networks (CNN) model training and visualization in electroencephalography (EEG) decoding. Available functions include model training, evaluation, and parameter visualization in terms of temporal and spatial representations. We demonstrate these functions using a well-studied public dataset of motor-imagery EEG and compare the results with existing knowledge of neuroscience. The primary objective of ExBrainable is to provide a fast, simplified, and user-friendly solution of EEG decoding for investigators across disciplines to leverage cutting-edge methods in brain/neuroscience research.
Turbulent Relaxation to Equilibrium in a Two-Dimensional Quantum Vortex Gas

We experimentally study emergence of microcanonical equilibrium states in the turbulent relaxation dynamics of a two-dimensional chiral vortex gas. Same-sign vortices are injected into a quasi-two-dimensional disk-shaped atomic Bose-Einstein condensate using a range of mechanical stirring protocols. The resulting long-time vortex distributions are found to be in excellent agreement with the meanfield Poisson-Boltzmann equation for the system describing the microcanonical ensemble at fixed energy $\cal{H}$ and angular momentum $\cal{M}$. The equilibrium states are characterized by the corresponding thermodynamic variables of inverse temperature $\hat{\beta}$ and rotation frequency $\hat{\omega}$. We are able to realize equilibria spanning the full phase diagram of the vortex gas, including on-axis states near zero-temperature, infinite temperature, and negative absolute temperatures. At sufficiently high energies the system exhibits a symmetry-breaking transition, resulting in an off-axis equilibrium phase at negative absolute temperature that no longer shares the symmetry of the container. We introduce a point-vortex model with phenomenological damping and noise that is able to quantitatively reproduce the equilibration dynamics.
Phenotypic Plasticity and Local Adaptation of Leaf Cuticular Waxes Favor Perennial Alpine Herbs under Climate Change

Six perennial herbs (Plantago asiatica, Polygonum viviparum, Anaphalis lactea, Kobresia humilis, Leontopodium nanum and Potentilla chinensis) widely distributed in alpine meadows were reciprocally transplanted at two sites in eastern edge of Qinghai-Tibetan Plateau, Hongyuan (3434 m, 2.97 °C, 911 mm) and Qilian (3701 m, 2.52 °C, 472 mm), aiming to evaluate the responses of alpine plants to changing environments. When plants were transplanted from Hongyuan to Qilian, most plant species showed a decrease of total wax coverage in first year and reverse trend was observed for some plant species in second year. However, when plants were transplanted from Qilian to Hongyuan, the response of total wax coverage differed greatly between plant species. When compared with those in first year, plasticity index of average chain length of alkane decreased whereas carbon preference index of alkane increased at both Hongyuan and Qilian in second year. The total wax coverage differed between local and transplanted plants, suggesting both environmental and genetic factors controlled the wax depositions. Structural equation modeling indicated that co-variations existed between leaf cuticular waxes and leaf functional traits. These results suggest that alpine herbs adjust both wax depositions and chain length distributions to adapt to changing environment, showing climate adaptations.
Active Ageing in Italy: A Systematic Review of National and Regional Policies

Active ageing is defined as the process of optimizing opportunities for health, participation and security in order to enhance quality of life as people age. The design of active ageing policies intersects with different overarching societal challenges, especially ageing populations, social rights and sustainability. However, there are no previous attempts to review active ageing policies in the light of these challenges and the international policy objectives and targets that are guiding the international community. The aim of this study is to systematically identify, review and analyse all national and regional policies on active ageing adopted in Italy, by applying a conceptual framework derived from main international policy initiatives in the three areas. The research was conducted in two stages. First, a case study analysis was carried out per each relevant national institution and regional government. Standardised interviews were combined with policy document search, selection and analysis. Second, we performed a policy analysis in the light of a conceptual framework adopted. This latter was composed by nine policy domains, selected and integrated from principles and objectives of three overarching international frameworks on ageing—i.e., the Regional Implementation Strategy (RIS) commitments of the Madrid International Plan of Action on Ageing (MIPAA), social rights—i.e., the European Pillar of Social Rights and sustainability—i.e., the Sustainable Development Goals (SDGs) of the Agenda 2030 for Sustainable Development. Results pointed out that out of the identified nine policy fields, the major intervention areas by Italian policy makers concerned labour market participation, life-long learning, social and economic inequalities, health and well-being. Less attention had been given to issues such as gender and equal opportunities and sustainable cities. This systematic policy review is a milestone for understanding how active ageing policies contribute to address major societal challenges and what domains need further policy development.
Effects of Shopping Rehabilitation on Older People’s Daily Activities

In an aged society, the deterioration of physical and cognitive functions is prevalent. To motivate the rehabilitation of older persons, an initiative known as “shopping rehabilitation” incorporates shopping as an element of a nudge. The purpose of this study was to clarify motor function changes and cognitive functions of participants during shopping rehabilitation, through a semi-experimental study. We measured changes in the Kihon Checklist score before and after rehabilitation interventions. A paired t-test was used to analyze changes in the overall score of the basic checklist before and after the rehabilitation intervention. In December 2020, 59 participants answered the Kihon Checklist after their shopping rehabilitation intervention. During the 6-month intervention period, the number of participants with a checklist score of 8 or higher was significantly reduced after the intervention (p = 0.050). In the sub-analysis, the score improved significantly for the group with families (p = 0.050). Improvement was observed in the group living alone, but the difference was not significant (p = 0.428). The shopping rehabilitation intervention improved the Kihon Checklist score. Continuous observations and research are necessary to measure the long-term effects of shopping rehabilitation and the mechanisms that foster their maintenance and effects.
Multiferroic Coupling of Ferromagnetic and Ferroelectric Particles through Elastic Polymers

Multiferroics are materials that electrically polarize when subjected to a magnetic field and magnetize under the action of an electric field. In composites, the multiferroic effect is achieved by mixing of ferromagnetic (FM) and ferroelectric (FE) particles. The FM particles are prone to magnetostriction (field-induced deformation), whereas the FE particles display piezoelectricity (electrically polarize under mechanical stress). In solid composites, where the FM and FE grains are in tight contact, the combination of these effects directly leads to multiferroic behavior. In the present work, we considered the FM/FE composites with soft polymer bases, where the particles of alternative kinds are remote from one another. In these systems, the multiferroic coupling is different and more complicated in comparison with the solid ones as it is essentially mediated by an electromagnetically neutral matrix. When either of the fields, magnetic or electric, acts on the ‘akin’ particles (FM or FE) it causes their displacement and by that perturbs the particle elastic environments. The induced mechanical stresses spread over the matrix and inevitably affect the particles of an alternative kind. Therefore, magnetization causes an electric response (due to the piezoeffect in FE) whereas electric polarization might entail a magnetic response (due to the magnetostriction effect in FM). A numerical model accounting for the multiferroic behavior of a polymer composite of the above-described type is proposed and confirmed experimentally on a polymer-based dispersion of iron and lead zirconate micron-size particles.
Anxiolytic Effect and Improved Sleep Quality in Individuals Taking Lippia citriodora Extract

The current fast-moving, hectic lifestyle has increased the number of individuals worldwide with difficulties in managing stress, which in turn is also affecting their sleep quality. Therefore, the objective of the current study was to assess a natural plant-based dietary supplement comprised of lemon verbena (Lippia citriodora) extract, purified in phenylpropanoids, in alleviating stress and improving quality of sleep. A double-blind, placebo-controlled study was conducted for 8 weeks, followed by a 4-week washout period. Both validated questionnaires and functional tests were performed during the study, whereas questionnaires were used after the washout. As a result, the group taking the lemon verbena extract significantly reduced their perception of stress after 8 weeks, which was corroborated by a significant decrease in cortisol levels. After the washout period, the subjects reported to present even lower stress levels, due to the lasting effect of the ingredient. As for sleep quality, the subjects taking the supplement reported feeling better rested, with a stronger effect observed in women. Sleep tracking using a wearable device revealed that the supplement users improved their times in the deeper stages of sleep, specifically their percentage of time in deep sleep and REM. In conclusion, lemon verbena extract purified in phenylpropanoids is revealed as a natural solution to help individuals to improve their stress and sleep quality.
Mean ± Standard Deviation Intake Values for 1–<10-Year-Old South African Children for Application in the Assessment of the Inflammatory Potential of Their Diets Using the DII® Method: Developmental Research

This study aimed to develop a set of mean ± standard deviation (SD) intake values for South African (SA) children for 36 of the 45 food parameters included in the original Dietary Inflammatory Index (DII®) tool. The SA food composition database contains 30 of the 45 food parameters included in the original DII®, and a supplementary database was developed for six of the food parameters not included in the SA database. The SA child mean ± SD intake of macronutrients, micronutrients and select flavonoids was calculated by age in years, using eight data sets from dietary surveys conducted in SA in the last three decades. A total sample of 5412 children was included in the calculation of the mean ± SD. The current study sample was determined to be representative of 1–<10-year-old children in SA, and the plausibility of the mean intake values was confirmed by being in line with age-appropriate recommendations. Furthermore, an increase in energy, macronutrient, and most micronutrient intakes with increase in age was evident. The generated mean ± SD values for SA children can be used for calculation of the inflammatory potential of the dietary intake of SA children in the age range of 1–<10-year-old children.