Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating High-Resolution Weather Models with Deep-Learning Hardware

The next generation of weather and climate models will have an unprecedented level of resolution and model complexity, and running these models efficiently will require taking advantage of future supercomputers and heterogeneous hardware. In this paper, we investigate the use of mixed-precision hardware that supports floating-point operations at double-, single- and half-precision. In particular, we investigate the potential use of the NVIDIA Tensor Core, a mixed-precision matrix-matrix multiplier mainly developed for use in deep learning, to accelerate the calculation of the Legendre transforms in the Integrated Forecasting System (IFS), one of the leading global weather forecast models. In the IFS, the Legendre transform is one of the most expensive model components and dominates the computational cost for simulations at a very high resolution. We investigate the impact of mixed-precision arithmetic in IFS simulations of operational complexity through software emulation. Through a targeted but minimal use of double-precision arithmetic we are able to use either half-precision arithmetic or mixed half/single-precision arithmetic for almost all of the calculations in the Legendre transform without affecting forecast skill.
Stochastic learning control of inhomogeneous quantum ensembles

In quantum control, the robustness with respect to uncertainties in the system's parameters or driving field characteristics is of paramount importance and has been studied theoretically, numerically and experiementally. We test in this paper stochastic search procedures (Stochastic gradient descent and the Adam algorithm) that sample, at each iteration, from the distribution of the parameter uncertainty, as opposed to previous approaches that use a fixed grid. We show that both algorithms behave well with respect to benchmarks and discuss their relative merits. In addition the methodology allows to address high dimensional parameter uncertainty; we implement numerically, with good results, a 3D and a 6D case.
BLIC: A Blockchain Protocol for Manufacturing and Supply Chain Management of ICS

Blockchain technology has brought a huge paradigm shift in multiple industries, by integrating distributed ledger, smart contracts and consensus protocol under the same roof. Notable applications of blockchain include cryptocurrencies and large-scale multi-party transaction management systems. The latter fits very well into the domain of manufacturing and supply chain management for Integrated Circuits (IC), which, despite several advanced technologies, is vulnerable to malicious practices, such as overproduction, IP piracy and deleterious design modification to gain unfair advantages. To combat these threats, researchers have proposed several ideas like hardware metering, design obfuscation, split manufacturing and watermarking. In this paper, we show, how these issues can be complementarily dealt with using blockchain technology coupled with identity-based encryption and physical unclonable functions, for improved resilience against certain adversarial motives. As part of our proposed blockchain protocol, titled ‘BLIC’, we propose an authentication mechanism to secure both active and passive IC transactions, and a composite consensus protocol designed for IC supply chains. We also present studies on the security, scalability, privacy and anonymity of the BLIC protocol.
Large Scale Clustering with Variational EM for Gaussian Mixture Models

How can we efficiently find large numbers of clusters in large data sets with high-dimensional data points? Our aim is to explore the current efficiency and large-scale limits in fitting a parametric model for clustering to data distributions. To do so, we combine recent lines of research which have previously focused on separate specific methods for complexity reduction. We first show theoretically how the clustering objective of variational EM (which reduces complexity for many clusters) can be combined with coreset objectives (which reduce complexity for many data points). Secondly, we realize a concrete highly efficient iterative procedure which combines and translates the theoretical complexity gains of truncated variational EM and coresets into a practical algorithm. For very large scales, the high efficiency of parameter updates then requires (A) highly efficient coreset construction and (B) highly efficient initialization procedures (seeding) in order to avoid computational bottlenecks. Fortunately very efficient coreset construction has become available in the form of light-weight coresets, and very efficient initialization has become available in the form of AFK-MC2 seeding. The resulting algorithm features balanced computational costs across all constituting components. In applications to standard large-scale benchmarks for clustering, we investigate the algorithm’s efficiency/quality trade-off. Compared to the best recent approaches, we observe speedups of up to one order of magnitude, and up to two orders of magnitude compared to the k-means++ baseline. To demonstrate that the observed efficiency enables previously considered unfeasible applications, we cluster the entire and unscaled 80 Mio. Tiny Images dataset into up to 32,000 clusters. To the knowledge of the authors, this represents the largest scale fit of a parametric data model for clustering reported so far.
Is My Game OK Dr. Scratch?: Exploring Programming and Computational Thinking Development via Metrics in Student-Designed Serious Games for STEM

Computational thinking (CT) is key to digital literacy and helps develop problem-solving skills, which are fundamental in modern school. As game design shows potential for teaching CT, metrics like Dr. Scratch emerge that help scholars systematically assess the CT of student-designed games, particularly with Scratch. Compared to other CT metrics, Dr. Scratch scores the CT of Scratch projects automatically and can be used to describe CT development. However, previous research using Dr. Scratch summatively assessed CT, but did not look at CT development. We use Dr. Scratch to assess the CT development of Scratch games designed by 8th-grade students in STEM curricula. We show how CT proficiency in student-designed games develops differently in each CT dimension, where parallelism, synchronization, and logic develop proficiently, while developing abstraction seems hard. We discuss insights into game-based CT development for STEM, and suggest improvements for metric-based CT assessment.
Biological properties of herpes simplex virus 2 replication-defective mutant strains in a murine nasal infection model.

We used a mouse nasal model of herpes simplex virus 2 (HSV-2) infection to examine the biological properties of HSV-2 wild-type (wt), TK-negative, and replication-defective strains in vivo. Nasal septa tissue is the major site of wt viral replication post intranasal (i.n.) inoculation. The HSV-2 strain 186 syn(+)-1 wt virus caused lethal encephalitis at doses of 10(4) PFU and above per nostril, and at lower doses no neurons in the trigeminal ganglia were positive for the latency-associated transcript, indicating a lack of latent infection. The 186DeltaKpn TK-negative mutant virus replicated in nasal septa tissue but showed low-level replication in trigeminal ganglia at only one timepoint. In situ hybridization of trigeminal ganglia showed that the number of LAT-positive neurons was proportional to the inoculum dose from 10(3) to 10(6) PFU per nare. The replication-defective mutant virus 5BlacZ showed no replication in nasal septa tissue and no persistence of viral DNA at the inoculation site or the trigeminal ganglia. Nevertheless, inoculation of 5BlacZ or the double-mutant dl5-29 at distal sites reduced acute replication and latent infection of 186DeltaKpn following intranasal challenge. This infection model provides a biological system to test the properties of HSV-2 strains and shows that replication-defective mutant strains do not persist at sites of inoculation or in sensory ganglia but can induce immune protection that reduces the latent viral load of a challenge virus.
A Cross-Chain Solution to Integration of IoT Tangle for Data Access Management

At present, the Internet of Things (IoT) is rapidly setting off a huge wave of digitalization in traditional industries. In the era of the Internet of Everything, massive data is generated by IoT devices. Recently, Blockchain has attracted more and more attentions in the field of IoT due to its decentralization, traceability, security and so on. Many IoT-oriented data services are built based on distributed ledger technology like blockchains. However, due to the lack of scalability and high transaction costs of the public blockchain, it is hard to directly apply the blockchain technology to IoT scenarios. The recent transaction directed acyclic graph (TDAG) technology, especially Tangle, is suitable for low-latency M2M (Machine to Machine) micropayment transactions in the ever-growing scale of the edge-centric IoT. In this paper, we propose a framework to integrate Tangle into IoT blockchains and build a cross-chain interactive decentralized access model in the context of data management. We employ a consortium blockchain as the control station, and Tangle runs as the backbone of all IoT devices. It is equivalent to opening the off-chain channel of the DAG Tangle structure on the consortium blockchain. Transactions in these channels form the sub-Tangle and then merge sub-Tangle into the main Tangle for confirmation through the notary mechanism. Finally, we implement a prototype of the proposed model and conduct extensive experiments to evaluate its performance. The results clearly demonstrate the effectiveness and efficiency of our framework.
Programs in the Palm of your Hand: How Live Programming Shapes Children's Interactions with Physical Computing Devices

As physical computing devices proliferate, researchers and educators push to make them more engaging to learners. One approach is to make the act of programming them more interactive and responsive via live programming so that program edits are immediately reflected in the behavior of the physical device. To understand the impact of live programming on interactions with physical computing devices, we conducted a comparative study where children ages 11-15 programmed a BBC micro:bit device using either the MicroBlocks live programming environment or MakeCode, the micro:bit default environment. Results show that MicroBlocks users spent more time interacting directly with the physical device while showing different patterns of interaction compared to MakeCode users. We also found variations in the differences between environments related to activity structures. This paper contributes to the growing body of literature on how the design of interfaces---like programming environments---for physical computing devices shapes emerging interaction patterns.
Postantibiotic effect in vitro

The interaction between a microorganism and various physical and chemical treatments is complex in nature. In many cases, bacterial killing seems to be a gradual process, and if the process has not progressed too far the surviving bacteria do not immediately resume multiplication. However, after incubation in a suitable environment, they regain the ability to multiply after a longer lag period as compared with non-treated bacteria. Treatments demonstrated to induce an extended lag before bacterial regrowth include heating, freezing, drying, radiation (Mackey & Derrick, 1982), exposure to the platelet-microbicidal protein (Yeaman et al. 1992) and amiloride, an inhibitor of potassium electrolyte transport (Giunta et al. 1986). The phenomenon with delayed regrowth following short-term exposure to an antimicrobial agent has been termed postantibiotic effect (PAE). Awareness of PAE as a biological phenomenon is not new. In 1944, Bigger noted delayed development in turbidity after adding penicillinase to cultures of staphylococci previously exposed to penicillin (Bigger, 1944). A few years later Parker (Parker & Luse, 1948; Parker & Marsh, 1946) demonstrated that staphylococci briefly exposed to penicillin and transferred to a drug free medium did not resume normal growth for up to three hours. Eagle confirmed and extended these observations with penicillin to staphylococci and streptococci both in vitvu and in vivu (Eagle et al. 1950b; Eagle et al. 1950b; Eagle & Musselman, 1949b; Eagle et al. 1950b). For the next decades PAE was studied by researchers in mycobacteriology. It was observed that most antimycobacterial drugs induced an extended bacterial lag of regrowth (Beggs & Jenne, 1969; Mitchison & Dickinson, 1971; Dickinson, 1968; Dickinson et al. 1968; Dickinson & Mitchison, 1966; Armstrong, 1960). In the 1970’s PAE was “rediscovered” in non-mycobacterial microorganisms (Wilson & Rolinson, 1979; Rolinson et al. 1977; Hunter et al. 1976; McDonald et al. 1977; McDonald et al. 1976) and during the past two decades PAE has been proved in almost every bacteria-drug combination tested. The intention of this review is to present data contributed by the author on PAE in vitro, which will include: methods used for determination of PAE, factors of importance for the presence and duration of PAE, the mechanism of action behind the PAE, and other postexposure events. Finally, considerations are given to the potential clinical significance of PAE.
Together Together: Combining Shared and Separate Activities in Designing Technology for Family Life

There is a growing concern that the pervasive individualized, digital means for connecting with others is challenging family togetherness, such as Sherry Turkle expresses it in her book entitled "Alone Together". As a response to this concern, we explore a new direction for designing technologies supporting families being 'Together Together' through using separate activities as drivers for shared activities in designing for domestic family life. The direction derives from the use of Participatory Design techniques, engaging six families in a series of in-situ workshops, envisioning how technology can support shared domestic family experiences. We explore this design space through the design of the STORIES prototype and we present the results from in-situ deployments of the STORIES prototype. We conclude that designing for a combination of separate and shared family activities is a promising approach in designing for Family Togetherness.
A Dynamic Network Model of the Unsecured Interbank Lending Market

We introduce a structural dynamic network model of the formation of lending relationships in the unsecured interbank market. Banks are subject to random liquidity shocks and can form links with potential trading partners to bilaterally Nash bargain about loan conditions. To reduce credit risk uncertainty, banks can engage in costly peer monitoring of counterparties. We estimate the structural model parameters by indirect inference using network statistics of the Dutch interbank market from 2008 to 2011. The estimated model accurately explains the high sparsity and stability of the lending network. In particular, peer monitoring and credit risk uncertainty are key factors in the formation of stable interbank lending relationships that are associated with improved credit conditions. Moreover, the estimated degree distribution of the lending network is highly skewed with a few very interconnected core banks and many peripheral banks that trade mainly with core banks. Shocks to credit risk uncertainty can lead to extended periods of low market activity, amplified by a reduction in peer monitoring. Finally, our monetary policy analysis shows that a wider interest rate corridor leads to a more active market through a direct effect on the outside options and an indirect multiplier effect by increasing banks' monitoring and search efforts.
ComChain: Bridging the Gap Between Public and Consortium Blockchains

As an alternative to the energy greedy proof-of-work, new blockchains constrain the set of participants whose selection is debatable. These blockchains typically allow a fixed consortium of machines to decide upon new transaction blocks. In this paper, we introduce the community blockchain that bridges the gap between these public blockchains and constrained blockchains. The idea is to allow potentially all participants to decide upon “some” block while restricting the set of participants deciding upon “one” block. We also propose an implementation called ComChain that builds upon the Red Belly Blockchain, the fastest blockchain we are aware of. It runs a consensus among the existing community to elect a new community. This reconfiguration speeds up as the number of removed nodes increases.
Load Balancing in the Fog of Things Platforms Through Software-Defined Networking

The centralization of Internet of Things (IoT) services (data and applications) running in the cloud has been a tendency since the introduction of the concept-design that the IoT enables the orchestration/management of a huge amount of Internet connected objects. However, the adoption of this conceptual design imposes deployment limitations as high latency and high bandwidth demands. This paper presents an approach to the need of executing load balancing for the Fog of Things Platforms via programmability for distributed Internet of Things environments through the use of Software-Defined Networks (SDN). In this approach each IoT Gateway is responsible for optimally managing a set of sensors through IoT managment Services by reducing the number of failures or overload network link(s) associated with an IoT Gateway. Failure on the IoT Gateway itself due to processing overhead may result in temporary or even permanent unavailability in IoT applications (Web, Mobile, and Desktop) that demand information provided by those devices. We address these problems using Fog of Things load balancing approach and here we present our findings, deployed and implemented progress and experiments results.
Non-Invasive Sleeping Posture Recognition and Body Movement Detection Based on RFID

Some sleep disorders, such as sleep apnea, restless legs syndromes (RLS), and periodic limb movement disorder (PLMD), require a full-night sleep monitoring for diagnosis. Conventional sleep monitoring devices are disturbing and often raise privacy concerns. In this paper, we propose a sleep monitoring system by embedding RFID tags into bed cloth and realize two main functions: sleeping posture recognition and body movement detection. We apply a finite impulse response low pass filter to get smooth signal wave and use a CNN algorithm to identify the sleeping postures of person objects. Meanwhile, the movements and their durations can also be detected by using k-means algorithm. Finally, we conduct experiments to evaluate the both two functions in a real world scenario.
Does CLIL work for Russian higher school students?: The Comprehensive analysis of Experience in St-Petersburg Peter the Great Polytechnic University

The article is devoted to the realization of the idea of content and language integrated learning in the system of higher professional education and analysis of students' motivation level in general educational process, difference in the level of professional disciplines knowledge and difference in the level of English language knowledge between CLIL and non-CLIL students after the course. In the St. Petersburg Polytechnic University there was conducted an experiment to receive overall assessment of using CLIL in curriculum of "Advertising and PR" department. CLIL was introduced in one of the professional disciplines -- International business. In this paper, attainments of CLIL and non-CLIL learners are examined and compared. All experiment tests were conducted through the online platform Moodle that was developed for the purposes of the St. Petersburg Polytechnic University.
A Serious Videogame to Support Emotional Awareness of People with Down Syndrome

Among the characteristics of people with Down syndrome is the difficulty in identifying and expressing their own emotions. This difficulty stems from the lack of development of their emotional awareness which can cause them anxiety, and depression when they are exposed to significant situations in their lives, and they do not have the skills to express their emotions. In the literature, several works focus on using serious videogames to support motor skills and literacy of children with Down syndrome. These show that the videogames maintain children's attention and motivate them to develop and practice the corresponding skills. However, research exploring serious videogames to support emotional awareness of children with Down syndrome is scarce. In this work, we present the design of a serious videogame for supporting the emotional awareness of people with Down together with the design process followed.
The Glueball Superpotential

We compute glueball superpotentials for four-dimensional, N=1 supersymmetric gauge theories, with arbitrary gauge groups and massive matter representations. This is done by perturbatively integrating out massive, charged fields. The Feynman diagram computations simplify, and are related to the corresponding matrix model. This leads to a natural notion of ``projection to planar diagrams'' for arbitrary gauge groups and representations. We discuss a general ambiguity in the glueball superpotential W(S) for terms, S^n, whose order, n, is greater than the dual Coxeter number. This ambiguity can be resolved for all classical gauge groups, (A,B,C,D), via a natural embedding in an infinite rank supergroup. We use this to address some recently raised puzzles. For exceptional groups, we compute the superpotential terms for low powers of the glueball field and propose an all-order completion for some examples including N=1^* for all simply-laced groups. We also comment on compactification of these theories to lower dimensions.
Concept Extraction: A Modular Approach to Extraction of Source Code Concepts

Code examples have always been one of the most sought after pieces of information when it comes to understanding and mastering programming concepts. Research shows extracting knowledge from such examples in any online tutoring system is a challenging task. Current methods rely upon specifically formed regular expressions that must be tailor made to the input language, or generation of an AST for the given input program. In our paper, we extend upon existing implementations in code recommendation software using a novel keyword based search tree (k-BST) method. K-BST recommends relevant code fragments by extracting existing keywords, matching with relevant coding examples by k-means clustering, and recommending the relevant coding examples back to the user. K-BSTs also address several major issues which modern knowledge extraction software often run into, like ease of use, extendibility to other domains and run time. With that in mind, K-BSTs are designed to tackle ease of use with popular recognizable file formats such as CSV while keeping the run time of extracting relevant keywords to be extremely low (compared to the more popular method that uses AST).