Miracle-dz/UniFAR-FTUs huggingface dataset:
A river vegetation quality metric in the eco‐hydromorphology philosophy

Vegetation is a key biological element used to assess the ecological status of rivers, for example, under the EU Water Framework Directive. Current macrophyte‐based river vegetation metrics may not accurately reflect overall ecological state, particularly when plant community components are weakly related to broader hydromorphological context. A more holistic River Vegetation Quality Metric (RVQM) is proposed. The RVQM applies an eco‐hydromorphic approach to assessment of vegetation integrity, with a focus on lowland (low–moderate energy) rivers. The concepts underlying the RVQM and the methods for applying this new metric are presented and discussed. A pilot study then explores relationships between the RVQM and recorded physical variables in 119 sites (river reaches) within 27 rivers across Ireland. Statistical models suggest that the RVQM score for these study sites was most strongly related to physical components (assessed under the River Hydromorphology Assessment Technique). Water quality (assessed using a benthic invertebrate metric, Q‐value) also had a positive effect on RVQM score, but high ecological status was only recorded in sample sites with natural morphology and hydraulics, irrespective of water quality. Results indicate that interactions between vegetation and hydromorphological descriptors within the river corridor deserve greater consideration when assigning ecological status to lowland rivers.
An efficient asymmetric distributed lock for embedded multiprocessor systems

Efficient synchronization is a key concern in an embedded many-core system-on-chip (SoC). The use of atomic read-modify-write instructions combined with cache coherency as synchronization primitive is not always an option for shared-memory SoCs due to the lack of suitable IP. Furthermore, there are doubts about the scalability of hardware cache coherency protocols. Existing distributed locks for NUMA multiprocessor systems do not rely on cache coherency and are more scalable, but exchange many messages per lock. This paper introduces an asymmetric distributed lock algorithm for shared-memory embedded multiprocessor systems without hardware cache coherency. Messages are exchanged via a low-cost inter-processor communication ring in combination with a small local memory per processor. Typically, a mutex is used over and over again by the same process, which is exploited by our algorithm. As a result, the number of messages exchanged per lock is significantly reduced. Experiments with our 32-core system show that when having locks in SDRAM, 35% of the memory traffic is lock related. In comparison, our solution eliminates all of this traffic and reduces the execution time by up to 89%.
Insights into the molecular mechanism of allostery in Hsp70s

Hsp70s chaperone an amazing number and variety of cellular protein folding processes. Key to their versatility is the recognition of a short degenerate sequence motif, present in practically all polypeptides, and a bidirectional allosteric intramolecular regulation mechanism linking their N-terminal nucleotide binding domain (NBD) and their C-terminal polypeptide substrate binding domain (SBD). Through this interdomain communication ATP binding to the NBD and ATP hydrolysis control the affinity of the SBD for polypeptide substrates and substrate binding to the SBD triggers ATP hydrolysis. Genetic screens for defective variants of Hsp70s and systematic analysis of available structures of the isolated domains revealed some residues involved in allosteric control. Recent elucidation of the crystal structure of the Hsp70 homolog DnaK in the ATP bound open conformation as well as numerous NMR and mutagenesis studies bring us closer to an understanding of the communication between NBD and SBD. In this review we will discuss our current view of the allosteric control mechanism of Hsp70 chaperones.
On the Optimal Measurement Configuration for Magnetic Nanoparticles-Enhanced Breast Cancer Microwave Imaging

Magnetic nanoparticles-enhanced microwave imaging has been recently proposed as an effective and reliable means to detect breast cancer. Thanks to the nonmagnetic nature of human tissues, the imaging problem corresponds to the retrieval of a weak magnetic anomaly hosted into an unknown nonmagnetic scenario. Hence, properly targeted magnetic nanoparticles in principle allow to avoid false positives and reduce occurrence of false negatives. In this paper, we outline some guidelines for the design of the imaging device based on an optimized measurement configuration. In particular, we determine the nonredundant number of probes and their collocation needed to ensure a reliable solution of the underlying inverse scattering problem. The analysis exploits the spectral properties of the relevant mathematical operators and it is corroborated by reporting numerical results exploiting the phantoms' repository from the University of Wisconsin. It is shown that magnetic nanoparticles-enhanced microwave imaging can reliably detect cancer lesions even using low-complexity arrangements, designed according to the devised guidelines.
Effect of Weak Layer on Seismic Stability of Anchor-Reinforced Slopes

In this paper, permanent displacement of anchor-reinforced slopes was studied numerically to investigate the effect of weak layer on the seismic stability. A variety of slopes with different height and reinforcing anchors were surveyed by employing three different methods: (1) dynamic finite element, (2) Newmark's sliding block and (3) simplified analytical formula. The position and properties of the weak layer was determined by "Phi-C-Reduction" method. Several time-history analyses were performed for the selected slopes subjected to two devastating earthquakes (Tabas and Kocaeli) at different intensities. It was shown that when there is a weak layer in anchor-reinforced slopes, which are statically stable with reasonable safety factor, the anchors could not necessarily provide seismic stability for slopes in some intensity levels. Furthermore, considering average acceleration that may be amplified throughout the slope, the resonance phenomenon was investigated.
The Effects of Methylene Blue on Autophagy and Apoptosis in MRI-Defined Normal Tissue, Ischemic Penumbra and Ischemic Core

Methylene blue (MB) USP, which has energy‐enhancing and antioxidant properties, is currently used to treat methemoglobinemia and cyanide poisoning in humans. We recently showed that MB administration reduces infarct volume and behavioral deficits in rat models of ischemic stroke and traumatic brain injury. This study reportsthe underlying molecular mechanisms of MB neuroprotection following transient ischemic stroke in rats.
Spatial and temporal variability of stable isotopes (δ18O and δ2H) in surface waters of arid, mountainous Central Asia

Characterization of spatial and temporal variability of stable isotopes (δ18O and δ2H) of surface waters is essential to interpret hydrological processes and establish modern isotope–elevation gradients across mountainous terrains. Here, we present stable isotope data for river waters across Kyrgyzstan. River water isotopes exhibit substantial spatial heterogeneity among different watersheds in Kyrgyzstan. Higher river water isotope values were found mainly in the Issyk‐Kul Lake watershed, whereas waters in the Son‐Kul Lake watershed display lower values. Results show a close δ18O–δ2H relation between river water and the local meteoric water line, implying that river water experiences little evaporative enrichment. River water from the high‐elevation regions (e.g., Naryn and Son‐Kul Lake watershed) had the most negative isotope values, implying that river water is dominated by snowmelt. Higher deuterium excess (average d = 13.9‰) in river water probably represents the isotopic signature of combined contributions from direct precipitation and glacier melt in stream discharge across Kyrgyzstan. A significant relationship between river water δ18O and elevation was observed with a vertical lapse rate of 0.13‰/100 m. These findings provide crucial information about hydrological processes across Kyrgyzstan and contribute to a better understanding of the paleoclimate/elevation reconstruction of this region.
Planning and Scheduling for Fleets of Earth Observing Satellites

We address the problem of scheduling observations for a collection of earth observing satellites. This scheduling task is a difficult optimization problem, potentially involving many satellites, hundreds of requests, constraints on when and how to service each request, and resources such as instruments, recording devices, transmitters, and ground stations. High-fidelity models are required to ensure the validity of schedules; at the same time, the size and complexity of the problem makes it unlikely that systematic optimization search methods will be able to solve them in a reasonable time. This paper presents a constraint-based approach to solving the Earth Observing Satellites (EOS) scheduling problem, and proposes a stochastic heuristic search method for solving it.
From business process models to process-oriented software systems

Several methods for enterprise systems analysis rely on flow-oriented representations of business operations, otherwise known as business process models. The Business Process Modeling Notation (BPMN) is a standard for capturing such models. BPMN models facilitate communication between domain experts and analysts and provide input to software development projects. Meanwhile, there is an emergence of methods for enterprise software development that rely on detailed process definitions that are executed by process engines. These process definitions refine their counterpart BPMN models by introducing data manipulation, application binding, and other implementation details. The de facto standard for defining executable processes is the Business Process Execution Language (BPEL). Accordingly, a standards-based method for developing process-oriented systems is to start with BPMN models and to translate these models into BPEL definitions for subsequent refinement. However, instrumenting this method is challenging because BPMN models and BPEL definitions are structurally very different. Existing techniques for translating BPMN to BPEL only work for limited classes of BPMN models. This article proposes a translation technique that does not impose structural restrictions on the source BPMN model. At the same time, the technique emphasizes the generation of readable (block-structured) BPEL code. An empirical evaluation conducted over a large collection of process models shows that the resulting BPEL definitions are largely block-structured. Beyond its direct relevance in the context of BPMN and BPEL, the technique presented in this article addresses issues that arise when translating from graph-oriented to block-structure flow definition languages.
Risk Bounds for Randomized Sample Compressed Classifiers

We derive risk bounds for the randomized classifiers in Sample Compression setting where the classifier-specification utilizes two sources of information viz. the compression set and the message string. By extending the recently proposed Occam's Hammer principle to the data-dependent settings, we derive point-wise versions of the bounds on the stochastic sample compressed classifiers and also recover the corresponding classical PAC-Bayes bound. We further show how these compare favorably to the existing results.
Ontology-Assisted Data Transformation and Integration

Schema-based data transformation and integration (DTI) has been an active research area for some time, while more recent advances in ontologies have led to significant research in ontology-based DTI. These two approaches present some overlaps and some differences, and in this paper we investigate possible synergies between them. In particular, we show how ontologies can enhance schema-based DTI approaches by providing richer semantics for schema constructs. We also illustrate one way in which schema-based DTI approaches can be used together with ontology-based approaches in a heterogeneous data integration setting.
On the Column Density of AGN Outflows: The Case of NGC 5548

We reanalyze the Hubble Space Telescope high-resolution spectroscopic data of the intrinsic absorber in NGC 5548 and find that the C IV absorption column density is at least 4 times larger than previously determined. This increase arises from accounting for the kinematical nature of the absorber and from our conclusion that the outflow does not cover the narrow emission line region in this object. The improved column density determination begins to bridge the gap between the high column densities measured in the X-ray and the low ones previously inferred from the UV lines. Combined with our findings for outflows in high-luminosity quasars, these results suggest that traditional techniques for measuring column densities—equivalent width, curve of growth, and Gaussian modeling—are of limited value when applied to UV absorption associated with active galactic nucleus outflows.
DiamondHelp: A Collaborative Task Guidance Framework for Complex Devices

DiamondHelp is a reusable Java framework for building collaborative task guidance systems for complex devices, such as digitally enabled home appliances. DiamondHelp combines a generic conversational interface, adapted from online chat programs, with an application-specific direct manipulation interface. DiamondHelp provides "a things to say" mechanism for use without spoken language understanding; it also supports extensions to take advantage of speech technology. DiamondHelp's software architecture factors all application-specific content into two modular plug-ins, one of which includes Collagen and a task model.
Cartographic animation and legends for temporal maps : exporation and or interaction

Temporal cartographic animations are increasingly common. For users to understand a temporal animation, they must not only apply an appropriate spatial knowledge schema that allows them to interpret relative geographic location, they must also apply an appropriate temporal schema that allows them to interpret meaning inherent in the sequence and pacing of the animation. Similar to static maps, then, the animated maps should be accompanied by a legend that prompts an appropriate schema. However, with animation, the legends function, not only as an interpretation devices but also as a navigation tools. This paper describes potential legends for temporal animation and argues that choices among them should be made with regard to the nature of the temporal data. A test is proposed to assess the viability of the different legends.
Processes Driving the Networked Economy : Process Portals , Process Vortexes , and Dynamically Trading Processes

The Internet’s World Wide Web has become the prime driver of contemporary electronic commerce (E-commerce) leading to a new networked economy. The emphasis has moved from Electronic Data Interchange (EDI) between applications in different organizations to an entirely new way of IT management and new forms of information not bound by traditional organizational boundaries. The current focus is on using Internet for easier communication and access through exchanging data (with a better syntactic vehicle of XML), messages, individual business transactions, and Web-based application interfaces (sometimes termed as webicization). What is needed in the future is to share information that can be more easily understood by the trading partners without prior context, as well as to support processes crossing organizational borders. This will advance E-commerce from facilitating individual business transactions to support the management of the causal relations between organizational processes. To a large extent, today’s workflow technology remains independent of collaboration and information systems, while critical information systems such as Enterprise Resource Planning (ERP) systems increasingly add workflow capabilities as new features (see box "Does workflow technology have future? "). This article proposes that the evolution of information system architecture will be powered by an organic workflow process technology and outlines three likely stages of architectural evolution in the context of networked economy. It also reviews the state of the art of the key components of current workflow technology—design and modeling, analysis, enactment, interoperability, and adaptability—while discussing critical gaps in the current technology with respect to the future we envision, and suggesting potential research issues.
Dynamics and Computation of Continuous Attractors

Continuous attractor is a promising model for describing the encoding of continuous stimuli in neural systems. In a continuous attractor, the stationary states of the neural system form a continuous parameter space, on which the system is neutrally stable. This property enables the neutral system to track time-varying stimuli smoothly, but it also degrades the accuracy of information retrieval, since these stationary states are easily disturbed by external noise. In this work, based on a simple model, we systematically investigate the dynamics and the computational properties of continuous attractors. In order to analyze the dynamics of a large-size network, which is otherwise extremely complicated, we develop a strategy to reduce its dimensionality by utilizing the fact that a continuous attractor can eliminate the noise components perpendicular to the attractor space very quickly. We therefore project the network dynamics onto the tangent of the attractor space and simplify it successfully as a one-dimensional Ornstein-Uhlenbeck process. Based on this simplified model, we investigate (1) the decoding error of a continuous attractor under the driving of external noisy inputs, (2) the tracking speed of a continuous attractor when external stimulus experiences abrupt changes, (3) the neural correlation structure associated with the specific dynamics of a continuous attractor, and (4) the consequence of asymmetric neural correlation on statistical population decoding. The potential implications of these results on our understanding of neural information processing are also discussed.
Finding Maximum Clique in Stochastic Graphs Using Distributed Learning Automata

Because of unpredictable, uncertain and time-varying nature of real networks it seems that stochastic graphs, in which weights associated to the edges are random variables, may be a better candidate as a graph model for real world networks. Once the graph model is chosen to be a stochastic graph, every feature of the graph such as path, clique, spanning tree and dominating set, to mention a few, should be treated as a stochastic feature. For example, choosing stochastic graph as the graph model of an online social network and defining community structure in terms of clique, and the associations among the individuals within the community as random variables, the concept of stochastic clique may be used to study community structure properties. In this paper maximum clique in stochastic graph is first defined and then several learning automata-based algorithms are proposed for solving maximum clique problem in stochastic graph where the probability distribution functions of the weights associated with the edges of the graph are unknown. It is shown that by a proper choice of the parameters of the proposed algorithms, one can make the probability of finding maximum clique in stochastic graph as close to unity as possible. Experimental results show that the proposed algorithms significantly reduce the number of samples needed to be taken from the edges of the stochastic graph as compared to the number of samples needed by standard sampling method at a given confidence level.
Mass splitting for jitter-free parallel rigid body simulation

We present a parallel iterative rigid body solver that avoids common artifacts at low iteration counts. In large or real-time simulations, iteration is often terminated before convergence to maximize scene size. If the distribution of the resulting residual energy varies too much from frame to frame, then bodies close to rest can visibly jitter. Projected Gauss-Seidel (PGS) distributes the residual according to the order in which contacts are processed, and preserving the order in parallel implementations is very challenging. In contrast, Jacobi-based methods provide order independence, but have slower convergence. We accelerate projected Jacobi by dividing each body mass term in the effective mass by the number of contacts acting on the body, but use the full mass to apply impulses. We further accelerate the method by solving contacts in blocks, providing wallclock performance competitive with PGS while avoiding visible artifacts. We prove convergence to the solution of the underlying linear complementarity problem and present results for our GPU implementation, which can simulate a pile of 5000 objects with no visible jittering at over 60 FPS.
Multiple Objects Fusion Tracker Using a Matching Network for Adaptively Represented Instance Pairs

Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT), which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame. After conducting tracking on a few frames, the initialized matching function is fine-tuned according to the appearance models of target objects. The fine-tuning process of the matching function is constructed as a structured form with diverse matching function branches. In general multiple object tracking situations, scale variations for a scene occur depending on the distance between the target objects and the sensors. If the target objects in various scales are equally represented with the same strategy, information losses will occur for any representation of the target objects. In this paper, the output map of the convolutional layer obtained from a pre-trained convolutional neural network is used to adaptively represent instances without information loss. In addition, MOFT fuses the tracking results obtained from each modality at the decision level to compensate the tracking failures of each modality using basic belief assignment, rather than fusing modalities by selectively using the features of each modality. Experimental results indicate that the proposed tracker provides state-of-the-art performance considering multiple objects tracking (MOT) and KITTIbenchmarks.
Identity Formation, Marijuana and “The Self”: A Study of Cannabis Normalization among University Students

Over the past half-century, as use of marijuana has become more widespread in Canadian society, there are indications of a normalizing process in societal reactions and experiences of use. Among other research avenues, these trends suggest a need for further exploration of young people’s understandings of how they make the choice to use or not and how decisions relate to presentation of the self. This study draws on interviews with 30 undergraduates recruited from a larger online survey of respondents at the University of Guelph, ON, Canada. In probing their perceptions of the use of marijuana, we often found that trying/using “pot” was the default option, whereas choosing not to use required more conscious effort. With specific reference to Goffman’s contribution to a situated understanding of the self, our findings are interpreted with emphasis on further theoretical development of the normalization thesis and on the role of marijuana in identity formation among persons in the process of transition to adulthood.