Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Uniform Adversarially Robust Pruning

Neural networks often are highly redundant and can thus be effectively compressed to a fraction of their initial size using model pruning techniques without harming the overall prediction accuracy. Additionally, pruned networks need to maintain robustness against attacks such as adversarial examples. Recent research on combining all these objectives has shown significant advances using uniform compression strategies, that is, all weights or channels are compressed equally according to a preset compression ratio. In this paper, we show that employing non-uniform compression strategies allows to improve clean data accuracy as well as adversarial robustness under high overall compression—in particular using channel pruning. We leverage reinforcement learning for finding an optimal trade-off and demonstrate that the resulting compression strategy can be used as a plug-in replacement for uniform compression ratios of existing state-of-the-art approaches.
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes

Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene11Code: https://github.com/ViLab-UCSD/PhotoScene, a framework that takes input image(s) of a scene along with approximately aligned CAD geometry (either reconstructed automatically or manually specified) and builds a photorealistic digital twin with high-quality materials and similar lighting. We model scene materials using procedural material graphs; such graphs represent photorealistic and resolution-independent materials. We optimize the parameters of these graphs and their texture scale and rotation, as well as the scene lighting to best match the input image via a differentiable rendering layer. We evaluate our technique on objects and layout reconstructions from ScanNet, SUN RGB-D and stock photographs, and demonstrate that our method reconstructs high-quality, fully relightable 3D scenes that can be re-rendered under arbitrary viewpoints, zooms and lighting.
Effectiveness of using ICTs to promote teaching and learning in technical education: case of Bangladesh

ICTs play a dynamic role in technical education during delivery of learning materials as learners can access knowledge and improve their skills from anywhere and anytime. The aim of the study is to identify the effectiveness of using ICTs to promote teaching and learning. Survey research design was adopted by the researchers for the study where the teachers of government polytechnic institutes of Bangladesh were considered as population. There have 45 government polytechnic institutes in Bangladesh and the size of the population is almost 1,500. Clustered sampling was used to select polytechnic institutes out of six administrative divisions. In Bangladesh, ICTs implication in technical education is very few. Thus, for the convenience of the study, a purposive random sampling was used for obtaining a sample of 120. A structured questionnaire is used for collecting data. The questionnaire was validated with experts’ opinions. The data were tabulated in the form of frequency distribution, percentage and weighted average. Collected data were analysed by the method of inferential statistic and quantitative analysis exposed in a tabular and graphical forms. The research revealed that integration of ICTs in teaching and learning process will make teaching and learning very easy, interesting, and time saving than that of traditional way of teaching-learning. More than 60% of technical education teachers strongly agreed that ICTs are essential for enhancing the process of teaching-learning in the polytechnic institutions. This research has suggested considering the stimulating factors such as motivation and attractiveness to design ICT-based teaching-learning process in the polytechnic institute of Bangladesh. Key words: ICT (Information and communication technology), teaching – learning (T-L), educational technology (ET), technical and vocational education (TVE) and competency standards for teachers (CST).
Physics-based Modelling for Aircraft Noise and Emission Predictions

Starting from semi-empirical noise source models for the aircraft and 4D trajectory computations, this work focuses on the environmental assessment of scenario studies regarding technology evaluation and procedural planning. Extensive work was performed on improving and validating the existing tools. The physics based dynamic modelling for straight inflight was extended to account for a third space dimension and the inclusion of non-zero wind. Real flight data were used for model verification. Ground noise measurements were used to validate the physics-based prediction of source noise for varying operating conditions and to adapt the model to a state-of-the-art aircraft and engine. In this thesis, a summary of the developed physics-based methods and their validation are presented and the selected case studies are described. An important part of the presented research focused on sustainability aspects and the evaluation of interdependencies between noise, NO x and CO 2 emissions. New propulsion system designs were generated for a state-of-the-art ultra-high bypass ratio turbofan engine by allowing variation in the OPR (Overall Pressure Ratio), FPR (Fan Pressure Ratio) and BPR (Bypass Ratio). By varying these parameters, the engine was optimized for minimum installed specific fuel consumption. Allowing minimum fuel burn variation around this optimal point, different engine designs and operational characteristics were established and trades between LTO (Landing and Take-off) NO x emissions and cumulative noise were examined. Another aspect focusing on the sustainability of air transport concerns the operational level, where the aim is to establish improved procedures and trajectories through the use of the existing technology. Several noise abatement procedures already exist and are implemented to reduce pollution around airports. Focusing on approach procedures, these standard operations were evaluated for noise and emissions. More advanced procedures were designed and assessed for their environmental impact and optimization was carried out to establish the optimal procedure for specific cases. It was demonstrated that quantifying these trade-offs and adapting the design to specific conditions is essential when new flight procedures are designed.
Bayesian network modeling of risk and prodromal markers of Parkinson’s disease

Parkinson's disease (PD) is characterized by a long prodromal phase with a multitude of markers indicating an increased PD risk prior to clinical diagnosis based on motor symptoms. Current PD prediction models do not consider interdependencies of single predictors, lack differentiation by subtypes of prodromal PD, and may be limited and potentially biased by confounding factors, unspecific assessment methods and restricted access to comprehensive marker data of prospective cohorts. We used prospective data of 20 established risk and prodromal markers of PD in 1178 healthy, PD-free individuals and 24 incident PD cases collected longitudinally in the Tuebingen evaluation of Risk factors for Early detection of NeuroDegeneration (TREND) study at 4 visits over up to 10 years. We employed artificial intelligence (AI) to learn and quantify PD marker interdependencies via a Bayesian network (BN) with uncertainty estimation using bootstrapping. The BN was employed to generate a synthetic cohort and individual marker profiles. Robust interdependencies were observed for BN edges from age to subthreshold parkinsonism and urinary dysfunction, sex to substantia nigra hyperechogenicity, depression, non-smoking and to constipation; depression to symptomatic hypotension and excessive daytime somnolence; solvent exposure to cognitive deficits and to physical inactivity; and non-smoking to physical inactivity. Conversion to PD was interdependent with prior subthreshold parkinsonism, sex and substantia nigra hyperechogenicity. Several additional interdependencies with higher statistical uncertainty were identified. Synthetic subjects generated via the BN based representation of the TREND study were realistic as assessed through multiple comparison approaches of real and synthetic data. Altogether our work demonstrates the potential of modern AI approaches (specifically BNs) in two ways: First, to model and understand interdependencies between PD risk and prodromal markers, which are so far not accounted for in PD prediction models. Second, the generative nature of BNs opens the door for facilitating data sharing in a legally compliant and privacy preserving manner.
A Review of the Decision Transformer Architecture: Framing Reinforcement Learning as a Sequence Modeling Problem

—This article provides a review of the Decision Transformer, a new approach to framing reinforcement learning as a sequence modeling problem. For modeling trajectories as sequences, the representation is modified by adding the return-to-go token. The modeling architecture is a decoder-only variant of the Transformer architecture and uses masked self-attention for autoregressive action prediction. Results of the original paper show that the Decision Transformer provides a valid alternative in offline reinforcement learning tasks but leave space for future work in regards to the importance of the size of the context window and effect of the return-to-go token.
EXPLORING NUTRITIONAL AND FUNCTIONAL PROPERTIES OF DIFFERENT VARIETIES OF SESAME SEED CAKES: AN INDUSTRIAL BY-PRODUCT

Sesame (Sesamum indicum), as an oilseed, has been explored widely by the oil industry. Therefore, the production of its by-product, seed cake is also high. Since the seed cake is a high source of protein and fiber, it has the potential to be utilized as value-added food product. The present study is aimed to evaluate various functional and nutritional properties of four selected varieties of white (Pragati and Shekhar) and black (VIP and Ojha) sesame seeds and their seed cake, as obtained by solvent extraction. The study revealed significantly (p<0.01) higher water and oil absorption capacities, as well as the swelling capacity of all studied sesame seed cakes than their whole or flour of sesame seed. The study has shown that sesame seed (27.25 g/100g) and cake (48.36 g/100g) of the Pragati variety have exceptionally high protein content. VIP variety of sesame seed and cake are a rich source of fiber, having 18.74g/100g and 29.56 g/100g respectively. The high fiber also shows good correlations with water absorption capacity, as recorded in the case of sesame flour (r 2 =0.966) and seed cake (r 2 =0.916). The high functional and nutritional values of sesame seed cakes suggest their potential use in developing a functional ingredient for food and nutraceutical formulations.
Entrepreneurial Motivation and Entrepreneurship Career Intention: Case at a Malaysian Public University

Entrepreneurship intention for graduate student like many other job and work career options depends on other considerations. Literature reviews revealed that more research works are needed in examining the determinants of entrepreneurship intentions of students. This study is to determine the relationship between student’s entrepreneurial motivation and entrepreneurship intention. A total of 450 self-administered questionnaires were distributed to the students of the various faculties, races and student seniority at a Public University using the convenient and structured sampling method. Finally, 413 duly completed questionnaires were returned and used in the analyses. Correlation analysis and regression analysis showed that strength of motivation in choosing entrepreneurship as a career option among the students is related with entrepreneurship intention. The entrepreneurial motivation factors affecting career intention to be an entrepreneur are made up of behavioral control, subjective norm, and attitude towards entrepreneurship. Subjective norm (tolerance for risk) and attitude (desirability) of self-employment are significantly related to both student immediate and future intentions to be an entrepreneur. However, behavioral control entrepreneurial motivation is found to be significantly related to student immediate career intention but not significantly related to student future entrepreneurship career intention. Creating of opportunity for practice of key entrepreneurial behaviors is seen as crucial in cultivating entrepreneurial behaviors. Theory of Planned Behavior Model can be used as a tool to identify would be entrepreneur and target them for entrepreneurship career development. This study argued and supported the call that those youths who are really serious to start and own a business as would be entrepreneurs can be identified and targeted to develop through entrepreneurship interventions initiatives.
A Comparison Analysis on Car User Decision When Faced with Different Congestion Pricing Policy (Case Study: Jakarta Metropolitan Area)

This research aims to analyze the car user’s decision in their mode choice when faced with two different congestion pricing, namely Road Pricing (RP) and Parking Pricing (P2) policy scheme. The comparison analysis is conducted through the developed binomial logit model both for RP and P2 policy schemes. Data is collected by utilizing a stated preference survey, where the car user is asked their preference towards the mode choice on a given set of price and timesaving combination for both policies. Then the utility function for each policy is established by conducting some statistical procedures to have a feasible and reasonable model. Having set the utility functions, the validity test is carried out to the developed model using a different set of data from the survey. Further analysis is done to select the model that satisfies the statistical feasibility and the validity threshold number. Comparison to both different choice models yields different decisions represented in the form of shifting mode percentages. The research results show that the RP policy scheme tends to shift the car user higher than the P2 policy scheme at the same total additional charging fare.
Hybridization of Air Quality Forecasting Models Using Machine Learning and Clustering: An Original Approach to Detect Pollutant Peaks

This paper presents an original approach combining Artificial Neural Networks (ANNs) and clustering in order to detect pollutant peaks. We developed air quality forecasting models using machine learning methods applied to hourly concentrations of ozone (O 3), nitrogen dioxide (NO 2) and particulate matter (PM 10) 24 hours ahead. MultiLayer Perceptron (MLP) was used alone, then hybridized successively with hierarchical clustering and with a combination of self-organizing map and k-means clustering. Clustering methods were used to subdivide the dataset, and then an MLP was trained on each subset. Two urban sites of Corsica Island in the western Mediterranean Sea were investigated. These models showed a good global precision (Index of Agreement reaching 0.87 for O 3 , 0.80 for NO 2 and 0.74 for PM 10). Considering it is particularly important than forecasting model used on an operational basis correctly predict pollution peaks, a sensitivity analysis was performed using Receiver Operating Characteristic curves (ROC curves). It allowed to evaluate the behaviour and the robustness of the models for high concentration situations. The results show that for PM 10 and O 3 , hybrid models made of a combination of clustering and MLP outperform classical MLP most of the time for high concentration prediction. An operational tool has been built with the models presented in this paper, and is used for air quality forecasting in Corsica.
Productivity and Pay in the United States and Canada

We study the productivity-pay relationship in the United States and Canada along two dimensions. The ﬁrst is divergence : the degree to which productivity has grown faster than pay. The second is delinkage : the degree to which incremental increases in the rate of productivity growth translate into incremental increases in the rate of growth of pay, holding all else equal. In both countries there has been divergence: the pay of typical workers has grown substantially more slowly than average labour productivity over recent decades, driven by both rising labour income inequality and a declining labour share of income. Even as the levels of productivity and pay have grown further apart, however, we ﬁnd evidence for a substantial degree of linkage between productivity growth and pay growth: in both countries, periods with faster productivity growth rates have been periods with faster rates of growth of the pay of average and typical workers, holding all else equal. This linkage appears somewhat stronger in the US than in Canada. Overall, our ﬁndings lead us to tentatively conclude that policies or trends which lead to incremental increases in productivity growth, particularly in large relatively closed economies like the USA, will tend to raise middle class incomes. At the same time, other factors orthogonal (i.e. statistically independent) to productivity growth have been driving productivity and typical pay further apart, emphasizing that much of the evolution in middle class living standards will depend on measures bearing on relative incomes.
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1Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy; 2Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands; 3Neurological Clinics, University Hospital of Pisa, Pisa, Italy; 4Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy; 5Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy
ARC Centre of Excellence in Population Ageing Research Paper 2018/20 Evaluating Consumers’ Choices of Medicare Part D Plans: A Study in Behavioural Welfare Economics

: We propose new methods to model choice behavior and conduct welfare analysis in complex environments where it is untenable to assume that choices fully reveal preferences. In particular, we investigate how Medicare beneficiaries choose prescription drug plans (PDPs) under the Medicare Part D program. Our approach is novel in that we estimate a multinomial logit model for PDP choice that allows for heterogeneity in both preferences and the behavioral choice process. We find the data can be well characterized by a mixture of three behavioral types: The “rational” type constructs expected out-of-pocket costs E(OOP) rationally, and, ceteris paribus , seeks to minimize premiums plus E(OOP) as theory suggests. The second type constructs expected out-of-pocket (OOP) costs rationally, but puts too much weight on premiums relative to E(OOP) in choosing plans. A third type, who we label “confused,” places weight on irrelevant financial aspects of drug plans, implying they fail to construct E(OOP) rationally. A consumer is more likely to be the “confused” type if they suffer from Alzheimer’s disease and/or depression. We use the model to quantify the monetary and welfare losses that arise from suboptimal decision making for the population, for the behavioral types, and for people with cognitive limitations. We also evaluate policies to simplify the choice set to reduce these losses.
The Long-Run Eﬀects of Psychotherapy on Depression, Beliefs, and Preferences ∗

We study the long-run eﬀects of inexpensive psychotherapy interventions on mental health, self-conﬁdence, economic preferences, and labor-market outcomes. We revisit two clinical trials in India that randomized depressed adults (n=775, 85% women) to a brief course of psychotherapy or a control condition. Four to ﬁve years later, we ﬁnd substantially lower rates (12 percentage points) and severity (0.15 standard deviations) of depression in the treatment group. To measure impacts on self-conﬁdence, participants performed a work task and estimated their performance relative to others. They then received noisy feedback about their relative performance. Therapy made people’s beliefs about themselves more accurate by reducing their tendency to react more to positive than negative feedback, thereby reducing overconﬁdence. Therapy also increased summary measures of altruism and patience, driven by changes in self-evaluations. We ﬁnd no signiﬁcant impacts on risk tolerance, employment, or consumption.
Blockchain Technology A

: Blockchain technology has attracted considerable interest in the last 15 years. It is argued that Blockchain can sustain any transaction of value, be it monetary or information, in a manner that is secure and independent of interpersonal trust. Yet, there remains little understanding on whether and how this technology enables trust-free transactions. This paper provides a novel theoretical account on the relationship between trust and Blockchain technology. Furthermore, it tests a set of hypotheses, associated with the conception of Blockchain as a trust-free environment, through an online experiment in which the properties of Blockchain-based smart contacts are exploited. The results indicate that the presence of Blockchain technology does not eliminate trusting and trustworthy behavior from human interactions. On the contrary, in comparison to the baseline group, the behavior of the participants in the Blockchain treatment exhibited more trusting and trustworthy behavior, indicating support for the claim that this technology might indeed be understood as a “trust-building machine”.
The role of the density profile location on pedestal stability in ASDEX Upgrade

The impact of a localised region of high density in the high field side scrape off layer (the high-field side high density, HFSHD), on pedestal structure and stability is presented. Notably, once the HFSHD is formed by a combination of high recycling at the inner target and power crossing the separatrix to ionise the reycled neutrals, the fuelling pattern of the pedestal changes. Instead of simply increasing the pedestal top density, the density profile now shifts outwards, as shown in a gas puff scan. This outward shift, which has been observed to be ∼5 mm in AUG, causes a significant degradation of the pedestal top pressure, typically manifested as a drop in the temperature. Predictive modelling of the pedestal top pressure indicates a drop of 25% in the attainable pedestal top pressure, which compares well with measurements. Mitigation of the HFSHD by the application of impurity seeding is also shown. Both nitrogen and neon reduce the density in the HFSHD by radiating the power required to ionise the neutrals at the HFS divertor entrance. At similar levels of radiated power, similar shifts of the density profile, and, hence, similar changes in the pedestal top pressure and global confinement are observed.