Miracle-dz/UniFAR-FTUs huggingface dataset:
Caching Video-on-Demand in Metro and Access Fog Data Centres

This paper examines the utilization of metro fog data centres and access fog datacentres with integrated solar cells and Energy Storage Devices (ESDs) to assist cloud data centres in caching Video-on-Demand content and hence, reduce the networking power consumption. A Mixed Integer Linear Programming (MILP) model is used to optimize the delivery of the content from cloud, metro fog, or access fog datacentres. The results for a range of data centre parameters show that savings by up to 38% in the transport network power consumption can be achieved when VoD is optimally served from fully renewable-powered cloud or metro fog data centres or from access fog data centres with 250 m2 solar cells. Additional 8% savings can be achieved when using ESDs of 100 kWh capacity in the access fog data centres.
Growth and longevity of the tubeworm Ridgeia piscesae in the variable diffuse flow habitats of the Juan de Fuca Ridge

The hydrothermal vent tubeworm Ridgeia piscesae visually dominates the vent envi- ronments in the northeast Pacific. One common vent habitat where R. piscesae flourishes is among diffuse vent flow on basaltic substrata. We used chemical analysis of discrete water samples combined with long-term temperature monitoring in 7 aggregations to characterize the immediate habitat of the 'long-skinny' morphotype of R. piscesae. We also monitored in situ growth of 536 indi- viduals from 8 separate aggregations. The temperature data show that exposure of most individuals to vent fluid at the plume level is extremely low compared to other previously studied hydrothermal vent tubeworms, which indicates significant microhabitat heterogeneity at small spatial and tempo- ral scales. The growth rates of this morphotype of R. piscesae ranged from 0 to 252 mm yr -1 and were highly variable between individuals in the same aggregation and between different vent sites. How- ever, individual growth rates in consecutive years show no significant difference and are correlated, indicating long-term stability of the sulfide source in some of these aggregations. This study found no significant relationship between initial tubeworm size and corresponding growth rates. The median plume-level temperature in an aggregation was not significantly correlated with growth rate over the entire dataset. In addition, very conservative longevity estimates suggest that this morphotype of R. piscesae lives for decades in the hydrothermal vent environments of the Juan de Fuca Ridge.
Automatic Plant Image Identification of Vietnamese species using Deep Learning Models

It is complicated to distinguish among thousands of plant species in the natural ecosystem, and many efforts have been investigated to address the issue. In Vietnam, the task of identifying one from 12,000 species requires specialized experts in flora management, with thorough training skills and in-depth knowledge. Therefore, with the advance of machine learning, automatic plant identification systems have been proposed to benefit various stakeholders, including botanists, pharmaceutical laboratories, taxonomists, forestry services, and organizations. The concept has fueled an interest in research and application from global researchers and engineers in both fields of machine learning and computer vision. In this paper, the Vietnamese plant image dataset was collected from an online encyclopedia of Vietnamese organisms, together with the Encyclopedia of Life, to generate a total of 28,046 environmental images of 109 plant species in Vietnam. A comparative evaluation of four deep convolutional feature extraction models, which are MobileNetV2, VGG16, ResnetV2, and Inception Resnet V2, is presented. Those models have been tested on the Support Vector Machine (SVM) classifier to experiment with the purpose of plant image identification. The proposed models achieve promising recognition rates, and MobilenetV2 attained the highest with 83.9%. This result demonstrates that machine learning models are potential for plant species identification in the natural environment, and future works need to examine proposing higher accuracy systems on a larger dataset to meet the current application demand.
Support for integrable Hopf algebras via noncommutative hypersurfaces

We consider finite-dimensional Hopf algebras $u$ which admit a smooth deformation $U\to u$ by a Noetherian Hopf algebra $U$ of finite global dimension. Examples of such Hopf algebras include small quantum groups over the complex numbers, restricted enveloping algebras in finite characteristic, and Drinfeld doubles of height $1$ group schemes. We provide a means of analyzing (cohomological) support for representations over such $u$, via the singularity categories of the hypersurfaces $U/(f)$ associated to functions $f$ on the corresponding parametrization space. We use this hypersurface approach to establish the tensor product property for cohomological support, for the following examples: functions on a finite group scheme, Drinfeld doubles of certain height 1 solvable finite group schemes, bosonized quantum complete intersections, and the small quantum Borel in type $A$.
Towards Building Knowledge by Merging Multiple Ontologies with CoMerger: A Partitioning-based Approach

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a complete representation of a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ontology merging mostly implement a binary merge. However, with the growing number and size of relevant ontologies across domains, scalability becomes a central challenge. A multi-ontology merging technique offers a potential solution to this problem. We present Co Merger, a scalable multiple ontologies merging method. It takes as input a set of source ontologies and existing mappings across them and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. In both steps, user-specified subsets of generic merge requirements (GMRs) are taken into account and used to optimize outputs. The experimental results on well-known datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. A prototypical implementation is freely accessible through a live web portal.
CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement

State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. These segmentations are inaccurate for very high-resolution images since using bicubic upsampling of low-resolution segmentation does not adequately capture high-resolution details along object boundaries. In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data. The key insight is our CascadePSP network which refines and corrects local boundaries whenever possible. Although our network is trained with low-resolution segmentation data, our method is applicable to any resolution even for very high-resolution images larger than 4K. We present quantitative and qualitative studies on different datasets to show that CascadePSP can reveal pixel-accurate segmentation boundaries using our novel refinement module without any finetuning. Thus, our method can be regarded as class-agnostic. Finally, we demonstrate the application of our model to scene parsing in multi-class segmentation.
On discrete pseudohyperbolic attractors of Lorenz type

We study geometrical and dynamical properties of the so-called discrete Lorenz-like attractors, that can be observed in three-dimensional diffeomorphisms. We propose new phenomenological scenarios of their appearance in one parameter families of such maps. We pay especially our attention to such a scenario that can lead to period-2 Lorenz-like attractors. These attractors have very interesting dynamical properties and we show that their crises can lead, in turn, to the emergence of pseudohyperbolic discrete Lorenz shape attractors of new types. We also show examples of all these attractors in three-dimensional generalized Henon maps.
Superoxide dismutase mimetic reduces hypoxia-induced , TGF-β, and VEGF production by macrophages

Normal tissue injury poses a major limitation to the success of radiation therapy (RT) in the treatment of solid tumors. We propose that radiation-induced lung injury is a result of chronic oxidative stress propagated by hypoxia-induced macrophage activation and cytokine production. Therefore, the objective of our study was two-fold. First, in vivo studies were conducted to support our hypothesis suggesting radiation injury is characterized by chronic hypoxia associated with increased macrophage infiltration/activation and pro-fibrogenic/angiogenic cytokine production. Second, we investigated the proposed mechanism of radiation injury in vitro. We demonstrate that hypoxia (0.5% O2) elicits macrophages to produce higher levels of , TGF-β, and VEGF than normoxia. Our hypothesis that is contributing to increased macrophage cytokine production was supported by a significant reduction in TGF-β and VEGF when redox signaling was minimized using a small molecular weight metalloporphyrin antioxidant, MnTE-2-PyP5+ .
A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models

We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed-forward neural networks and learn their parameters directly from the available market option prices. This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying, possibly in an adversarial manner, distances to market prices. The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows the calculation of model prices and model implied volatilities in an accurate way using only small sets of sample paths. For numerical illustration we implement a SABR-type LSV model and conduct a thorough statistical performance analysis on many samples of implied volatility smiles, showing the accuracy and stability of the method.
Performance and Energy Trade-Offs for Parallel Applications on Heterogeneous Multi-Processing Systems

This work proposes a methodology to find performance and energy trade-offs for parallel applications running on Heterogeneous Multi-Processing systems with a single instruction-set architecture. These offer flexibility in the form of different core types and voltage and frequency pairings, defining a vast design space to explore. Therefore, for a given application, choosing a configuration that optimizes the performance and energy consumption is not straightforward. Our method proposes novel analytical models for performance and power consumption whose parameters can be fitted using only a few strategically sampled offline measurements. These models are then used to estimate an application’s performance and energy consumption for the whole configuration space. In turn, these offline predictions define the choice of estimated Pareto-optimal configurations of the model, which are used to inform the selection of the configuration that the application should be executed on. The methodology was validated on an ODROID-XU3 board for eight programs from the PARSEC Benchmark, Phoronix Test Suite and Rodinia applications. The generated Pareto-optimal configuration space represented a 99% reduction of the universe of all available configurations. Energy savings of up to 59.77%, 61.38% and 17.7% were observed when compared to the performance, ondemand and powersave Linux governors, respectively, with higher or similar performance.
ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction

Incorporating environmental, social, and governance (ESG) considerations into systematic investments has drawn numerous attention recently. In this paper, we focus on the ESG events in financial news flow and exploring the predictive power of ESG related financial news on stock volatility. In particular, we develop a pipeline of ESG news extraction, news representations, and Bayesian inference of deep learning models. Experimental evaluation on real data and different markets demonstrates the superior predicting performance as well as the relation of high volatility prediction to stocks with potential high risk and low return. It also shows the prospect of the proposed pipeline as a flexible predicting framework for various textual data and target variables.
Notes on the Dynastid genus Neohyphus Heller, 1896 (Coleoptera, Lamellicornia)

A nomenclatural and taxonomic history of Neohyphus, with comments and conclusions, is given. Neohyphus celebesus Heller, 1896, and Neohyphus semivelutinus (Fairmaire, 1883) remain valid species. Neohyphus philippinus (Arrow, 1931) is revived to a valid species. Neohyphus celebesus subsp. philippinus Endrodi, 1959, is a synonym of Neohyphus celebesus (syn. nov.). The same holds for' Peltonotus sulawesiensis Wada, 1990 (syn. nov.). Neohyphus kleinschmidtii (Fairmaire, 1881) is tentatively replaced in its original genus Melanhyphus. Diagnostic characters are discussed and figured.
THE USE OF POLYPORUS FOR THE INVESTIGATION OF STYLET BEHAVIOUR IN THE HEMIPTERA

The hardness (i.e. resistance to penetration) of water‐saturated polyporus and of various plant structures, determined by a 0.254 mm diameter needle‐penetrometer, ranged from 12–50 and 11–112 g/mm respectively. The isolated mandibular stylets of three species of Homoptera (Typhlocyba rosae, Philaenus spumarius, Macropsis fuscula) possessed tapered but non‐curved apices while the maxillaries were incurved to varying extents: this condition was reversed in two Heteroptera (Orthotylus ochrotrichus, Tingis cardui) where the mandibulars were incurved and the maxillaries straight. Insertion of the isolated stylets into wet polyporus produced curved paths closely related to the stylet structure with the average radius of curvature of the path decreasing by half in the hard (50 g/mm) compared with the soft (12 g/mm) polyporus. These results are related to published information on the stylet paths of these and related insects when they are feeding. Directional control of the stylets in plant tissue is considered to be primarily a function of the mandibular stylets in the Heteroptera and of the maxillaries in the Homoptera, for species in which the maxillary stylets are independently‐moving.
Causality in Static Models as an Initial Constraint on the Chronology of Events in System Behavior

This paper analyzes the notion of causality in a conceptual model, mainly as applied in software engineering. Conceptual system modeling can be considered a three-level process that begins with building a static structural description to develop a dynamic model that will identify events used to specify the chronology of events. In this context, the model involves a representation of a portion of reality, based on ontology of different kinds of things and their basic relations to each other. Relations are defined in terms of their participating entities. This paper concerns relations between events, specifically causal relations among events in modeling. We examine causality in many fields of study to understand its role in modeling. The problem is that, according to many researchers, causation is so inextricably bound up in misleading associations that it is hard to define and is shrouded in mystery, controversy, and caution. We study and clarify the notion of causality through several examples, utilizing an event definition as a time thing/machine in a new conceptual modeling methodology. In conclusion, we claim that the purpose of causal relations in a system s static description is to constrain the system s behavior and thus exclude some possible chronologies of events.
One-shot ultraspectral imaging with reconfigurable metasurfaces

One-shot spectral imaging that can obtain spectral information from thousands of different points in space at one time has always been difficult to achieve. Its realization makes it possible to get spatial real-time dynamic spectral information, which is extremely important for both fundamental scientific research and various practical applications. In this study, a one-shot ultraspectral imaging device fitting thousands of micro-spectrometers (6336 pixels) on a chip no larger than 0.5 cm$^2$, is proposed and demonstrated. Exotic light modulation is achieved by using a unique reconfigurable metasurface supercell with 158400 metasurface units, which enables 6336 micro-spectrometers with dynamic image-adaptive performances to simultaneously guarantee the density of spectral pixels and the quality of spectral reconstruction. Additionally, by constructing a new algorithm based on compressive sensing, the snapshot device can reconstruct ultraspectral imaging information ($\Delta\lambda$/$\lambda$~0.001) covering a broad (300-nm-wide) visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm standard deviation and spectral resolution of 0.8 nm. This scheme of reconfigurable metasurfaces makes the device can be directly extended to almost any commercial camera with different spectral bands to seamlessly switch the information between image and spectral image, and will open up a new space for the application of spectral analysis combining with image recognition and intellisense.
Uncovering Hierarchical Structure of International FOREX Market by Using Similarity Metric between Fluctuation Distributions of Currencies

The decentralized international market of currency trading is a prototypical complex system having a highly heterogeneous composition. To understand the hierarchical structure relating the price movement of different currencies in the market, we have focused on quantifying the degree of similarity between the distributions of exchange rate fluctuations. For this purpose we use a metric constructed using the Jensen-Shannon divergence between the normalized logarithmic return distributions of the different currencies. This provides a novel method for revealing associations between currencies in terms of the statistical nature of their rate fluctuations, which is distinct from the conventional correlation-based methods. The resulting clusters are consistent with the nature of the underlying economies but also show striking divergences during periods of major international crises.
Towards Concise, Machine-discovered Proofs of Gödel's Two Incompleteness Theorems

There is an increasing interest in applying recent advances in AI to automated reasoning, as it may provide useful heuristics in reasoning over formalisms in first-order, second-order, or even meta-logics. To facilitate this research, we present MATR, a new framework for automated theorem proving explicitly designed to easily adapt to unusual logics or integrate new reasoning processes. MATR is formalism-agnostic, highly modular, and programmer-friendly. We explain the high-level design of MATR as well as some details of its implementation. To demonstrate MATR's utility, we then describe a formalized metalogic suitable for proofs of Godel's Incompleteness Theorems, and report on our progress using our metalogic in MATR to semi-autonomously generate proofs of both the First and Second Incompleteness Theorems.
Exploring Exploration: Comparing Children with RL Agents in Unified Environments

Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn. In turn, this early learning supports more robust generalization and intelligent behavior later in life. While much work has gone into developing methods for exploration in machine learning, artificial agents have not yet reached the high standard set by their human counterparts. In this work we propose using DeepMind Lab (Beattie et al., 2016) as a platform to directly compare child and agent behaviors and to develop new exploration techniques. We outline two ongoing experiments to demonstrate the effectiveness of a direct comparison, and outline a number of open research questions that we believe can be tested using this methodology.
Hydrothermal Treatment of Date Palm Lignocellulose Residue for Organic Fertilizer Conversion: Effect on Cell Wall and Aerobic Degradation Rate

Date palm (Phoenix roebelenii) woodchips, a residue of palm tree plantations, was subjected to hydrothermal treatment (HTT) at mild reaction conditions (160°C < T < 220°C, 0.6 MPa < P < 2.4 MPa) for 30 min, and the effect of treatments on the cell wall (CW) solubilization and subsequent aerobic degradation rate (as CO production) was tested under controlled composting conditions during 63 days of incubation (38°C). The HTT at 160 and 180°C reaction temperatures notably solubilized hemicellulose, decreasing the fraction of this CW polymer from 34.1% in the untreated material, to 9.5 and 4.6% in the respective residues. However, treatment at 200 and 220°C reaction temperatures rapidly liquefied the lignin, which apparently went into solution with hemicellulose and appeared to stabilize a portion of this polysaccharides against hydrolysis. Consequently, the fraction of hemicellulose in 200 and 220°C – treated residues gradually increased; the respective values were 5.8 and 9.4%. The treatment temperature of 180°C was the most effective HTT temperature for subsequent aerobic degradation by solubilizing the largest portion of hemicellulose within the CW, which had two consequences: 1) it supplied additional readily bioavailable form of carbon, which in turn promoted rapid microbial activities in the early stage of decomposition; and 2) it created pores and cavities within the CW, which permitted rapid bacterial penetration and CW degradation. As a consequence, biodegradation of the residue treated under this reaction temperature proceeded rapidly and stability was reached within 21 days, compared to 63 days of continued degradation for the untreated CW. The enhanced biodegradability was also partially linked to the effect of 180°C treatment temperature on solubilization of amorphous cellulose and partial hydrolysis of lignin. Based on the results, the HTT system can successfully be used as a pretreatment step to accelerate the aerobic digestion rate of date palm residues for the production of organic fertilizers.