Miracle-dz/UniFAR-FTUs huggingface dataset:
SymNet 2.0: Effectively handling Non-Fluents and Actions in Generalized Neural Policies for RDDL Relational MDPs

Relational MDPs (RMDPs) compactly represent an infinite set of MDPs with an unbounded number of objects. Solving an RMDP requires a generalized policy that applies to all instances of a domain. Recently, Garg et al. proposed SymNet for this task – it constructs a graph neural network that shares parameters across all instances in a domain, thus making it applicable to any instance in a zero-shot manner. Our analysis of SymNet reveals that it performs no better than random on 1/4th of planning competition domains. The key reasons are its design choices: it misses important information during graph construction, leading to (1) poor generalizability, and (2) potential non-identifiability of different actions. In response, our solution, S YM N ET 2.0, substantially augments SymNet’s graph construction approach by introducing additional nodes and edges which allow a better transfer of important information about a domain. It also improves SymNet’s action decoders with relevant information from objects to make different actions identifiable during scoring. Extensive experiments on twelve competition domains, where we use imitation learning over data generated from the PROST planner, demonstrate that S YM N ET 2.0 performs vastly better than SymNet. Interestingly, even though S YM N ET 2.0 is trained over data from PROST, it outperforms the planner on several test instances due to former’s ability to scale to large instances in a zero-shot manner.
Input Impedance Modeling and Verification of Single-Phase Voltage Source Converters Based on Harmonic Linearization

The impedance-based stability analysis of single-phase voltage source converters (VSCs) emphasizes the precise impedance modeling. Considering the complexity of $dq$-frame impedance modeling and measurement, this paper proposes a mirror-frequency impedance modeling and measurement approach of single-phase VSCs in the stationary frame based on harmonic linearization. First, the mirror frequency definition is put forward and the dynamics of the second-order generalized phase-locked loop considering the mirror-frequency effects is validated by simulations. Next, the impedance model considering the mirror-frequency effects and dc-link voltage control loops is compared with the conventional one-dimension impedance model and a mirror-frequency impedance measurement method is presented, which are verified by experiments on the hardware-in-the-loop platform. The results indicate that the mirror-frequency impedance model considering the dc-link voltage control is more accurate and better captures the cross-coupling dynamics of VSCs. Finally, the different bandwidths of dc-link voltage control loops and different capacitances are designed to study the impacts of dc-link voltage control loops and ripples on impedance. The experiments and impedance-based analysis illustrate that the wider bandwidth of dc-link voltage control loops or the less capacitance with high dc-link voltage ripple will expand the frequency range of negative impedance in the low frequency.
Trash Image Classification System using Machine Learning and Deep Learning Algorithms

In today’s fast pacing world of the internet age with all the amenities and latest gadgets, the major urban cities in the world are still struggling with trash management. Only a few countries using the recycling of wastes but most of them dumping all the trash to the landfills. The quantity of generated trash in day to day life is affecting land, water, and air which causes a serious threat to the aquatic species and their surroundings and ultimately to humans if not managed properly. The objective of this study is to develop a system that can classify these trash images into their correct categories with the help of machine learning and deep learning methodologies. The dataset used for achieving this objective is released by the TACO dataset consist of 1500 images of litter and trash with annotations and labeled. There are five categories of trash with the name ’Plastic straw’, ’Drink can ’, Cigarette’, and ’Clear plastic bottle’ considered for this work. This dataset is recently released and very rarely used in any research work. To make the dataset more scalable and balanced various data augmentation techniques will be adopted. Four classification algorithms such as the Sequential Keras model, transfer learning ResNet-50, and VGG-19 models and XGBoost classifier model will be developed for features extraction and classification of images. The performance of the model will be evaluated based on accuracy and comprehensive comparison will occur among all the four models on several parameters. The outcomes showed that pre-trained transfer learning models can be used for high classification accuracy and assured that data augmentation techniques assists in improving the overall results.
A thermo-frictional tyre model including the effect of flash temperature

ABSTRACT A new tyre model is developed that can predict the influence of both macroscopic and local flash temperature on tyre force generation. The model comprises two heat-transfer solvers. A macroscopic solver calculates the 3D temperature distribution across the tread and sidewall at a resolution of a few millimetres. A separate flash-temperature solver calculates the local hot-spot temperature distribution at the macro-asperity tyre-road contact interface at a resolution of micrometres. The two heat-transfer solvers are coupled with a structural model for the calculation of tyre forces and the sliding speed distribution along the contact patch. The sliding speed distribution feeds into the flash-temperature model and the local coefficient of friction is found as a function of sliding speed, flash temperature, normal pressure, road roughness and the complex modulus of rubber. The proposed tyre model is the first to include the effect of a changing macroscopic temperature distribution on the build-up of the local flash temperature, and to account for road-tread conduction at the macro-asperity contact interface. The model is applicable for identifying the friction envelope and optimum temperature range for tyres on roads with known roughness. This is important in motorsport where knowledge of grip offers a competitive advantage.
Negotiated Syllabus and Undergraduate Students Reading Comprehension and Oral Production Ability: Teachers’ and Learners’ Perspectives

The negotiated syllabus, also known as a learner-centered and process-oriented syllabus, was introduced as an alternative to a pre-determined syllabus a couple of decades ago. The review of the related studies shows the number of studies in the use of negotiated syllabus in English language teaching is scanty. This study aimed at delving into the impacts of the negotiated syllabus on undergraduate students’ reading comprehension, oral production, and the participants’ perspectives about the syllabus. A mixed-methods research design was employed. Data were collected through researcher-developed tests and an interview checklist. The quantitative section analyzed through independent samples-tests (p=0.05) verified that the negotiated syllabus had a significant effect on the experimental mean scores regarding reading comprehension and oral ability production. In addition, results of the qualitative phase indicated that the use of negotiated syllabus, despite some learner, teacher, and institute-related constraints, has some pedagogical advantages. In light of the results, teachers are recommended to minimize the limitations and implement the negotiated syllabus in teaching English programs to optimize the language learners’ motivation and language achievement.
Model-Based Air Flow Path Optimization for Heat Pump Clothes Dryer

A heat pump clothes dryer (HPCD) uses a vapor compression system to dry clothes. The condenser heats air, which passes through the drum to evaporate moisture out of the clothes, and the evaporator condenses water out of the air stream. As a result, the HPCD can achieve 50% energy savings compared to a conventional electric resistance dryer. In this work we developed a physics-based, quasi-steady-state HPCD system model with detailed heat exchanger and compressor models. The system model can simulate the inherently transient HPCD drying process, to size components, and to reveal trends in key variables (e.g. compressor discharge temperature, power consumption, required drying time, etc.) The system model was calibrated using experimental data from a prototype HPCD. Air leakages, in and out, along the closed air circulation path of HPCD cause varied effects on the performance. Understanding the location, magnitude, and direction of air leakage of the heat pump clothes dryer is critical for accurately characterizing the performance and developing a high-performance design. The system model was used to reveal the impacts. In addition, model-based parametric optimizations were conducted to design the HPCD charge inventory, flow rate, air path and leak points for optimum performance.
On Gauge Mediation and Cosmological Vacuum Selection

Gauge mediation of supersymmetry breakdown has many attractive features and can be realized in phenomenologically interesting string-motivated models. We point out that in models with the Polonyi-like field stabilized at a low expectation value by quantum corrections, gravity seems to limit from above the admixture of gravity mediation to the dominant gauge mediation channel. However, we also point out that in a class of such models the low energy metastable supersymmetry breaking vaccum appears to be cosmologically disfavoured. These features should hold also in the case of typical stabilized models with anomalous U(1) groups.
Benchmarking Multimodal Variational Autoencoders: GeBiD Dataset and Toolkit

Multimodal Variational Autoencoders (VAEs) have been a subject of intense re- 1 search in the past years as they can integrate multiple modalities into a joint rep- 2 resentation and can thus serve as a promising tool for both data classification and 3 generation. Several approaches toward multimodal VAE learning have been pro- 4 posed so far, their comparison and evaluation have however been rather inconsistent. 5 One reason is that the models differ at the implementation level, another problem 6 is that the datasets commonly used in these cases were not initially designed for 7 the evaluation of multimodal generative models. This paper addresses both men- 8 tioned issues. First, we propose a toolkit for systematic multimodal VAE training 9 and comparison. Second, we present a synthetic bimodal dataset designed for a 10 comprehensive evaluation of the joint generation and cross-generation capabilities. 11 We demonstrate the utility of the dataset by comparing selected state-of-the-art 12 models. 13
Marriage Trends in Korea: Changing Composition of the Domestic Marriage Market and Growth in International Marriage

We advance research on pronounced declines in marriage in gender-inegalitarian countries by decomposing the decline in marriage among Korean women and men into changes in educational composition of the marriage market and changes in the likelihood of specific pairings. We also consider the role of international marriages in offsetting the posited implications of changing marriage market composition. Rapid expansion of women’s education and increase in marriages between Korean grooms with low levels of education and foreign brides make Korea a particularly relevant setting in which to address the limitations of previous research on marriage in East Asia. Using individual records from two censuses, we show that changes in the availability of potential spouses offset rapid decline in the marriage propensities of women with a high school education or less, but accounted for between 10-30% of the decline in marriage among women with tertiary education. Among men with a high school education or less, changes in marriage market composition accounted for between one-fifth and one-fourth of the decline in marriage between 1985-89 and 2005-09. We also show that growth in international marriages played a role in preventing even more dramatic decline in marriage among low educated men. These findings have potentially important implications for inequality in a society like Korea where marriage is remains a symbol of social success and is closely related to women’s economic well-being and men’s health and subjective well-being.
The aerodynamic development of the wing of the A 300B

The A300B, otherwise known as the European Airbus, is an international collaborative project. Hawker Siddeley Aviation are under contract to Airbus Industrie to design and manufacture the wing. Manufacture is now well advanced, the first wing having been delivered to Toulouse in November 1971. In describing the aerodynamic development of the wing for this aircraft, it is necessary to describe events before the advent of the designation A300B, as the aerodynamic design philosophy was established prior to that date. In particular reference will be made to the preceding paper project the A300 and to a lesser extent to projects being considered before the opening of technical discussions between Sud Aviation, Deutsche Airbus, and Hawker Siddeley Aviation.
Food Detection by Fine-Tuning Pre-trained Convolutional Neural Network Using Noisy Labels

Summary Deep learning is an advanced technology for large-scale data analysis, with numerous promising cases like image processing, object detection and significantly more. It becomes customarily to use transfer learning and fine-tune a pre-trained CNN model for most image recognition tasks. Having people taking photos and tag themselves provides a valuable resource of in-data. However, these tags and labels might be noisy as people who annotate these images might not be experts. This paper aims to explore the impact of noisy labels on fine-tuning pre-trained CNN models. Such effect is measured on a food recognition task using Food101 as a benchmark. Four pre-trained CNN models are included in this study: InceptionV3, VGG19, MobileNetV2 and DenseNet121. Symmetric label noise will be added with different ratios. In all cases, models based on DenseNet121 outperformed the other models. When noisy labels were introduced to the data, the performance of all models degraded almost linearly with the amount of added noise.
Undergraduate Review Undergraduate The Effects of TikTok Use on College Student Learning The Effects of TikTok Use on College Student Learning

TikTok is an app that allows someone to make dancing or lifestyle videos, and it has become increasingly popular the past couple of years, especially with college students. But does that mean that this app poses a challenge to college students paying attention in class and getting their schoolwork done? One hundred and eleven college students between the ages of 18 and 28 from colleges such as Bridgewater State University and UMass Amherst participated in an online survey of 85 questions relating to emerging adulthood and topics such as anxiety, social media, relationships, stress, and more topics that are similar. The results showed that the more time participants spent on TikTok each day, the more they became distracted on TikTok when they were trying to pay attention in class and complete schoolwork. Similar results occurred when looking at losing track of time on TikTok and becoming distracted on TikTok when they were trying to pay attention in class and complete schoolwork. The more that someone found themselves going on TikTok each day, the more they lost track of time on TikTok. These findings show that TikTok can impact college students’ abilities to be able to pay attention in class and get their schoolwork done, so have the if
Adaptation to Surface Water Scarcity in Irrigated Agriculture

This paper studies the long-run effects of surface water scarcity in irrigated agriculture and the extent of adaptation. First, I estimate the long-run effects of persistent differences in water supplies using spatial discontinuities between neighboring water utilities in California. Then, I measure adaptation by comparing these long-run effects with the short-run effects of weather-driven fluctuations in annual water supplies. Water scarcity reduces crop area and crop revenue (as predicted by crop choice) in both the short run and the long run. Differing crop substitution patterns reveal that farmers adapt but in ways that do not offset the lost production.
What modelling research on supply chain collaboration informs us? Identifying key themes and future directions through a literature review

Although research regarding supply chain collaboration has been increasing, studies reviewing supply chain collaboration remains limited. This review paper categorises supply chain collaboration and identifies different themes in this research field. Articles were collected based on a designed keywords list and specific subject restrictions in citation databases. Text-mining technology and conceptual criteria were used to categorise and screen articles into different categories. Finally, cluster analysis was used to group articles and identify emerging themes. From the year 1999 to 2017, a total of 678 articles regarding supply chain collaboration were found. These were sorted into six categories: levels of collaboration, industries, supply chain stages, company scales, forms/methods of collaboration and typology of article, and into corresponding sub-categories. 380 articles employing modelling approach where collaboration forms the core of analysis were screened for hierarchical cluster analysis, resulting in four clusters: information sharing paradigm, joint decision-making paradigm, resource sharing paradigm and coordinating contract paradigm. Eleven themes were finally identified based on the four clusters. The number of articles in the field of supply chain collaboration shows an increasing trend by year. However, most of the collaborations discussed were still at a low level. It is expected to explore a higher level of collaborations and related themes in future research.
Stable Limits for the OLS estimators in the AR-ARCH model

This note establishes that the OLS estimator of the autoregressive parameter in the AR-ARCH model is asymptotically stable, with a non-standard rate of convergence if the ARCH process has finite second order moment, but non-finite fourth order moment and the true autoregressive parameter is smaller than one in absolute value. Since the use of this result has not yet been determined, the purpose of this note is solely to state and prove the result.
Polar codes for optical communications

POLAR CODES FOR OPTICAL COMMUNICATIONS Tufail Ahmad M.S. in Electrical and Electronics Engineering Advisor: Erdal Arıkan May 2016 Optical communication systems have become the backbone of long distance communication networks due to their ability to transport data at high rates. A typical modern optical communication system should be capable to achieve data rates of 100 Gb/s or beyond. At such a high data rate, it is not feasible to retransmit the corrupted data. For reliable communication at improved power efficiency, communication systems use forward error correction (FEC) schemes. FEC schemes should have low latency to provide high throughput and good error performance to achieve an output bit error rate (BER) of 10−15 or lower in optical channels. Moreover, implementation schemes of these FEC codes should be simple, as telecommunications equipments in optical access networks can only accommodate restricted hardware complexity. In this contribution, we study existing ITU-T G.975. 1 recommended FEC schemes and recently proposed state-of-the-art FEC codes for optical networks. Next, we analyze polar codes, a recently proposed class of error-correcting codes with advantageous properties in terms of error performance, structure, latency and design method. Throughout our analysis we assume that optical channels can be modeled as additive white Gaussian noise (AWGN) channels. We investigate whether polar codes can compete with the above mentioned FEC schemes in the arena of optical communications. We conclude that polar codes outdo all G.975. 1 recommended FEC codes in terms of error performance with the same overhead and relatively shorter block lengths. We also highlight some of the issues/aspects which need to be addressed to enhance the error performance of polar codes at finite block lengths so that they can catch up with (or surpass) recently proposed third generation FEC codes. Most of the proposed FEC codes for next generation optical networks are based on LDPC and turbo codes. Unfortunately, these codes have error floors at very iii
A methodological framework to assess temporal trends and sub-national disparities in healthcare quality metrics using facility surveys, with applications to sick-child care in Kenya, Senegal, and Tanzania

Monitoring healthcare quality at a subnational resolution is key to identify and resolve geographic inequities and ensure that no subpopulation is left behind. Yet, health facility surveys are typically not powered to report reliable estimates at a subnational scale. In this study, we present a framework to fill this gap and jointly analyse publicly available facility survey data, allowing exploration of temporal trends and subnational disparities in healthcare quality metrics. Specifically, our Bayesian hierarchical model includes random effects to account for differences between survey instruments; space-time processes to leverage correlations in space and time; and covariates to incorporate auxiliary information. We apply this framework to Kenya, Senegal, and Tanzania, three countries with at least four rounds of standardized facility surveys each, and estimate the readiness and process quality of sick-child care over time and across subnational areas. These estimates of readiness and process quality of care over time and at a fine spatial resolution show uneven progress in improving facility based service provision in Kenya, Senegal, and Tanzania. For instance, while national gains in overall readiness of care improved in Tanzania, geographic inequities persisted; in contrast, Senegal, and Kenya experienced stagnation in overall readiness at the national level, but disparities grew across subnational areas. Overall, providers adhered to about one third of the clinical guidelines for managing sick child illnesses at the national level. Yet across subnational units, such adherence greatly varied (e.g., 25% to 85% between counties of Kenya in 2020). Our new approach enables identifies precise estimation of changes in the spatial distribution of healthcare quality metrics over time, at a a programmatic spatial resolution, and with accompanying uncertainty estimates. Use of our framework will provide new insights at a policy relevant spatial resolution for national and regional decision makers, and international funders.