Miracle-dz/UniFAR-FTUs huggingface dataset:
Design scheme for copyright management system using Blockchain and IPFS

The existing technologies allow to distribute digital images quickly and easily through the internet. The misuse of digital images, intentionally or unintentionally, increases dramatically especially with copyright protection that facing a major challenge. There are many unsolved problems in the centralized copyright office registration such as high cost services, long time processing, store copyright information in the centralized server and susceptibility to tampering registration records. In this paper, a schema of copyright digital management system is designed based on blockchain, Inter Planetary File System (IPFS) and Perceptual hash image. Blockchain technology evolvement has very changed the network that makes numerous applications become distributed and decentralized without loss of security. Moreover, our solution exploits the benefits of IPFS to store image and text file of copyright owner’s information on a decentralized file system. Perceptual image hashing is used for authentication based on the understanding of the image content. The scheme uses Point to Point (P2P) network to improve the copyright image management, make the distribution of copyrighted work without third part and finally protect the copyright owner.
Determination of the Quinone-loading of a Modified Carbon Powder-based Electrode for Electrochemical Capacitor

Black Pearls (BP) carbon powder was chemically modified with chloroanthraquinone groups by spontaneous reduction of 1-amino, 5-chloroanthraquinone with the aim of determining the quinone content of the modified carbon. This information is essential to determine the increase of capacitance associated to the grafted quinone molecules. The capacitance of pristine carbon electrode was doubled due to the presence of the grafted electroactive molecules. The modified BP powder was characterized by thermogravimetric and elemental analyses. The modified powder was also used to fabricate composite electrodes that were characterized by electrochemistry to determine the loading of electroactive quinone molecules. The presence of the chlorine atom on the anthraquinone moiety allows an estimation of the loading from elemental analysis, which is in relatively good agreement with the value estimated by electrochemistry. © The Electrochemical Society of Japan, All rights reserved.
Carbon balance and water use efficiency of frequently cut Lolium perenne L. swards at elevated carbon dioxide

The impact of doubled atmospheric [CO2] on the carbon balance of regularly cut Lolium perenne L. swards was studied for two years under semi-field conditions in the Wageningen Rhizolab. CO2 and H2O vapour exchange rates of the swards were measured continuously for two years in transparent enclosures. The light utilization efficiencies of the swards ranged between 1.5 g CO2 MJ ‐1 global radiation (high light, ambient [CO2]) and 2.8 g CO2 MJ ‐1 (low light, doubled [CO2]). The above-ground net primary productivity (NPP) in the enclosures was greater by 29% in 1994 and 43% in 1995 in the doubled [CO2] treatments, but only 20% and 25% more carbon was recovered in the periodical cuts. Thus, NPP increased significantly more than did the harvested aboveground biomass. The positive [CO2] effect on net carbon assimilation is therefore associated with a preferential allocation of extra carbon to the roots and soil. In addition to higher canopy photosynthesis and leaf elongation rates, a small part of the positive [CO 2] effects on NPP could be attributed to a decrease of the specific respiration of the shoots. On a canopy basis however, respiration was equal or slightly higher at doubled [CO2] due to the higher amount of standing biomass. Comparison of NPP and carbon recovered in different harvests showed that allocation to roots and soil was highest in spring, it was low in early summer and increased again in late summer and autumn. The total gross amount of carbon partitioned to the roots and soil during the two year period was 57% more at doubled [CO2]. The total amount of carbon that was sequestered in the soil after subtraction of the respiratory losses was 458 g m ‐2 and 779 g m ‐2 in the ambient and doubled [CO2] treatments, respectively. The average water use efficiency (WUE) of the swards was increased by a factor 1.5 at doubled [CO2]. Both WUE and its positive interaction with [CO2] varied between years and were positively correlated with global irradiance. At doubled [CO2], the higher WUE was fully compensated for by a higher leaf area index. Therefore, total transpiration on a canopy basis was equal for the ambient and the doubled [CO2] concentrations in both years.
Sorption of Neptunium on Clays and Clay Minerals — A Review

During recent decades, the search for possible repositories for high-level nuclear waste has yielded large amounts of sorption data for actinides on different minerals. Clays and clay minerals are of special interest as potential host-rock formations and backfill materials, by virtue of their good retardation properties. Neptunium (Np) is one of the actinides which is considered in long-term scenarios due to its long-lived nuclide 237Np (t1/2 = 2.1 × 106 y). Because neptunium sorption is heavily dependent on the experimental conditions, comparison of sorption data from different experiments is challenging. Normalizing reported data with respect to the surface area of the sorbent enables conversion of conventional distribution coefficients (Kd) to normalized (Ka) values, which improves comparability among the results of different experiments. The present review gives a detailed summary of sorption data of Np on clays and clay minerals and examines critically the applicability of the Ka approach.
Proceedings, Advancing Astrophysics with the Square Kilometre Array (AASKA14)

In 2014 it was 10 years since the publication of the comprehensive ‘Science with the Square Kilometre Array’ book and 15 years since the first such volume appeared in 1999. In that time numerous and unexpected advances have been made in the fields of astronomy and physics relevant to the capabilities of the Square Kilometre Array (SKA). The SKA itself progressed from an idea to a developing reality with a baselined Phase 1 design (SKA1) and construction planned from 2017. To facilitate the publication of a new, updated science book, which will be relevant to the current astrophysical context, the meeting "Advancing Astrophysics with the Square Kilometre Array" was held in Giardina Naxos, Sicily. Articles were solicited from the community for that meeting to document the scientific advances enabled by the first phase of the SKA and those pertaining to future SKA deployments, with expected gains of 5 times the Phase 1 sensitivity below 350 MHz, about 10 times the Phase 1 sensitivity above 350 MHz and with frequency coverage extending to ~20 GHz. The chapters listed here are the direct result of that meeting.
Fault Diagnosis Using Rough Set Theory

This paper has presented an effective and efficient approach to extract diagnosis rules from inconsistent and redundant data set of power transformers using rough set theory. The extracted diagnosis rules can effectively reduce space of input attributes and simplify knowledge representation for fault diagnosis. The fault diagnosis decision table is first built through discretized attributes. Next, the GA based optimization process is used to obtain the minimal reduct of symptom attributes. Finally, the rule simplification process is adapted to achieve the maximal generalized decision rules, which can be derived from inconsistent and redundant information. Experimental results demonstrate that the proposed approach has remarkable diagnosis accuracy than the existing method. Keywords— Fault Diagnosis, Dissolved Gas Analysis, Rough Set
The Atmospheric Fate of C3−C6 Hydroxyalkyl Nitrates

Rate coefficients for the reaction of OH radical with eleven C3−C6 hydroxyalkyl nitrates and with two C4 hydroxy nitrates containing a double bond were determined at atmospheric pressure and 296 ± 2 K. The rate coefficients were measured in a photochemical reactor by the relative rate technique, employing solid-phase microextraction (SPME) coupled to gas chromatography (GC) for detection of the organic reactants. Hydroxyalkyl nitrates react faster than alkyl nitrates with the OH radical. The rate coefficients increase with increasing chain length and separation between the hydroxy and the nitrooxy groups. By including different loss processes such as photolysis, gas-phase reactions, and solubility, the tropospheric lifetime of C3−C6 hydroxyalkyl nitrates is estimated to range between 0.5 and 4.5 days. Due to their higher reactivity and solubility, hydroxyalkyl nitrates have a shorter atmospheric lifetime than alkyl nitrates.
BRAFV600E induces reversible mitotic arrest in human melanocytes via microRNA-mediated suppression of AURKB

Benign melanocytic nevi frequently emerge when an acquired BRAFV600E mutation triggers unchecked proliferation and subsequent arrest in melanocytes. Recent observations have challenged the role of oncogene-induced senescence in melanocytic nevus formation, necessitating investigations into alternative mechanisms for the establishment and maintenance of proliferation arrest in nevi. We compared the transcriptomes of melanocytes from healthy human skin, nevi, and melanomas arisen from nevi and identified a set of microRNAs as highly expressed nevus-enriched transcripts. Two of these microRNAs – MIR211-5p and MIR328-3p – induced mitotic failure, genome duplication and proliferation arrest in human melanocytes through convergent targeting of AURKB. We demonstrate that BRAFV600E induces a similar proliferation arrest in primary human melanocytes that is both reversible and conditional. Specifically, BRAFV600E expression stimulates either arrest or proliferation depending on the differentiation state of the melanocyte. We report genome duplication in human melanocytic nevi, reciprocal expression of AURKB and microRNAs in nevi and melanomas, and rescue of arrested human nevus cells with AURKB expression. Together, our data describe an alternative molecular mechanism for melanocytic nevus formation that is congruent with both experimental and clinical observations.
Estimating Unknown Time-Varying Parameters in Uncertain Differential Equation

Uncertain differential equations have a wide range of applications. How to obtain estimated values of unknown parameters in uncertain differential equations through observations has always been a subject of concern and research, many methods have been developed to estimate unknown parameters. However, these parameters are constants. In this paper, the method of least squares estimation is recast for estimating the unknown time-varying parameters in uncertain differential equations. A set of unknown time-varying parameter estimates will be obtained, and then the unknown time-varying parameters will be obtained by regression fitting using the estimated values. Using this method, the uncertain differential equation of blood alcohol concentration in human body after drinking and the uncertain differential equation of COVID-19 are derived.
Explainable Machine Learning with Prior Knowledge: An Overview

This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability. The complexity of machine learning models has elicited research to make them more explainable. However, most explainability methods cannot provide insight beyond the given data, requiring additional information about the context. We propose to harness prior knowledge to improve upon the explanation capabilities of machine learning models. In this paper, we present a categorization of current research into three main categories which either integrate knowledge into the machine learning pipeline, into the explainability method or derive knowledge from explanations. To classify the papers, we build upon the existing taxonomy of informed machine learning and extend it from the perspective of explainability. We conclude with open challenges and research directions.
Covariance-Free Sparse Bayesian Learning

Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix. To resolve this issue, we introduce a new method for accelerating SBL inference – named covariance-free expectation maximization (CoFEM) – that avoids explicit computation of the covariance matrix. CoFEM solves multiple linear systems to obtain unbiased estimates of the posterior statistics needed by SBL. This is accomplished by exploiting innovations from numerical linear algebra such as preconditioned conjugate gradient and a little-known diagonal estimation rule. For a large class of compressed sensing matrices, we provide theoretical justifications for why our method scales well in high-dimensional settings. Through simulations, we show that CoFEM can be up to thousands of times faster than existing baselines without sacrificing coding accuracy. Through applications to calcium imaging deconvolution and multi-contrast MRI reconstruction, we show that CoFEM enables SBL to tractably tackle high-dimensional sparse coding problems of practical interest.
Reduced-Order-Model-Based Feedback Design for Thruster-Assisted Legged Locomotion

Real-time constraint satisfaction for robots can be quite challenging due to the high computational complexity that arises when accounting for the system dynamics and environmental interactions, often requiring simplification in modelling that might not necessarily account for all performance criteria. We instead propose an optimization-free approach where reference trajectories are manipulated to satisfy constraints brought on by ground contact as well as those prescribed for states and inputs. Unintended changes to trajectories especially ones optimized to produce periodic gaits can adversely affect gait stability, however we will show our approach can still guarantee stability of a gait by employing the use of coaxial thrusters that are unique to our robot.
Turnover-Adjusted Information Ratio

In this paper, we study the behavior of information ratio (IR) as determined by the fundamental law of active investment management. We extend the classic relationship between IR and its two determinants (i.e., information coefficient and investment “breadth”) by explicitly and simultaneously taking into account the volatility of IC and the cost from portfolio turnover. Through mathematical derivations and simulations, we show that – for both mean-variance and quintile portfolios – a turnover-adjusted IR is always lower than an IR that ignores the cost from turnover; more importantly, we find that, contrary to the implication from the fundamental law but consistent with available empirical evidence, investment managers may improve their investment performance or IR by limiting/optimizing trade or portfolio turnover.
An Intelligent Transaction Migration Scheme for RAFT-Based Private Blockchain in Internet of Things Applications

The integration of multi-access edge computing (MEC) and RAFT consensus makes it feasible to deploy blockchain on trustful base stations and gateways to provide efficient and tamper-proof edge data services for Internet of Things (IoT) applications. However, reducing the latency of storing data on blockchain remains a challenge, especially when an anomaly-triggered data flow in a certain area exceeds the block generation speed. This letter proposes an intelligent transaction migration scheme for RAFT-based private blockchain in IoT applications to migrate transactions in busy areas to idle regions intelligently. Simulation results show that the proposed scheme can apparently reduce the latency in high data flow circumstances.
Polyjuice: High-Performance Transactions via Learned Concurrency Control

Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, optimistic concurrency control (OCC) is better than two-phase-locking (2PL) under low contention, while the converse is true under high contention. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. Instead of choosing among a small number of known algorithms, our approach searches in a"policy space"of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Under different configurations of TPC-C and TPC-E, Polyjuice can achieve throughput numbers higher than the best of existing algorithms by 15% to 56%.
External calibrator in global signal experiment for detection of the epoch of reionization

We present a conceptual design study of external calibrators in the 21 cm experiment towards detecting the globally averaged radiation of the epoch of reionization (EoR). Employment of external calibrator instead of internal calibrator commonly used in current EoR experiments allows removing instrumental effects such as beam pattern, receiver gain and instability of the system if the conventional three-position switch measurements are implemented in a short time interval. Furthermore, in the new design the antenna system is placed in an underground anechoic chamber with an open/closing ceiling to maximally reduce the environmental effect such as RFI and ground radiation/reflection. It appears that three of the four external calibrators proposed in this paper, including two indoor artificial transmitters and one outdoor celestial radiation (the Galactic polarization), fail to meet our purpose. Diurnal motion of the Galactic diffuse emission turns out to be the most probable source as an external calibrator, for which we have discussed the observational strategy and the algorithm of extracting the EoR signal.
Multi-objective Optimisation of Digital Circuits based on Cell Mapping in an Industrial EDA Flow

—Modern electronic design automation (EDA) tools can handle the complexity of state-of-the-art electronic systems by decomposing them into smaller blocks or cells, introducing different levels of abstraction and staged design ﬂows. However, throughout each independent-optimised design step, overhead and inefﬁciency can accumulate in the resulting overall design. Performing design-speciﬁc optimisation from a more global viewpoint requires more time due to the larger search space, but has the potential to provide solutions with improved performance. In this work, a fully-automated, multi-objective (MO) EDA ﬂow is introduced to address this issue. It speciﬁcally tunes drive strength mapping, preceding physical implementation, through multi-objective population-based search algorithms. Designs are evaluated with respect to their power, performance and area (PPA). The proposed approach is aimed at digital circuit op- timisation at the block-level, where it is capable of expanding the design space and offers a set of trade-off solutions for different case-speciﬁc utilisation. We have applied the proposed MOEDA framework to ISCAS-85 and EPFL benchmark circuits using a commercial 65nm standard cell library. The experimental results demonstrate how the MOEDA ﬂow enhances the solutions initially generated by the standard digital ﬂow, and how simultaneously a signiﬁcant improvement in PPA metrics is achieved.
Unified Expression of Achievable Region in Privacy-Constrained Source Coding

—Privacy-constrained source coding problems have become increasingly important recently, and the utility-privacy trade-off has been investigated for various systems. As pioneering work, Yamamoto (1983) found theoretical limits of the coding rate, privacy and utility in two cases; (i) both public and private information is encoded and (ii) only public information is encoded. However, the theoretical limit has not been characterized in a more general case; (iii) encoded messages consist of public information and a part of private information. Then in this paper, we characterize the trade-off relation in case (iii). The obtained expression of the achievable region is a “uniﬁed expression” because it includes the ones in case (i) and (ii) as special cases. Through the numerical results, we clarify the signiﬁcance of the choice of the encoded set.
Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis

In recent years, wildfires have caused havoc across the world, which are especially aggravated in certain regions due to climate change. Remote sensing has become a powerful tool for monitoring fires, as well as for measuring their effects on vegetation over the following years. We aim to explain the dynamics of wildfires’ effects on a vegetation index (previously estimated by causal inference through synthetic controls) from pre-wildfire available information (mainly proceeding from satellites). For this purpose, we use regression models from Functional Data Analysis, where wildfire effects are considered functional responses, depending on elapsed time after each wildfire, while pre-wildfire information acts as scalar covariates. Our main findings show that vegetation recovery after wildfires is a slow process, affected by many pre-wildfire conditions, among which the richness and diversity of vegetation is one of the best predictors for the recovery.
Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging

In a dialogue system pipeline, a natural language generation (NLG) unit converts the dialogue direction and content to a corresponding natural language realization. A recent trend for dialogue systems is to first pre-train on large datasets and then fine-tune in a supervised manner using datasets annotated with application-specific features. Though novel behaviours can be learned from custom annotation, the required effort severely bounds the quantity of the training set, and the application-specific nature limits the reuse. In light of the recent success of data-driven approaches, we propose the novel future bridging NLG (FBNLG) concept for dialogue systems and simulators. The critical step is for an FBNLG to accept a future user or system utterance to bridge the present context towards. Future bridging enables self supervised training over annotation-free datasets, decoupled the training of NLG from the rest of the system. An FBNLG, pre-trained with massive datasets, is expected to apply in classical or new dialogue scenarios with minimal adaptation effort. We evaluate a prototype FBNLG to show that future bridging can be a viable approach to a universal few-shot NLG for task-oriented and chit-chat dialogues.