Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Analysis of Cloud Objects in the High-Resolution Rapid Refresh (HRRR) Model Using Object-Based Verification

AbstractIn this study, object-based verification using the method for object-based diagnostic evaluation (MODE) is used to assess the accuracy of cloud-cover forecasts from the experimental High-Resolution Rapid Refresh (HRRRx) model during the warm and cool seasons. This is accomplished by comparing cloud objects identified by MODE in observed and simulated Geostationary Operational Environmental Satellite 10.7-μm brightness temperatures for August 2015 and January 2016. The analysis revealed that more cloud objects and a more pronounced diurnal cycle occurred during August, with larger object sizes observed in January because of the prevalence of synoptic-scale cloud features. With the exception of the 0-h analyses, the forecasts contained fewer cloud objects than were observed. HRRRx forecast accuracy is assessed using two methods: traditional verification, which compares the locations of grid points identified as observation and forecast objects, and the MODE composite score, an area-weighted calculat.
Timed Secret Sharing

Secret sharing has been a promising tool in cryptographic schemes for decades. It allows a dealer to split a secret into some pieces of shares that carry no sensitive information on their own when being treated individually but lead to the original secret when having a sufficient number of them together. Existing schemes lack considering a guaranteed delay prior to secret reconstruction and implicitly assume once the dealer shares the secret, a sufficient number of shareholders will get together and recover the secret at their wish. This, however, may lead to security breaches when a timely reconstruction of the secret matters as the early knowledge of a single revealed share is catastrophic assuming a threshold adversary. This paper presents the notion of timed secret sharing (TSS), providing lower and upper time bounds for secret reconstruction with the use of time-based cryptography. The recent advances in the literature including short-lived proofs [Asiacrypt 2022], enable us to realize an upper time bound shown to be useful in breaking public goods game , an inherent issue in secret sharing-based systems. Moreover, we establish an interesting trade-off between time and fault tolerance in a secret sharing scheme by having dealer gradually release additional shares over time, offering another approach with the same goal. We propose several constructions that offer a range of security properties while maintaining practical efficiency. Our constructions leverage a variety of techniques and state-of-the-art primitives.
Is Semantic-aware BERT more Linguistically Aware? A Case Study on Natural Language Inference

Recent work has shown that predicate-argument label representations from semantic role labeling (SRL) can be concatenated with BERT representations to improve natu-ral language understanding tasks such as natu-ral language inference (NLI) and reading comprehension. Two natural questions that arise are whether infusing SRL representations with BERT 1) improves model performance and 2) increases the model’s linguistic awareness. This paper aims at answering both questions with a case study on the NLI task. We start by analyzing whether and how infusing SRL information helps BERT learn linguistic knowledge. We compare model performance on two benchmark datasets, SNLI and MNLI. We also conduct in-depth analysis on two probing datasets, Breaking NLI and HANS, which contain abundant examples where SRL information is expected to be helpful. We found that combining SRL representations with BERT representations does outperform BERT-only representations in general, with better awareness in lexical meaning and world knowledge but not in logic knowledge. We also found that infusing SRL information via predicate-wise concatenation with BERT word representations followed by an interaction layer is more effective than sentence-wise concatenation. 1
DisLog: A Separation Logic for Disentanglement

Disentanglement is a run-time property of parallel programs that facilitates task-local reasoning about the memory footprint of parallel tasks. In particular, it ensures that a task does not access any memory locations allocated by another concurrently executing task. Disentanglement can be exploited, for example, to implement a high-performance parallel memory manager, such as in the MPL (MaPLe) compiler for Parallel ML. Prior research on disentanglement has focused on the design of optimizations, either trusting the programmer to provide a disentangled program or relying on runtime instrumentation for detecting and managing entanglement. This paper provides the first static approach to verify that a program is disentangled: it contributes DisLog, a concurrent separation logic for disentanglement. DisLog enriches concurrent separation logic with the notions necessary for reasoning about the fork-join structure of parallel programs, allowing the verification that memory accesses are effectively disentangled. A large class of programs, including race-free programs, exhibit memory access patterns that are disentangled "by construction". To reason about these patterns, the paper distills from DisLog an almost standard concurrent separation logic, called DisLog+. In this high-level logic, no specific reasoning about memory accesses is needed: functional correctness proofs entail disentanglement. The paper illustrates the use of DisLog and DisLog+ on a range of case studies, including two different implementations of parallel deduplication via concurrent hashing. All our results are mechanized in the Coq proof assistant using Iris.
Unsupervised Domain Adaptation via Minimized Joint Error

Unsupervised domain adaptation transfers knowledge from a fully labeled source domain to a different target domain, where no labeled data are available. Some researchers have proposed upper bounds for the target error when transferring knowledge. For example, Ben-David et al. (2010) established a theory based on minimizing the source error and distance between marginal distributions simultaneously. However, in most research, the joint error is ignored because of its intractability. In this research, we argue that joint errors are essential for domain adaptation problems, particularly when the domain gap is large. To address this problem, we propose a novel objective related to the upper bound of the joint error. Moreover, we adopt a source/pseudo-target label-induced hypothesis space that can reduce the search space to further tighten this bound. To measure the dissimilarity between hypotheses, we define a novel cross-margin discrepancy to alleviate instability during adversarial learning. In addition, we present extensive empirical evidence showing that the proposed method boosts the performance of image classification accuracy on standard domain adaptation benchmarks.
Quantifying the effects of the microphysical properties of black carbon on the determination of brown carbon using measurements at multiple wavelengths

Abstract. Methods based on the absorption Ångström exponent (AAE) are widely used to estimate the absorption by brown carbon (BrC), and the estimated absorption by BrC can be significantly different from 0, even for pure black carbon (BC). However, few studies have systematically quantified the effects of BC microphysical properties. Moreover, the conditions under which AAE-based methods are applicable are still unclear. In this work, we used BC models partially coated with non-absorbing materials to calculate the total absorption. Since the total absorption is entirely due to BC, the estimated BrC absorption should be 0 if the retrieval methods are accurate. Thus, the ratio of the estimated BrC absorption to BC absorption (ABSBrC) should be the proportion of the BC absorption that is incorrectly attributed to BrC. The results show that a BC AAE of 1 can generally provide reasonable estimates for freshly emitted BC, since ABSBrC is generally in the range of −4.8 % to 2.7 % during that period. However, when BC aerosols are aged, ABSBrC can sometimes reach about 38.7 %. The wavelength dependence of the AAE (WDA) method does not necessarily improve the estimates; sometimes a negative ABSBrC of about −40.8 % is found for partially coated BC. By combining simulations of a global chemical transport model, this work also quantified the effects of BC microphysical properties on BrC global aerosol absorption optical depth (AAOD) estimates. The AAE = 1 method sometimes leads to a misassigned global mean AAOD of about −0.43–0.46×10-3 if the BC aerosols have a complex morphology. The WDA method does not necessarily improve the estimates. In our cases, the WDA methods based on spherical models could lead to a global-mean misassigned AAOD range of about −0.87–0.04×10-3. At the regional scale, the AAE = 1 method sometimes leads to a distributed AAOD of about −7.3 to 5.7×10-3 in some specific regions. Mie-theory-based WDA methods lead to an estimated AAOD error of about -22×10-3 in some regions (e.g., East Asia). This work also showed that the misattributed BrC absorption would lead to substantial uncertainties in the estimation of the global direct radiative forcing (DRF) of absorbing aerosols from different sources.
CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations

Abstract. CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI (Spinning Enhanced Visible and InfraRed Imager), was released in December 2022. It is based on observations from SEVIRI, on board geostationary satellites Meteosat-8, 9, 10 and 11, which are operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). CLAAS-3 was produced and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), which aims to provide high-quality satellite-based data records suitable for climate monitoring applications. Compared to previous CLAAS releases, CLAAS-3 is expanded in terms of both temporal extent and cloud properties included, and it is based on partly updated retrieval algorithms. The available data span the period from 2004 to present, covering Europe; Africa; the Atlantic Ocean; and parts of South America, the Middle East and the Indian Ocean. They include cloud fractional coverage, cloud-top height, phase (liquid or ice) and optical and microphysical properties (water path, optical thickness, effective radius and droplet number concentration), from instantaneous data (every 15 min) to monthly averages. In this study we present an extensive evaluation of CLAAS-3 cloud properties, based on independent reference data sets. These include satellite-based retrievals from active and passive sensors, ground-based observations and in situ measurements from flight campaigns. Overall results show very good agreement, with small biases attributable to different sensor characteristics, retrieval/sampling approaches and viewing/illumination conditions. These findings demonstrate the fitness of CLAAS-3 to support the intended applications, which include evaluation of climate models, cloud characterisation and process studies focusing especially on the diurnal cycle and cloud filtering for other applications. The CLAAS-3 data record is publicly available via the CM SAF website at https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V003 (Meirink et al., 2022).
Modal Numerical Analysis of Helicopter Rotor Sample Using Holzer-Myklestad Method

Since rotor system of helicopters is responsible for producing lift forces and thrust, analyzing their vibrations is very essential. This research describes a method applied for developing a computer program to analyze coupled vibration of helicopter rotor. Natural frequency and rotor blade mode shapes were analyzed by MATLAB software. In-plane, out-of-plane coupled and torsion vibration were also considered in this analysis. First, Myklestad method, which is one of the most accurate ones to calculate vibration, was used to find governing equations of rotor vibrations. Based on governing equation, vibration code was developed by MATLAB software. After that for validating the program, the obtained results were compared with numerical results using PATRAN software, with maximum 4.264% error. In these problems, three beams in different geometric and material conditions with clamped end were defined. The innovation of this research is developing a MATLAB code to calculate the coupled natural frequencies and mode shapes of helicopter blade.
Towards Fine-Grained, High-Coverage Internet Monitoring at Scale

The massiveness of the Internet makes it rather difficult to achieve high-coverage monitoring at scale with reasonable overhead. The traditional wisdom for scalable and high-coverage Internet monitoring is to consider clients in each /24 as a whole and only monitor the representatives, either by active probing or by passive traffic sniffing, such that performance of the rest can be predicted for high coverage. There are two basic assumptions behind this traditional wisdom: 1) clients in the same /24 have similar performance, and 2) tracking all targeted /24s equates to full-coverage monitoring. With the increasing prevalence of load balancing, both assumptions are now questionable. Through large-scale measurements, we evaluate the coverage and predictability issues of current practices, motivate the necessity of link-level fine-grained, high-coverage monitoring, and present new insights on how to achieve it. Our key findings are: 1) the current practices using the representatives of /24s may fail to capture the changes of up to 85% of links in the Internet; 2) the path difference between client flows to the same /24 is both significant and prevalent; 3) it is possible to cover most of the visible links from DCs to both small and large prefixes by carefully choosing client flows; 4) high-coverage monitoring can be achieved with at least three times less overhead than direct link monitoring.
BARRIERS AND ENABLERS OF SCREENING AND BRIEF INTERVENTION PROGRAM IMPLEMENTATION FOR PATIENTS WITH ALCOHOL-RELATED INJURIES IN A SOUTH AFRICAN TRAUMA CENTRE: VIEWS OF HEALTH CARE PROVIDERS

Despite high levels of alcohol-related injury in South Africa, there are no screening and brief intervention (SBI) programs in any hospital trauma centres. We conducted semi-structured interviews with 16 trauma centre staff (8 doctors, 7 nurses, 1 social worker) focused on barriers and enablers to the future implementation of a SBI program. Data were analysed using thematic analysis. Perceived barriers included time constraints and injury treatment as priority, with the central barrier being inadequate funding. Enablers included the support of senior staff, and training. These findings can be used to support the successful implementation of a SBI program in South African trauma centres, with the purpose of reducing high rates of risky alcohol use and related injury recidivism.
Mid-Term report A ROS 2 Interface for the UMI-RTX robotic arm

The aim of this project is to work with an old robotic arm called the UMI-RTX (created in the 1980’s) and make it grab objects on a plane with its gripper. Some work has already been done on this robot by students, but mainly with old tools. The idea of our project is to implement a new way of making it work and to use more recent tools. More specifically, our goal is to set up a ROS 2 environment and build an interface that will allow us to perform image analysis, trajectory planning, and target grabbing. We have chosen a plush banana as a target. With the computer vision library OpenCV, we managed to detect this banana with our camera, in a dedicated ROS 2 node, and to get the coordinates of its centroid. Besides, we managed to calibrate our stereo camera, the ZED M module, and begin the depth estimation needed to locate the target in a 3D space. Once this is done, this information is sent into our ROS 2 architecture, more specifically to a node dedicated to inverse kinematics. In this node, the joints’ states required to reach the aimed pose are processed and sent both to a simulation and the real arm. For this, we have two nodes, each dedicated to its own part, one for the simulation, the other for the real arm. Finally, we designed a custom Graphic User Interface (GUI) in which the simulation and the processed image are integrated, and in which we are able to choose between automatic control of the arm and a manual mode, where we can choose our own target’s position.
Human-computer co-creativity interface with Generative Deep Learning

Generative Adversarial Networks (GANs) possess great potential regarding artistic content generation, such as paintings, provided that they’re trained with the appropriate datasets. However, they’re still not as accessible to artists given the hardware and technical knowledge requirements to train and execute a model. This work introduces an interactive interface which allow artists to use pre-trained models whithin a certain category of artworks and use their own sketches as inputs in order to create a final piece with the artist’s linework and the model’s style. Besides, they can perform a style transfer of the final result to another available style, if desired.
A Conversation with CHCCS 2021

The 2022 CHCCS Achievement Award from the Canadian Human Computer Communications Society is presented to Professor Pourang Irani (UBC Okanagan) for his impact to the field of human-computer interaction. His research interests sit at the cross-section of MobileHCI and Information Visualization, and lately include the design and evaluation of in-situ user interfacese for improve sense-making. CHCCS invites a publication by the award winner to be included in the proceedings, and this year we continue the tradition of an interview format rather than a formal paper. This permits a casual discussion of the research areas, insights, and contributions of the award winner. What follows is an edited transcript of a conversation between Pourang Irani and Xing-Dong (Associate Professor at Simon Fraser University) that took place on April 20th, 2022.
Narrator or Character: Voice Modulation in an Expressive Multi-speaker TTS

Current Text-to-Speech (TTS) systems are trained on audio-book data and perform well in synthesizing read-style speech. In this work, we are interested in synthesizing audio stories as narrated to children. The storytelling style is more expressive and requires perceptible changes of voice across the narrator and story characters. To address these challenges, we present a new TTS corpus of English audio stories for children with 32.7 hours of speech by a single female speaker with a UK accent. We provide evidence of the salient differences in the suprasegmentals of the narrator and character utterances in the dataset, motivating the use of a multi-speaker TTS for our application. We use a fine-tuned BERT model to label each sentence as being spoken by a narrator or character that is subsequently used to condition the TTS output. Experiments show our new TTS system is superior in expressiveness in both A-B preference and MOS testing compared to reading-style TTS and single-speaker TTS.
Evaluation of the Usage of Wafer Waste as an Easily Soluble Carbohydrate Source in Alfalfa Silage

: This study examined the addition of wafer waste as a readily soluble carbohydrate source to alfalfa silage for its impa ct on silage quality, fermentation characteristics, in vitro organic matter digestion, and in vitro CH 4 values. Fresh alfalfa was ensiled with 0% wafer waste (Control), 1% wafer waste, 2% wafer waste, 4% wafer waste, and 6% wafer waste for 60 days. The differences between the groups in dry matter (DM), crude protein (CP), crude ash (CA), neutral detergent f iber (NDF), acid detergent fiber (ADF), metabolizable energy (ME), in vitro organic matter digestion (IVOMD), and in vitro CH 4 values of silages were found to be statistically significant. In comparison to the control group, increases in IVOMD and ME value s were seen in all additive - containing groups. Depending on the quantity of wafer waste, the pH, ammonia nitrogen (NH 3 - N/TN), and carbon dioxide (CO 2 ) values of the silages declined. In the study, the highest amount of lactic acid occurred in the group with 6% wafer waste added. Propionic acid (PA) was detected only in the control group. Butyric acid (BA) values tended to decrease depending on the addition of wafer waste and were not found in the groups to which 4% and 6% wafer waste addition. Yeast and mold values of the silages decreased in parallel with the increase in the wafer waste rate. As a result, it was determined that adding wafer waste as an easily soluble carbohydrate source positively affected alfalfa silage quality and fermentation characteristics.
Dynamic changepoint detection in count time series: a particle filter approach

ABSTRACT We study Bayesian dynamic models for detecting changepoints in count time series that present structural breaks. As the inferential approach, we develop a parameter learning version of the algorithm proposed by Chopin [Chopin N. Dynamic detection of changepoints in long time series. Annals of the Institute of Statistical Mathematics 2007;59:349–366. ], called the Chopin filter with parameter learning, which allows us to estimate the static parameters in the model. In this extension, the static parameters are addressed by using the kernel smoothing approximations proposed by Liu and West [Liu J, West M. Combined parameters and state estimation in simulation-based filtering. In: Doucet A, de Freitas N, Gordon N, editors. Sequential Monte Carlo methods in practice. New York: Springer-Verlag; 2001]. The proposed methodology is then applied to both simulated and real data sets and the time series models include distributions that allow for overdispersion and/or zero inflation. Since our procedure is general, robust and naturally adaptive because the particle filter approach does not require restrictive specifications to ensure its validity and effectiveness, we believe it is a valuable alternative for dealing with the problem of detecting changepoints in count time series. The proposed methodology is also suitable for count time series with no changepoints and for independent count data.
Advances in Machine Learning Explainability to Contextualize Equity Market Sustainability in South Africa During the COVID-19 Era

The Covid 19 pandemic exacerbated global economic and equity market uncertainty globally with as much as a 30 percent decline recorded in Europe and the United States. Despite the overreach of the COVID-19 pandemic, businesses and industries were not adversely affected similarly. The dissimilarity of COVID-19 impact was also in evidence in national stock markets. As such, this research proposes a machine learning method – an artificial neural network (ANN) – to model the prediction of stock returns for large firms trading on the Johannesburg stock exchange (JSE) during the COVID-19 regime. Until the advent of COVID-19, sustainability was a global business agenda. In a novel approach, the current research study was augmented to include environmental (E), social (S), and governmental (G) policy features to capture sustainability value. Accordingly, to efficiently predict stock returns from firms traded on the Johannesburg Stock Exchange (JSE), the current research augmented traditional efforts to include a COVID-19 feature and separate E, S, and G factors. The study also proposes a novel extension to the literature with the implementation of ‘e xplainable ’ artificial intelligence (XAI) to assess the trustworthiness of estimated model results. Overall this study revealed that COVID-19 had a mixed impact on the performance of selected top 40 shares trading at JSE. The importance of the separated ESG factors was also shown. For example, Aspen, a leading African pharmaceutical company, experienced negative elasticities due to COVID-19 and varying impacts attributable to ESG factors.