Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to build physical structures better over time

Our ability to plan and build a wide array of physical structures, from sand castles to skyscrapers, is a defining feature of modern human intelligence. What cognitive tools enable us to create such complex and varied structures? Here we investigate how practice “reverse-engineering” a set of physical structures impacts the procedures that people subsequently use to build those structures, as well as how well they build them over time. Participants (N=105) viewed 2D silhouettes of 8 unique block towers in a virtual environment simulating rigid-body physics, and aimed to reconstruct each one in less than 60 seconds. We found that people learn to build each tower more accurately and quickly across repeated attempts, and that these gains reflect both group-level convergence upon a smaller set of viable policies, as well as error-dependent updating of each individual’s strategies. Taken together, our study provides novel insight into how humans learn from prior experience to discover better solutions to physical reasoning problems over time.
Complexed Iron Removal from Groundwater

The paper demonstrates an intensive work carried out and results obtained on the pilot plant of the City of Kogalym Water Treatment Station (Tjumen, Siberia, Russian Federation) to elaborate on a contemporary nonreagent treatment technology for the local iron-rich groundwater. Several filter materials (Birm, Pyrolox, hydroanthracite, Everzit, granulated activated carbon) and chemical oxidants (ozone, chlorine, hydrogen peroxide, oxygen, and potassium permanganate) were tested to solve the problem with complexed iron removal from groundwater. The final elaborated technology consists of raw water intensive aeration in the gas-degas treatment unit followed by sequential filtration through hydroanthracite and the special anthracite Everzit.
The Dynamics of Architecture-Governance Configurations: An Assemblage Theory Approach

Research on digital infrastructures and platforms studies large-scale systems that are characterized by constant evolution, loosely defined boundaries, and growing complexity. This research demonstrates that evolution is driven by tensions (between stability and change), which are in turn determined by the systems’ architecture and governance structures. This paper argues that architecture and governance are intrinsically related and conceptualizes them as a unified entity that we call an architecture-governance (A-G) configuration. We focus on the dynamics of A-G configurations—i.e., how architecture and governance interact and, in combination, shape the evolution of digital infrastructures, while, at the same time, change as emergent outcomes of the evolution of infrastructures. Toward this end, this paper applies assemblage theory as a lens for conducting a longitudinal study on an electronic prescription infrastructure. We identify three overall A-G configurations corresponding to different phases of the evolution of the infrastructure. This paper makes three contributions. First, we theorize the A-G configuration as an intertwined intermediate-scale entity that represents the form of the infrastructure and simultaneously constitutes an assemblage in its own right. Second, we demonstrate how an A-G configuration and its infrastructure coevolved through a series of interacting stabilization and destabilization processes operating within and across levels. Finally, we argue that tensions driving the evolution of infrastructures are also dynamic and that, accordingly, the focus of study should be on the processes of stabilization and destabilization rather than on stability and change themselves.
Electrochemical and photophysical characterization of non-peripherally-octaalkyl substituted dichlorotin(IV) phthalocyanine and tetrabenzotriazaporphyrin compounds

Three non-peripherally substituted tin(IV) macrocylic compounds, octahexylphthalocyaninato dichlorotin(IV) (3a), octahexyltetrabenzo-5,10,15- triazaporphyrinato dichlorotin(IV) (3b) and octadecylphthalocyaninato dichlorotin(IV) (3c) were synthesized and their photophysical and electrochemical behavior studied. Complex 3b, containing a CH group in place of one of the aza nitrogen atoms of the phthalocyanine core, shows a split Q-band due to its lower symmetry. The triplet state quantum yields were found to be lower than would be expected on the basis of the heavy atom effect of tin as the central metal for phthalocyanine derivatives (3a and 3c). In contrast, 3b shows a triplet quantum yield ΦT = 0.78. The triplet state lifetimes were solvent dependent, and were higher in tetrahydrofuran than in toluene. Cyclic voltammetry and spectroelectrochemistry of the complexes revealed only ring-based redox processes.
DRCCTPROF: A Fine-Grained Call Path Profiler for ARM-Based Clusters

ARM is an attractive CPU architecture for exascale systems because of its energy efficiency. As a recent entry into the HPC paradigm, ARM lags in its software stack, especially in the performance tooling aspect. Notably, there is a lack of fine-grained measurement tools to analyze fully optimized HPC binary executables on ARM processors. In this paper, we introduce DRCCTPROF — a fine-grained call path profiling framework for binaries running on ARM architectures. The unique ability of DRCCTPROF is to obtain full calling context at any and every machine instruction that executes, which provides more detailed diagnostic feedback for performance optimization and correctness tools. Furthermore, DRCCTPROF not only associates any instruction with source code along the call path, but also associates memory access instructions back to the constituent data object. Finally, DRCCTPROF incurs moderate overhead and provides a compact view to visualize the profiles collected from parallel executions.
Physical and biological activities of newly designed, macro-pore-structure-controlled 3D fibrous poly(ε-caprolactone)/hydroxyapatite composite scaffolds

Electrospun poly(e-caprolactone) micro/nanofibers have been used widely for tissue regeneration. However, their low three-dimensional (3D) shape-ability, difficult macro-pore-structure controllability, and relatively low bioactivities have been major obstacles to their use as various tissue regenerative scaffolds. In this study, we present a new 3D fibrous scaffold in which the macrosized pore structure was manipulated using an electrohydrodynamic jet process supplemented with in vitro mineralization to obtain a hydroxyapatite (HA) layer in simulated body fluid (SBF). The fabricated scaffolds were a mesh-like structure (average diameter of fiber bundles: 342 ± 36 μm; average diameter of microfiber: 8.5 ± 1.2 μm) with high porosity (>91%), macro-pore size (368 ± 16 μm), and 100% interconnected pores. The HA particles on the surface of the scaffolds were well developed after a 7 day incubation in SBF, and the 3D fibrous scaffolds showed markedly higher and more homogenous HA deposition compared with that of control scaffolds fabricated with solid free-form fabrication, due to the larger surface area of the fibrous bundles. The mineralized scaffolds were analyzed in terms of various physical and biological properties (water and protein absorption, mechanical properties, cell viability, DAPI/phalloidin staining, and osteogenic gene expression). Protein and water absorption of the fibrous scaffolds at 12 h were ∼2.5-fold and ∼2.3-fold greater than those of the control scaffolds. Additionally, proliferation of viable cells (MC3T3-E1 pre-osteoblasts) on the 3D fibrous scaffold improved significantly compared to the control. The osteogenic gene expression (alkaline phosphatase activity and calcium mineralization) levels of the fibrous scaffolds were significantly enhanced compared with those of the control scaffolds.
An Efficient and Non-Intrusive GPU Scheduling Framework for Deep Learning Training Systems

Efficient GPU scheduling is the key to minimizing the execution time of the Deep Learning (DL) training workloads. DL training system schedulers typically allocate a fixed number of GPUs to each job, which inhibits high resource utilization and often extends the overall training time. The recent introduction of schedulers that can dynamically reallocate GPUs has achieved better cluster efficiency. This dynamic nature, however, introduces additional overhead by terminating and restarting jobs or requires modification to the DL training frameworks.We propose and develop an efficient, non-intrusive GPU scheduling framework that employs a combination of an adaptive GPU scheduler and an elastic GPU allocation mechanism to reduce the completion time of DL training workloads and improve resource utilization. Specifically, the adaptive GPU scheduler includes a scheduling algorithm that uses training job progress information to determine the most efficient allocation and reallocation of GPUs for incoming and running jobs at any given time. The elastic GPU allocation mechanism works in concert with the scheduler. It offers a lightweight and nonintrusive method to reallocate GPUs based on a “SideCar” process that temporarily stops and restarts the job’s DL training process with a different number of GPUs. We implemented the scheduling framework as plugins in Kubernetes and conducted evaluations on two 16-GPU clusters with multiple training jobs based on TensorFlow. Results show that our proposed scheduling framework reduces the overall execution time and the average job completion time by up to 45% and 63%, respectively, compared to the Kubernetes default scheduler. Compared to a termination based scheduler, our framework reduces the overall execution time and the average job completion time by up to 20% and 37%, respectively.
An End-to-End BLE Indoor Localization Method Using LSTM

: This paper proposes an indoor localization method for Bluetooth Low Energy (BLE) devices using an end-to-end LSTM neural network. We focus on a large-scale indoor space where there is a tough environment for wireless indoor localization due to signal instability. Our proposed method adopts end-to-end localization, which means input is a time-series of signal strength and output is the estimated location at the latest time in the input. The neural network in our proposed method consists of fully-connected and LSTM layers. We use a custom-made loss function with 3 error components: MSE, the direction of travel, and the leap of the estimated location. Considering the di ﬃ culty of data collection in a short preparation term, the data generated by a simple signal simulation is used in the training phase, before training with a small amount of real data. As a result, the estimation accuracy achieves an average of 1.92m, using the data collected in GEXPO exhibition in Miraikan, Tokyo. This paper also evaluates the estimation accuracy assuming the troubles in a real operation.
Determination of N2(A 3Σu+) metastable density produced by nitrogen streamers at atmospheric pressure: 2. Experimental verification

A newly developed spectrometric method presented in a previous paper was applied to determine absolute densities of the N2(A 3Σu+) metastables produced by nitrogen streamers at atmospheric pressure. Primary streamers were generated in high-purity nitrogen with quantified NO traces employing very short high-voltage pulses in a coaxial wire–cylinder electrode geometry. The post-discharge formation of the N2(C 3Πu, C'' 5Πu), states was tracked and the evolution of the N2(A 3Σu+) species was inferred through the time dependence analysis of band intensities of the second positive N2(C 3Πu→B 3Πg) and Herman infrared N2(C'' 5Πu→{A}' 5Σg+) systems. The post-discharge evolution of the NO(A 2Σ+) state was monitored through the band intensities of the NO-γ(A 2Σ+→X 2Π) system. The concentration of N2(A 3Σu+) metastables was determined by analysing the time evolutions and relative intensities of NO and N2 emissions driven by pooling and resonant energy transfer reactions. The highest N2(A 3Σu+) concentrations (~5 × 1014 cm−3) were found close to the central wire anode during early post-discharge time (~1 μs).
Extra-atomic relaxation and X-ray spectra of narrow-band metals. II. Results†

The results of a numerical analysis of the effect of the hole potential on the form of the X-ray spectra of narrow band metals are given. The dependence of the spectra on the magnitude of conduction band filling as well as on the magnitude of intratomic interaction between conduction band electrons and the hole is investigated. It is shown that when the band is filled, the emission and absorption spectra vary from a form of the spectra, which occurs if the hole potential is engaged adiabatically, to a form where the interaction with the hole is not taken into account. In the first case the spectra reflect the local impurity density, and in the second one the density of states of the unperturbed matrix. If the hole potential is strong enough, then the formation of a local level is possible. The most interesting consequence of this is the presence of two lines in core XPS. The systematic change is recorded of the intensities of these lines as the band is filled. The results of the investigation are drawn into a qualitative interpretation of the width of emission bands and of the shift of the inner lines of d-metals. #Es werden Ergebnisse einer numerischen Analyse des Einflusses des Lochpotentials auf die Form von Rontgenspektren von Schmalbandmetallen angegeben. Die Abhangigkeit der Spektren sowohl vom Wert der Leitungsbandbesetzung als auch von der Grose der interatomaren Wechselwirkung zwischen Leitungsbandelektronen und Lochern wird untersucht. Es wird gezeigt, das die Fullung der Bander die Emissions- und Absorptionsspektren sich von der Spektrenform, die dunn auftritt, wenn das Lochpotential adiabatisch eingeht, in eine Form andert, wo Wechselwirkung mit dem Loch nicht berucksichtigt wird. Im ersten Fall spiegeln die Spektren die lokale Storstellendichte, und im zweiten die Zustandsdichte der ungestorten Matrix wider. Wenn das Lochpotential stark genug ist, ist die Bildung eines lokalen Niveaus moglich. Die interessanteste Konsequenz ist die Anwesenheit von zwei Linien in den Rumpf-Rontgenphotospektren. Die systematische Anderung der Intensitaten dieser Linien, wenn das Band gefullt wird, wird aufgezeichnet. Die Ergebnisse der Untersuchung werden in eine qualitative Interpretation der Breite der Emissionsbanden und der Verschiebung der inneren Linien der d-Metalle einbezogen.
Whole grain food intake elevates serum enterolactone

Both intake of whole grain and higher levels of serum enterolactone have been related to reduced risk for CHD and some cancers. Because lignans are prevalent in the outer layers of grains, these findings may be related. We carried out a crossover feeding study in which overweight, hyperinsulinaemic, non-diabetic men (n 5) and women (n 6) ate, in random order, wholegrain foods or refined-grain foods in a diet with 30% energy from fat. The dominant whole grain was wheat, followed by oats and rice. All food was supplied by the investigators and each diet lasted for 6 weeks, with an intervening washout period of 6–9 weeks. Serum enterolactone concentrations were higher when eating the wholegrain than the refined-grain diet by 6·2 (within person SE 1·7) nmol/l (P=0·0008). Most of the increase in serum enterolactone when eating the wholegrain diet occurred within 2 weeks, though the serum enterolactone difference between wholegrain and refined-grain diets continued to increase through 6 weeks. Serum enterolactone concentrations can be raised by eating a diet rich in whole grains.
TRIAL FOR MAKING THREE DIMENSIONAL PZT CAPACITOR FOR HIGH DENSITY FERROELECTRIC RANDOM ACCESS MEMORY

ABSTRACT Three dimensional capacitors of Pb(Zr,Ti)O3 [PZT] together with the Ru electrodes were tried to deposit by metal organic chemical vapor deposition for realizing high density ferroelectric random access memory (FeRAM). Good conformability was obtained for both of CVD-Ru and PZT layers on the trenched substrates by optimizing the deposition conditions. In addition, PZT films deposited on Ru bottom electrode showed the good compositional conformability. Finally we demonstrated the Ru/PZT/Ru three dimensional capacitor. These data accelerate the development of three dimensional PZT capacitors.
EvoMaster: A Search-Based System Test Generation Tool

Testing web/enterprise applications is complex and expensive when done manually. Often, software testing takes up to half of the development time and cost for a system. So much testing is needed because the cost of software failure is simply too large: for example, in 2017, 304 software failures (reported in the media) impacted 3.6 billion people and $1.7 trillion in assets worldwide (Tricentis, 2017). Unfortunately, due to its high cost, software testing is often left incomplete, and only applied partially. To address this problem, in Software Engineering (SE) research a lot of effort has been spent in trying to design and implement novel techniques aimed at automating several different tasks, where software testing is among the most studied tasks. Search-Based Software Testing (SBST) (Harman et al., 2012) casts the problem of software testing as an optimization problem, aimed at, for example, maximizing code coverage and fault detection. Our SBST tool called EvoMaster addresses these challenges by using evolutionary techniques to automatically generate test cases. It currently focuses on RESTful web services, which are the pillars of modern web and enterprise applications (Allamaraju, 2010; Fielding, 2000). The EvoMaster tool is aimed at:
Kaleidoscope

Trigger warning: the following contains disturbing content and it might be harmful for some. We use alerts so that individuals with traumatic histories can avoid unnecessary provocation of symptoms: consider it the psychiatric equivalent of the epilepsy risk warning ‘the following contains flashing images’. This seems sensible, given how avoidance of trauma-resembling or reminiscent situations and distress at their occurrence are part of the post-traumatic stress disorder (PTSD) diagnostic criteria. However, there is a dearth of data to support this approach; Jones et al randomised 451 trauma survivors to receive, or not receive, a trigger warning before reading potentially distressing pieces of literature. These ranged from neutral (for example a character description from Moby Dick) through mildly distressing (for example a battle description from Flags of our Fathers) to markedly distressing (for example the murder scene in Crime and Punishment), and were presented in a randomised order. The warnings made no difference in terms of self-reported emotional reaction or anxiety, even in individuals with PTSD caused by a trauma similar to that read about. Indeed, the authors found that trigger warnings reinforced counter-therapeutic self-views of trauma being a core part of their identity. The wider use of trigger warnings has encompassed debates from curtailment of free speech and academic freedom, through inadequately preparing individuals for the real world, to creating inclusive environments and allowing victims of trauma to make informed decisions about engaging with and preparing for difficult discussions. However, beyond the sociological, the science now suggests that they are useless at best, potentially harmful at worst.
A goal-oriented fuzzy approach for big data analytics adoption in manufacturing systems

Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from big data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This requires re-architecting their manufacturing legacy information systems to enable integration with contemporary big data analytics platforms. A systematic re-architecting approach is required incorporating careful and thorough evaluation of goals for big data adoption. In addition, ameliorating uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing. Objective: We propose an approach to reason about goals, obstacles, and to select suitable big data solution architecture that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in big data solution architecture and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The proposed approach brings two innovations to the state of the art of big data analytics platform adoption in manufacturing systems: (i) A systematic goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with big data analytics platforms at the requirement level and (ii) A systematic analysis of the architectural decisions under uncertainty incorporating stakeholders’ preferences. The efficacy of our approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to big data architecture.
Cloud Based Automatic Building and Road Extraction from Large Scale Open Geospatial Datasets

We author Jupyter notebooks to develop deep learning models on Amazon SageMaker instance. These models automatically extract building footprints and road networks from open geospatial datasets. The notebooks reproduce winning algorithms from the SpaceNet challenges. In addition to the SpaceNet satellite images, we introduce USGS 3D Elevation Program (3DEP) light detection and ranging (LiDAR) data to the workflow. We demonstrate using satellite images, LiDAR data, or combination of both to train and test deep learning models for building and road extraction. Both datasets are hosted on Amazon Web Services (AWS). This tutorial will share the notebooks and provide hands-on and step-by-step instructions on running machine learning services to extract features from large scale geospatial data on AWS. At the end of the tutorial, audiences can reproduce the building and road extraction tasks, apply the models to other area of interests where satellite or LiDAR data are available, and innovate with new ideas to improve the performances. The audiences can also appreciate the benefits of cloud computing and storage first-hand.
A Review on Techniques to Control and Mitigate Odors in Swine Facilities

Received: 11 December 2020 Revised: 23 December 2020 Accepted: 24 December 2020 Malodor emission from livestock and manure production facilities is a persistent concern for researchers and farm owners. Intensive livestock production and manure management have gained significant environmental attention; malodorous emissions are also a concern for livestock and human health. Several technologies, methods, and techniques have been developed in an attempt to resolve the concerns associated with malodorous emissions. Malodorous compounds consist of hundreds of chemical compounds; the most common compounds that humans can smell or sense are volatile organic compounds, ammonia, hydrogen sulfide, and volatile fatty acids. Livestock production houses and manure storage and treatment facilities are key sources of malodorous gas emissions. Livestock feed, the animals themselves, and microbial reactions in the manure are all responsible for malodorous gas generation. The objective of this article is to review the sources of malodorous gas emissions, the common malodorous compounds, and the practices, techniques, and technologies that are used to mitigate the issues related to these emissions.