Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical fibre titrations. Part 3. Construction and performance of a fluorimetric acid-base titrator with a blue LED as a light source

An opto-electronic titration unit, which can be used for monitoring the course of acid-base titrations via an optical light-guide, is described. A blue light-emitting diode (LED) is used as a light source and an inexpensive photodiode as a detector to monitor changes in the fluorescence of an added indicator. The instrument is considered to have significant cost advantages over electrode-based titrators.Various kinds of acids and bases have been titrated with excellent precision using the described technique. A voltage change of ca. 100 mV is measured at the end-point. 1-Hydroxypyrene 3,6,8-trisulphonate and fluorescein are used as indicators because of their water solubility and the close overlap of their absorptions with the emission of the blue LED. 7-Hydroxy-3-(4-pyridyl)coumarin is used as a two-step pH indicator for the titration of phosphoric acid, but this dye can only be excited by the UV light of, e.g., a xenon lamp.The advantages of the method described include simplicity, broad applicability (as demonstrated by the titration of aggressive acids such as hydrofluoric acid), the ease of automation and the possibility of performing assays with sample volumes as small as 0.5 ml.
Neural Network Training With Homomorphic Encryption

We introduce a novel method and implementation architecture to train neural networks which preserves the confidentiality of both the model and the data. Our method relies on homomorphic capability of lattice based encryption scheme. Our procedure is optimized for operations on packed ciphertexts in order to achieve efficient updates of the model parameters. Our method achieves a significant reduction of computations due to our way to perform multiplications and rotations on packed ciphertexts from a feedforward network to a back-propagation network. To verify the accuracy of the training model as well as the implementation feasibility, we tested our method on the Iris data set by using the CKKS scheme with Microsoft SEAL as a back end. Although our test implementation is for simple neural network training, we believe our basic implementation block can help the further applications for more complex neural network based use cases.
Neural closure models for dynamical systems

Complex dynamical systems are used for predictions in many domains. Because of computational costs, models are truncated, coarsened or aggregated. As the neglected and unresolved terms become important, the utility of model predictions diminishes. We develop a novel, versatile and rigorous methodology to learn non-Markovian closure parametrizations for known-physics/low-fidelity models using data from high-fidelity simulations. The new neural closure models augment low-fidelity models with neural delay differential equations (nDDEs), motivated by the Mori–Zwanzig formulation and the inherent delays in complex dynamical systems. We demonstrate that neural closures efficiently account for truncated modes in reduced-order-models, capture the effects of subgrid-scale processes in coarse models and augment the simplification of complex biological and physical–biogeochemical models. We find that using non-Markovian over Markovian closures improves long-term prediction accuracy and requires smaller networks. We derive adjoint equations and network architectures needed to efficiently implement the new discrete and distributed nDDEs, for any time-integration schemes and allowing non-uniformly spaced temporal training data. The performance of discrete over distributed delays in closure models is explained using information theory, and we find an optimal amount of past information for a specified architecture. Finally, we analyse computational complexity and explain the limited additional cost due to neural closure models.
Fuzzing with Fast Failure Feedback

Fuzzing—testing programs with random inputs—has become the prime technique to detect bugs and vulnerabilities in programs. To generate inputs that cover new functionality, fuzzers require execution feedback from the program—for instance, the coverage obtained by previous inputs, or the conditions that need to be resolved to cover new branches. If such execution feedback is not available, though, fuzzing can only rely on chance, which is ineffective. In this paper, we introduce a novel fuzzing technique that relies on failure feedback only—that is, information on whether an input is valid or not, and if not, where the error occurred. Our BFUZZER tool enumerates byte after byte of the input space and tests the program until it finds valid prefixes, and continues exploration from these prefixes. Since no instrumentation or execution feedback is required, BFUZZER is language agnostic and the required tests execute very quickly. We evaluate our technique on five subjects, and show that BFUZZER is effective and efficient even in comparison to its white-box counterpart.
Evaluation and Comparison of Edge-Preserving Filters

Edge-preserving filters play an essential role in some of the most basic tasks of computational photography, such as abstraction, tonemapping, detail enhancement and texture removal, to name a few. The abundance and diversity of smoothing operators, accompanied by a lack of methodology to evaluate output quality and/or perform an unbiased comparison between them, could lead to misunderstanding and potential misuse of such methods. This paper introduces a systematic methodology for evaluating and comparing such operators and demonstrates it on a diverse set of published edge-preserving filters. Additionally, we present a common baseline along which a comparison of different operators can be achieved and use it to determine equivalent parameter mappings between methods. Finally, we suggest some guidelines for objective comparison and evaluation of edge-preserving filters.
Information Restrain in Open Quantum Systems

Non-Hermitian skin effect of Liouvillian superoperators in quantum open systems can induce phenomena of non-trivial damping, known as chiral/helical damping. While non-Hermitian skin effect and chiral/helical damping occur only under open boundary condition, we propose an effect called information restrain which does not rely on boundary conditions. We demonstrate that information restrain is stable against disorder and is an intrinsic property of a type of open quantum systems or non-Hermitian system. Then we define the strength of information restrain IR, which describes the ratio of different decay rates of signal’s strengthes along opposite propagation directions. Based on information restrain, We can provide a simple and elegant explanation of chiral and helical damping, and get the local maximum of relative particle number for periodical boundary system, consistent with numerical calculations. In terms of information restrain, we also illustrate the existence of correspondence between edge modes and damping modes and deduce that there are many chiral/helical transport properties in this ’information restrain’ class.
Thermodynamic instability of 3D Einstein-Born-Infeld AdS black holes *

Super-entropic black holes possess finite-area but noncompact event horizons and violate the reverse isoperimetric inequality. It has been conjectured that such black holes always have negative specific heat at constant volume $C_{V}$ or negative specific heat at constant pressure $C_{P}$ whenever $C_{V}>0$, making them unstable in extended thermodynamics. In this paper, we test this instability conjecture on a family of nonlinear electrodynamics black holes, namely $3$D Einstein-Born-Infeld (EBI) AdS black holes. Our results show that when nonlinear electrodynamics effects are weak, the instability conjecture is valid. However, the conjecture can be violated in some parameter region when nonlinear electrodynamics effects are strong enough. This observation thus provides a counter example to the instability conjecture, which suggests that super-entropic black holes could be thermodynamically stable.
Lip-reading with Hierarchical Pyramidal Convolution and Self-Attention

In this paper, we propose a novel deep learning architecture to improving word-level lip-reading. On the one hand, we first introduce the multi-scale processing into the spatial feature extraction for lip-reading. Specially, we proposed hierarchical pyramidal convolution (HPConv) to replace the standard convolution in original module, leading to improvements over the model's ability to discover fine-grained lip movements. On the other hand, we merge information in all time steps of the sequence by utilizing self-attention, to make the model pay more attention to the relevant frames. These two advantages are combined together to further enhance the model's classification power. Experiments on the Lip Reading in the Wild (LRW) dataset show that our proposed model has achieved 86.83% accuracy, yielding 1.53% absolute improvement over the current state-of-the-art. We also conducted extensive experiments to better understand the behavior of the proposed model.
Quantum photocell based on GaN quantum dots

In this work, we propose an eﬃcient photocell based on GaN quantum dots. We exploit the strong built-in electric ﬁeld in GaN QDs and excitonic dipole-dipole coupling between adjacent QDs to break detailed balance. This is a much stronger eﬀect than Fano interference, and we show that such a photocell exhibits enhanced photo-voltage and photocurrent over its conventional counterpart. The proposed quantum photocell could be the ﬁrst step towards harnessing quantum eﬀects in practical energy harvesting devices.
Fundamental Limits on Energy-Delay-Accuracy of In-Memory Architectures in Inference Applications

This article obtains fundamental limits on the computational precision of in-memory computing architectures (IMCs). An IMC noise model and associated signal-to-noise ratio (SNR) metrics are defined and their interrelationships analyzed to show that the accuracy of IMCs is fundamentally limited by the compute SNR (<inline-formula> <tex-math notation="LaTeX">${\mathrm {SNR}}_{a}$ </tex-math></inline-formula>) of its analog core, and that activation, weight, and output (ADC) precision needs to be assigned appropriately for the final output SNR (<inline-formula> <tex-math notation="LaTeX">${\mathrm {SNR}}_{T}$ </tex-math></inline-formula>) to approach <inline-formula> <tex-math notation="LaTeX">${\mathrm {SNR}}_{a}$ </tex-math></inline-formula>. The minimum precision criterion (MPC) is proposed to minimize the analog-to-digital converter (ADC) precision and hence its overhead. Three in-memory compute models—charge summing (QS), current summing (IS), and charge redistribution (QR)—are shown to underlie most known IMCs. Noise, energy, and delay expressions for the compute models are developed and employed to derive expressions for the SNR, ADC precision, energy, and latency of IMCs. The compute SNR expressions are validated via Monte Carlo simulations in a 65 nm CMOS process. For a 512 row SRAM array, it is shown that: 1) IMCs have an upper bound on their maximum achievable <inline-formula> <tex-math notation="LaTeX">${\mathrm {SNR}}_{a}$ </tex-math></inline-formula> due to constraints on energy, area and voltage swing, and this upper bound reduces with technology scaling for QS-based architectures; 2) MPC enables <inline-formula> <tex-math notation="LaTeX">${\mathrm {SNR}}_{T}$ </tex-math></inline-formula> to approach <inline-formula> <tex-math notation="LaTeX">${\mathrm {SNR}}_{a}$ </tex-math></inline-formula> to be realized with minimal ADC precision; and 3) QS-based (QR-based) architectures are preferred for low (high) compute SNR scenarios.
Brain-Inspired Search Engine Assistant Based on Knowledge Graph

Search engines can quickly respond to a hyperlink list according to query keywords. However, when a query is complex, developers need to repeatedly refine search keywords and open a large number of web pages to find and summarize answers. Many research works of question and answering (Q&A) system attempt to assist search engines by providing simple, accurate, and understandable answers. However, without original semantic contexts, these answers lack explainability, making them difficult for users to trust and adopt. In this article, a brain-inspired search engine assistant named DeveloperBot based on knowledge graph is proposed, which aligns to the cognitive process of humans and has the capacity to answer complex queries with explainability. Specifically, DeveloperBot first constructs a multilayer query graph by splitting a complex multiconstraint query into several ordered constraints. Then, it models a constraint reasoning process as a subgraph search process inspired by a spreading activation model of cognitive science. In the end, novel features of the subgraph are extracted for decision-making. The corresponding reasoning subgraph and answer confidence are derived as explanations. The results of the decision-making demonstrate that DeveloperBot can estimate answers and answer confidences with high accuracy. We implement a prototype and conduct a user study to evaluate whether and how the direct answers and the explanations provided by DeveloperBot can assist developers’ information needs.
Nonlinear exciton drift in piezoelectric two-dimensional materials

Noncentrosymmetric nature of single-layer transition metal dichalcogenides manifest itself in the finite piezoelectricity and valley-Zeeman coupling. We microscopically model nonlinear exciton transport in nano-bubble of single-layers of transition metal dichalcogenide. Thanks to the giant piezoelectric effect, we obtain an enormous internal electric field, $E_{\rm piezo}\sim 10^7$V/m, resulting in a built-in dipole moment of excitons. We demonstrate that the piezo-induced dipole-dipole interaction provides a novel channel for the nonlinear exciton transport distinct from the conventional isotropic funneling of excitons and leading to the formation of hexagon-shaped exciton droplet on top of a circularly symmetric nano-bubble. The effect is tunable via the bubble size dependence of the piezo-electric field $E_{\rm piezo} \sim h^2_{\rm max}/R^3$ with $h_{\rm max}$ and $R$ being the bubble height and radius, respectively.
ThamizhiUDp: A Dependency Parser for Tamil

This paper describes how we developed a neural-based dependency parser, namely ThamizhiUDp, which provides a complete pipeline for the dependency parsing of the Tamil language text using Universal Dependency formalism. We have considered the phases of the dependency parsing pipeline and identified tools and resources in each of these phases to improve the accuracy and to tackle data scarcity. ThamizhiUDp uses Stanza for tokenisation and lemmatisation, ThamizhiPOSt and ThamizhiMorph for generating Part of Speech (POS) and Morphological annotations, and uuparser with multilingual training for dependency parsing. ThamizhiPOSt is our POS tagger, which is based on the Stanza, trained with Amrita POS-tagged corpus. It is the current state-of-the-art in Tamil POS tagging with an F1 score of 93.27. Our morphological analyzer, ThamizhiMorph is a rule-based system with a very good coverage of Tamil. Our dependency parser ThamizhiUDp was trained using multilingual data. It shows a Labelled Assigned Score (LAS) of 62.39, 4 points higher than the current best achieved for Tamil dependency parsing. Therefore, we show that breaking up the dependency parsing pipeline to accommodate existing tools and resources is a viable approach for low-resource languages.
Stability-Certified Reinforcement Learning via Spectral Normalization

In this article, two types of methods from different perspectives based on spectral normalization are described for ensuring the stability of the system controlled by a neural network. The first one is that the L2 gain of the feedback system is bounded less than 1 to satisfy the stability condition derived from the small-gain theorem. While explicitly including the stability condition, the first method may provide an insufficient performance on the neural network controller due to its strict stability condition. To overcome this difficulty, the second one is proposed, which improves the performance while ensuring the local stability with a larger region of attraction. In the second method, the stability is ensured by solving linear matrix inequalities after training the neural network controller. The spectral normalization proposed in this article improves the feasibility of the a-posteriori stability test by constructing tighter local sectors. The numerical experiments show that the second method provides enough performance compared with the first one while ensuring enough stability compared with the existing reinforcement learning algorithms.
Time-Fluid Field-Based Coordination through Programmable Distributed Schedulers

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective, in terms of functional manipulation and evolution in "space and time" of distributed data structures called fields. More specifically regarding time, in field-based coordination (as in many other distributed approaches to coordination) it is assumed that local activities in each device are regulated by a fair and unsynchronised fixed clock working at the platform level. In this work, we challenge this assumption, and propose an alternative approach where scheduling is programmed in a natural way (along with usual field-based coordination) in terms of causality fields, each enacting a programmable distributed notion of a computation "cause" (why and when a field computation has to be locally computed) and how it should change across time and space. Starting from low-level platform triggers, such causality fields can be organised into multiple layers, up to high-level, collectively-computed time abstractions, to be used at the application level. This reinterpretation of time in terms of articulated causality relations allows us to express what we call "time-fluid" coordination, where scheduling can be finely tuned so as to select the triggers to react to, generally allowing to adaptively balance performance (system reactivity) and cost (resource usage) of computations. We formalise the proposed scheduling framework for field-based coordination in the context of the field calculus, discuss an implementation in the aggregate computing framework, and finally evaluate the approach via simulation on several case studies.
Jacobian-based learning for inverse kinematics of soft robots

—This paper presents a new method to solve the inverse kinematic (IK) problem in real-time on soft robots with highly non-linear deformation. The major challenge of efﬁciently computing IK for such robots is caused by the lack of analytical formulation for either forward or inverse kinematics. To tackle this challenge, we employ neural-networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We ﬁrst generate large amount of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. After that, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from very limited number of training samples generated on the hardware. The effectiveness of our approach has been veriﬁed on several pneumatic-driven soft robots in the tasks of trajectory following and interactive positioning.
Fall Armyworm ( Spodoptera frugiperda ) control by RNAi as a Plausible Component to Integrated Pest Management Practices

Fall armyworm (Spodoptera frugiperda) control through RNAi machinery as a novel mode of action is a weapon sought after by the agricultural community to squelch the growing problem of S. frugiperda resistance to pesticides and transgenic crops. Significant gains in the understanding of RNAi cellular interactions and insect gene silencing are represented to provide opportunities to influence further research of S. frugiperda RNAi. This composition highlights the intracellular action of dsRNA, successful RNAi attempts on insect pests, the biological difficulty of S. frugiperda and how RNAi success against S. frugiperda would be highly advantageous in the quest to mitigate resistance regarding current integrated pest management (IPM) practices to control this polyphagous and genetically plastic agricultural pest. Table of
Glasartiger Kohlenstoff Sigradur® – ein Werkstoff für Chemie und Technik

Glaskohlenstoff ist eine Kohlenstofform mit glasartigem Bruchbild. Rohstoffe, Herstellungsverfahren und Struktur des glasartigen Kohlenstoffs werden beschrieben, die Ergebnisse bruchmechanischer Untersuchungen diskutiert und ein Uberblick uber die zahlreichen Anwendungen und Einsatzmoglichkeiten gegeben. Glassy Carbon Sigradur® – a Material for Chemistry and Technology Glassy carbon is a carbon modification characterized by its glasslike fracture behaviour. Raw material, manufacturing, microstructure, and studies on fracture mechanics are discussed and examples for the widespread application of glassy carbon are given.
Variation in the emission factor of N2O derived from chemical nitrogen fertilizer and organic matter: A case study of onion fields in Mikasa, Hokkaido, Japan

Abstract Variability in the emission factors of nitrous oxide (N2O) associated with the application of chemical fertilizer (EFF) and organic matter (EFO) were analyzed in two onion fields (GL, Gray Lowland soil [Gleysol; Food and Agriculture Organization/UNESCO]; BL, Brown Lowland soil [Fluvisol; Food and Agriculture Organization/UNESCO]) in Mikasa, Hokkaido, Japan. Nitrous oxide flux was measured using a closed chamber technique in four treatments (FOP, chemical nitrogen fertilization and organic matter application, with plants; F, chemical nitrogen fertilization only, without plants; OP, organic matter application only, with plants; C, control, no fertilization or organic matter application, without plants) for 4 years in GL (2000, 2003–2005) and for 1 year in BL (2005). The application rate of chemical fertilizer nitrogen ranged from 237 to 242 kg N ha−1 year−1 in GL and was 284 kg N ha−1 year−1 in BL; organic matter nitrogen ranged from 81 to 117 kg N ha−1 year−1 in GL and was 181 kg N ha−1 year−1 in BL. The emission factors (EF) were calculated using the equations: EFF (%) = (N2O emission in FOP–N2O emission in OP)/(applied chemical nitrogen fertilizer) × 100 and EFO (%) = (N2O emission in FOP–N2O emission in F)/(applied organic matter nitrogen) × 100. The annual N2O emissions for treatments FOP, F, OP and C were 7.2–17, 5.7–17, 3.2–9.9 and 2.0–12 kg N ha−1 year−1, respectively, in GL and 5.6, 2.8, 1.9 and 1.8 kg N ha−1 year−1, respectively, in BL. The EFF ranged from 1.3% to 5.5% in GL and was 1.3% in BL. The EFF was positively correlated with the mean annual air temperature (P < 0.01), suggesting that N2O emission derived from chemical nitrogen fertilizer increases as air temperature rises. The EFO, however, differed greatly between GL (ranging from −5.2% to 9.1%) and BL (1.5%). The EFO was positively correlated with the mean annual relative humidity, although the correlation was not significant (P = 0.23). This finding suggests that much wetter climatic conditions may increase N2O emissions derived from organic matter nitrogen. The estimated N2O emissions based on these EF values and the rate of nitrogen application coincided well with the measured N2O emissions in the FOP treatment in both soils.