Miracle-dz/UniFAR-FTUs huggingface dataset:
TC OFFICERS AND NEWSLETTER EDITORS

The COVID-19 pandemic has caused a global public health crisis in the US and many other countries in the world. To combat the coronavirus, many countries have been using mobile phone tools and data sources for COVID-19 surveillance, such as tracking infections, monitoring community spread, and identifying populated areas at risk. However, the use of mobile devices as part of this effort has raised concerns around privacy, specifically location privacy. Our project uses a mobile crowdsourcing-based symptom report to develop a fine-grained COVID-19 vulnerability map. The vulnerability map tracks the number of people with COVID-like symptoms, so that the potential outbreak communities can be identified. This allows valuable healthcare resources to be proactively and dynamically allocated. To address th e users’ privacy concern, we proposed to make a COVID -19 vulnerability map without disclosing the participants’ actual location. To protect the user’s privacy, we used a semi -honest crowdsourcing aggregator. Based on the differentially private geo-indistinguishability, the mobile participants were able to locally perturb their geographic data. With the users’ geographic location masked, we employ a best linear unbiased prediction estimator (BLIP) with spatial smoothing to obtain reliable vulnerability estimates in the areas of interest and construct the map. Furthermore, a federated learning framework that enables multiple crowdsourcing mobile apps to collaboratively derive the vulnerability prediction and construct the fine-grained VMP is developed. The FL-based fine-grained vulnerability map construction leverages a large amount of survey data while at the same time circumventing data privacy regulations that prevent user information from multiple apps to be shared with each other. Simulation results of the proposed FL framework with a long short-term memory (LSTM) neural-network model show a 6% improvement in prediction accuracy over the widely used FedAvg algorithm while at the same time providing user privacy.
Master Computer Science Opinionated content summarization with Transformer architectures

Discussions are more prevalent than ever on online spaces. Automatic summarization can be a tool to provide valuable insights on public opinion and accelerate decision making. Recent advances in abstractive summarization research that leverage pre-trained large language models led to summarizers that generate fluent, human-like abstracts. However, online discussions are an under-investigated domain, mainly due to the lack of big training datasets with human curated summaries. In this research we are investigating the performance of some state-of-the-art summarization models on the online discussion domain and specifically on opinionated content. We focus on two architecture paradigms: Multi–document summarization models and Long–input summarization models. For all the models we are using pre-trained checkpoints on variants of masked language modelling tasks. We fine-tune these models on a dataset curated with comments from the New York Times comment section and their respective human curated summaries as ground truth. Subsequently, we evaluate the fine-tuned models on the test subset of the NYT dataset and another dataset containing comments from The Guardian. Our evaluation is quantitative and we pick a small sample of examples that we examine closer in a qualitative manner. Eventually, we observed that Long-input summarization techniques perform better, however the frequency of highly extractive input, hallucinated output and low rate of combining opinions and views points showcased from all the models, leads us to believe that tailored datasets and methods could be explored in the future.
Heat Roadmap Europe Inputs for Technical Modelling and Policy Recommendations

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An Overview of Pathways Network Analysis of Pendred Syndromic Genes

: Pendred syndrome, mainly labeled as sensorineural hearing loss and goiter, is an autosomal recessive disorder that forms an association to worsen the disease severity. It is the most known and prevalent form of the audiological disease. Some studies showed that the disorder may account for almost 10% of inherited deafness. Like all other disorders, a set of genes are known to characterize the Pendred Syndrome, where each one of them is segregated with the disease according to the families they belong to. The proposed study focusing on exploring the extent of segregation of each gene that is playing a role in the disorder. SLC26, a protein family of ion transporters, is known to play a significant role in this accord. The mutations in the genes mainly SLC26A4 underlie the major anomalies and malfunctioning that characterize the Pendred Syndrome. The biological pathway analysis using different online tools and databases like Ensemble decision analysis, DAVID (Database for Annotation, Visualization, and Integrated Discovery), IPA (Ingenuity Pathway Analysis software ) was performed to see Cellular Enrichment Components and Biological Enrichment Processes followed by gene network analysis, which determines the candidate gene interactions including the ion transporter family genes that are present on the plasma membrane, out of which two genes belonged to the focused protein family SLC26. The proposed study has highlighted the central role of the SLC26 protein family in hearing impairment and hearing loss.
An improved total variation technique for seismic image denoising

Noise muffling of seismic records is a significant step in seismic data processing and it is of great importance in processing and interpreting seismic data subsequently.Based on the analysis of principles of a traditional total variation image denoising model,an improved total variation approach to image denoising with diffusion tensor appended was proposed.Through enhancing the anisotropic filtering of diffusion terms along the orientation of stratigraphic textures in the total variation model,this approach could consistently strengthen the formation continuity along the orientation and control the diffusion state perpendicular to stratigraphic bedding so as to preserve the edge structure information.The actual result of this approach in application to some seismic data from western Sichuan showed that it could effectively remove the noise in seismic images,enhance the continuity of seismic events and increase the signal to noise ratio of seismic data.
Energy-efﬁcient Hardware Acceleration of Shallow Machine Learning Applications

—ML accelerators have largely focused on building general platforms for deep neural networks (DNNs), but less so on shallow machine learning (SML) algorithms. This paper proposes Axiline, a compact, conﬁgurable, template-based generator for SML hardware acceleration. Axiline identiﬁes computational kernels as templates that are common to these algorithms and builds a pipelined accelerator for efﬁcient execution. The dataﬂow graphs of individual ML instances, with different data dimensions, are mapped to the pipeline stages and then optimized by customized algorithms. The approach generates energy-efﬁcient hardware for training and inference of various ML algorithms, as demonstrated with post-layout FPGA and ASIC results.
Oral drug therapy in elderly with dysphagia: between a rock and a hard place!

: Demographic indicators forecast that by 2050, the elderly will account for about one-third of the global population. Geriatric patients require a large number of medicines, and in most cases, these products are administered as solid oral solid dosage forms, as they are by far the most common formulations on the market. However, this population tends to suffer difficulties with swallowing. Caregivers in hospital geriatric units routinely compound in solid oral dosage forms for dysphagic patients by crushing the tablets or opening the capsules to facilitate administration. The manipulation of a tablet or a capsule, if not clearly indicated in the product labeling, is an off-label use of the medicine, and must be supported by documented scientific evidence and requires the patient’s informed consent. Compounding of marketed products has been recognized as being responsible for an increased number of adverse events and medical errors. Since extemporaneous compounding is the rule and not the exception in geriatrics departments, the seriousness and scope of issues caused by this daily practice are probably underestimated. In this article, the potential problems associated with the manipulation of authorized solid oral dosage forms are discussed.
Master Computer Science Dungeons & Firearms: AI-Directing Action Intensity of Procedural Levels

—Experience-Driven Procedural Content Generation is an extension to Procedural Content Generation, meant to personalise generative content to optimise user experience. In this paper, we present G ONDVAAN , a procedural dungeon crawler and shooter game, where dungeons and ﬁrearms are procedurally generated. Our aim was to create an AI-director that could predict how action-intense procedural dungeons will be. We conducted two experiments with 11 and 16 participants respectively. The ﬁrst one focussed on collecting play data for training an AI-director; 289 levels were played. The second one focussed on how certain game metrics are affected by levels of different AI-directed action intensities, as well as a gradual difﬁculty increase; 347 levels were played. We found that the AI-director could successfully present levels of the desired intensity, and that intensity and difﬁculty inﬂuenced certain game metrics.
A Review on Surface Coatings Methods in Modern Materials Engineering

: An overview of surface coating techniques is provided in this abstract, emphasizing their crucial role in modifying and improving material properties. Applying a thin layer of material to a substrate results in surface coating, which modifies surface features while maintaining bulk qualities. The goals include corrosion prevention, increased adhesion, improved wear resistance, and particular functions. There includes discussion of a number of methods, including painting, electroplating, and physical and chemical vapor deposition. Understanding these techniques is essential for improving functionality and performance in various industrial and consumer applications, highlighting the critical function of surface coatings in contemporary materials engineering.
Automated synthesis of mixed-signal ML inference hardware under accuracy constraints

Due to the inherent error-tolerance of machine learning (ML) algorithms, many parts of the inference computation can be performed with adequate accuracy and low power under relatively low precision. Early approaches have used digital approximate computing methods to explore this space. Recent approaches using analog-based operations achieve power-efficient computation at moderate precision. This work proposes a mixed-signal optimization (MiSO) approach that optimally blends analog and digital computation for ML inference. Based on accuracy and power models, an integer linear programming formulation is used to optimize design metrics of analog/digital implementations. The efficacy of the method is demonstrated on multiple ML architectures.
DYNAMIC ESTIMATION OF OBSTACLE POSITION WITH VISION SENSING FOR REACTIVE COLLISION AVOIDANCE OF UAVs

It is very crucial for unmanned aerial vehicles to have autonomous obstacle detection and avoidance capability for their survivability during flight. This paper proposes and validates the application of extended Kalman filter for online obstacle position estimation with a vision based sensor and the usefulness of this information with two recently developed guidance algorithms for collision avoidance. The vision sensor is assumed to continuously sense the environment in front of the vehicle during flight. In case any obstacle is detected, the information from this sensor is then utilized in the filter to estimate the obstacle position online. Simultaneously, the collision cone approach is applied to predict any potential collision in future and, in case of a potential threat, to steer away the vehicle in order to avoid the collision. This is done by first computing a suitable ‘aiming point’ towards which the velocity vector of the vehicle must be aligned as soon as possible and then by using either of two recently proposed guidance laws, namely nonlinear geometric guidance and differential geometric guidance (which are identically same with appropriate gain correlation, but otherwise are different) to achieve this objective. Exhaustive simulation studies show that this overall strategy is fairly successful.
Patient-speci ﬁ c analysis of co-expression to measure biological network rewiring in individuals

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the speci ﬁ c etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quanti ﬁ cation tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are signi ﬁ cantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.
A Novel First Order Dithered Hybrid MASH-EFM with Cancellation Transfer Function Sigma-delta Modulator for Eight-bit Audio DAC

: This work proposes a new architecture named “Dithered hybrid multi-stage noise shaping-error feedback modulator with cancellation transfer function” sigma-delta modulator for audio digital-to-analog (D/A) converter. Multi-stage noise shaping (MASH) architecture possesses noise shaping technique which produces high signal-to-noise ratio with greater stability. Error feedback modulator (EFM) has an advantage of providing greater stability with time varying inputs. The state-of-the-art technology under the name of dithered hybrid MASH-EFM with cancellation transfer function (CTF) sigma-delta modulator provides better noise shaping, high SNR and greater stability than the traditional sigma-delta architecture. MATLAB simulation shows that 1st order Dithered hybrid MASH-EFM SDM with CTF achieves high signal-to-noise ratio (SNR) of 184db over 22kHz bandwidth when oversampling ratio (OSR) is 128, which is approximately equal to the SNR of 2nd order traditional architecture. Other performance parameters like signal-to-quantization noise ratio (SQNR), effective number of bits (ENOB), signal-to-noise and distortion ratio (SNDR) and dynamic range (DR) are also recorded in this work. Noise transfer function (NTF) graphs are also plotted for traditional and proposed architectures.
A Modern Bayesian Approach to Model Updating of Bridges Considering Measurement Uncertainty

: Existing bridges were designed using contemporaneous standards, but increased loading due to increased freight demand must be considered in their ongoing safety management. Finite-Element (FE) modelling provides the structural responses under known loads. However, such models typically include many assumptions about structural behaviour. On the other hand, structural health monitoring (SHM) provides valuable data about the actual structural behaviour and potentially the structural condition. Typically, there are significant differences between the FE model prediction and the SHM measurements. Due to the significant prior engineering knowledge about structural behaviour and performance, an effective way to combine new observations with existing models is using Bayesian updating strategies. This paper reviews the current state of the art of Bayesian updating, which has undergone tremendous developments in the last decade or so. We discuss how engineers can benefit from these developments, specifically for updating structural models based on data through the fusion of prior engineering information and structural health monitoring data. The modern Bayesian workflow is applied to a simple case study of a moving load on a beam. The work illustrates the potential benefits of the approach for updating the performance prediction of structures based on data.
Finnish young adolescents’ digital 1 gaming and physical activity

24 In this cross-sectional study of Finnish adolescents, we investigated the rates of digital 25 game playing and examined the associations between playing a lot of digital games and 26 physical activity or sport club participation. A national representative sample (n = 27 1979) from Finland aged between 11y–15y olds completed a self-report survey in 2022. 28 Analyses were carried out by chi-square tests and logistic regression analyses. More 29 males (74%) than females (26%) played digital games at least once a day. Playing at 30 least daily decreased from 11y (53%), 13y (30%) to 15y (17%) olds. Positive 31 associations were found with playing ball sport simulations and taking part in >4 32 days/week of physical activity or sport club participation (OR = 2.8, CI=1.7–4.4). 33 Negative associations were found between playing a lot of first-person shooter games 34 and sport club participation (OR = 0.6, CI=0.4–0.9). The results imply that 35 representational features in genres may be relevant for their links to physical activity.
Bacterial Biofilms and Ethylenediamine-N,N’-disuccinic acid (EDDS) as Potential Biofilm Inhibitory compound

: Bacterial biofilms are populations of microorganisms that stick to some surfaces and are embedded in self-secreted extracellular polymeric matrixes. These synergistically controlled sessile cells exhibit different proteome than their planktonic counterpart and are often difficult to treat. They make a different mechanism to escape the antimicrobial activity and host defenses. Biofilms on indwelling prosthetic devices are a major problem in the medical field because they could lead to recurrent and systemic infections. Biofilms also result in bio-corrosion in industries and their remediation often causes environmental problems. The goal of this study is to find antimicrobial and biofilm inhibition activity of non-toxic and biodegradable economical iron-chelating compound EDDS against Staphylococcus aureus SA113 and Pseudomonas aeruginosa PAO1 strains. Broth microdilution assay was performed to find the inhibitory concentration of EDDS and biofilm assay performed in the micro-well plate to determine the biofilm inhibitory concentration of EDDS against SA113 and PAO1. The results show that a higher concentration of EDDS is required to suppress the growth of SA113 a gram-positive bacterium compare to gram-negative PAO1 strain. EDDS successfully inhibited the biofilm formation of both SA113 and PAO1 at very low concentration and significant biofilm inhibition (p<0.05) was observed at 0.78 mM and 1.56 mM EDDS concentration for PAO1 and SA113 respectively. Biodegradable ion-chelator EDDS is a good biofilm inhibitor compound for both S. aureus SA113 and P. aeruginosa PAO1 strains.
Analysis of Construction Cost Estimation Ministry Regulation Year 2016 and Year 2022 for Construction Projects

Cost is one of the three most crucial project management components, with the other two being Quality and Time. A Ministry Regulation regulates the calculation of construction work costs in detail in Indonesia. This Ministry Regulation has only undergone changes in 2022, which replaces and fixes several existing rules in the previous Regulation issued in 2016. For brevity, the two Regulations are shortened to regulation 2016 and regulation 2022. This study uses the two Regulations on two different Construction Projects to analyze the different cost calculation results caused by the regulation change. This change must be understood to continuously improve the Ministry Regulation regarding cost estimation. This research method observes each research project's working coefficient, calculates project cost using Regulation 2016 and Regulation 2022 and compares it with contractor/actual price. Based on the calculation, most results show that Regulation 2022 is closer to the actual cost than Regulation 2016. Still, there are some components that even Regulation 2022 must improve, such as floor plate formwork, Ceiling work, plasterwork, floor and coverings work, and other works which have a significant deviation, as can be seen on the paper. In conclusion, regulation 2022 can produce more accurate results, with regulation 2016 often giving a higher cost estimation than regulation 2022. For future research, it is best to analyze another type of construction infrastructure to gain more insight into other improvements .
Technische Universiẗat Chemnitz Sonderforschungsbereich 393 Numerische Simulation auf massiv parallelen Rechnern Sven Beuchler Fast solvers for degenerated problems

In this paper, £nite element discretizations of the degenerated operator −ω(y)uxx − ω(x)uyy = g in the unit square are investigated, where the weight function satis£es ω(ξ) = ξ with α ≥ 0. We propose two multi-level methods in order to solve the resulting system of linear algebraic equations. The £rst method is a multi-grid algorithm with line smoothe r. A proof of the smoothing property is given. The second method is a BPX-like p reconditioner which we call MTS-BPX preconditioner. We show that the upper eigenvalu e bound of the MTS-BPX preconditioned system matrix grows proportionally to the level numb er. Preprint-Reihe des Chemnitzer SFB 393 SFB393/03-04 February 2003 Author’s addresses: Sven Beuchler TU Chemnitz Fakulẗat für Mathematik D-09107 Chemnitz Germany email: sven.beuchler@mathematik.tu-chemnitz.de http://www.tu-chemnitz.de/sfb393/
Cryospheric Chemistry: Fingerprint to Decipher Climate/ Cryospheric Chemistry: Fingerprint to Decipher Climate/ Environmental Changes and Anthropogenic Activities Environmental Changes and Anthropogenic Activities

Abstract Cryospheric chemistry is a new discipline and one of important branch of cryospheric science. Pollutant emissions from anthropogenic activities have greatly altered the status of chemical components in the cryospheric environment since the Industrial Revolution. When this scenario is coupled with the rapid shrinkage of cryosphere under global warming, both anthropogenic activity and global warming have cooperatively influenced the biogeochemical cycling in the cryosphere and even on a global scale, which results in significant effects on climate and environment. In this paper, firstly, the role of cryospheric chemistry in the cryospheric science is introduced, and the discipline framework and research focuses of cryospheric chemistry are summarized. Secondly, the coupling relationship among cryospheric chemistry and climate/environmental changes, and anthropogenic activities, with emphasis on this relationship in the research field of global change, was exemplified. Lastly, we summarize a review and prospect of hot topics in the current research of cryospheric chemistry. The rapid development of cryospheric chemistry will provide important scientific and technological support for tackling the climate and environmental issues that challenge human survival and development.