Miracle-dz/UniFAR-FTUs huggingface dataset:
Frontiers in Computational Neuroscience

Reservoir networks do not require classical attractor states and are compatible with the view that cortical computation is based It has been shown that neural systems may exhibit transients of long durations which carry more information about the stimulus then the steady states towards which the activity evolves (Mazor and Laurent, 2005). Attempts to endow RNNs with unsupervised learning abilities by incorporating biologically plausible local plasticity mechanisms such as spike-timing-dependent plasticity (STDP) (Markram et al., 1997; Bi and Poo, 1998) have remained largely unsuccessful (and often unpublished). The problem is most diffi cult, because structural changes induced by plasticity will impact the network's dynamics giving rise to altered fi ring patterns between neurons. These altered fi ring patterns can further induce changes in connectivity through the plasticity mechanisms and so forth. Understanding and controlling the ensuing self-organization of network structure and dynamics as a function of the network's inputs is a formidable challenge. The key to the brain's solution to this problem may be the syner-gistic combination of multiple forms of neuronal plasticity. There has been extensive evidence that synaptic learning is accompanied by homeostatic mechanisms. Synaptic scaling regulates the total synaptic drive received by a neuron but maintains the relative strength of synapses established during learning (Turrigiano et al., 1998). At the same time, intrinsic plasticity (IP) was shown to directly regulate neuronal excitability (Desai et al., 1999; Zhang and Linden, 2003). In a RNN, IP induced robust homeostatic effects on the network dynamics (Steil, 2007; Schrauwen et al., 2008). But there is only little work combining several forms of plasticity in RNNs (Lazar et al., 2007). In the following, we present a RNN of threshold units combining three different forms of plasticity that learns to effi ciently represent and " understand " the spatio-temporal patterns in its input. The SORN model (self-organizing recurrent network) consists
Spatial distribution of micrometre‐scale porosity and permeability across the damage zone of a reverse‐reactivated normal fault in a tight sandstone: Insights from the Otway Basin, SE Australia

Knowledge of the permeability structure of fault‐bearing reservoir rocks is fundamental for developing robust hydrocarbon exploration and fluid monitoring strategies. Studies often describe the permeability structure of low porosity host rocks that have experienced simple tectonic histories, while investigations of the influence of faults with multiple‐slip histories on the permeability structure of porous clastic rocks are limited. We present results from an integrated petrophysical, microstructural, and mineralogical investigation of the Eumeralla Formation (a tight volcanogenic sandstone) within the hanging wall of the Castle Cove Fault which strikes 30 km NE–SW in the Otway Basin, southeast Australia. This late Jurassic to Cenozoic‐age basin has experienced multiple phases of extension and compression. Core plugs and thin sections oriented relative to the fault plane were sampled from the hanging wall at distances of up to 225 m from the Castle Cove Fault plane. As the fault plane is approached, connected porosities increase by ca. 10% (17% at 225 m to 24% at 0.5 m) and permeabilities increase by two orders of magnitude (from 0.04 mD at 225 m to 1.26 mD at 0.5 m). Backscattered Scanning Electron Microscope analysis shows that microstructural changes due to faulting have enhanced the micrometre‐scale permeability structure of the Eumeralla Formation. These microstructural changes have been attributed to the formation of microfractures and destruction of original pore‐lining chlorite morphology as a result of fault deformation. Complex deformation, that is, formation of macrofractures, variably oriented microfractures, and a hanging wall anticline, associated with normal faulting and subsequent reverse faulting, has significantly influenced the off‐fault fluid flow properties of the protolith. However, despite enhancement of the host rock permeability structure, the Eumeralla Formation at Castle Cove is still considered a tight sandstone. Our study shows that high‐resolution integrated analyses of the host rock are critical for describing the micrometre‐scale permeability structure of reservoir rocks with high porosities, low permeabilities, and abundant clays that have experienced complex deformation.
The Efficacies of Utilizing a Multimedia Based Instructional Supplement on Learners’ Cognitive Skills

Calls for creative and more interesting ways of teaching and learning continue to echo in academia. In this study, we conduct an experiment to determine whether a multimedia based instructional supplement (MBIS), prepared using business and accounting students as presenters enhances any or all four lower-level Bloom’s taxonomy cognitive skills of remembering, understanding, applying, and analyzing. We offer further and unique insight towards the understanding of technology’s impact on learning since this study identifies the specific students’ cognitive skills impacted by the use of technology for learning. Using the post-test control group design, students were randomly assigned to either the experimental or control group. The experimental group used the MBIS to prepare for a prior-to-the-lecture quiz. The control group received traditional type material (text only that included PowerPoint slides) for them to read in preparation for the same quiz. We perform univariate as well as a multivariate analysis of covariance test (MANCOVA) to test for differences between the groups. Results indicate that such a MBIS is beneficial not only for enhancing students’ overall performance, but more specifically in enhancing understanding, applying, and analyzing levels of cognitive skills.
Dynamic context generation for natural language understanding: a multifaceted knowledge approach

We describe a comprehensive framework for text understanding, based on the representation of context. It is designed to serve as a representation of semantics for the full range of interpretive and inferential needs of general natural language processing. Its most distinctive feature is its uniform representation of the various simple and independent linguistic sources that play a role in determining meaning: lexical associations, syntactic restrictions, case-role expectations, and most importantly, contextual effects. Compositional syntactic structure from a shallow parsing is represented in a neural net-based associative memory, where it then interacts through a Bayesian network with semantic associations and the context or "gist" of the passage carried forward from preceding sentences. Experiments with more than 2000 sentences in different languages are included.
Climate engineering with stratospheric sulphate aerosol : development and application of a global atmosphere-aerosol model for studying potential efficacy and impacts

The enhancement of the stratospheric sulphate aerosol layer has been proposed as a method to abate the global warming caused by anthropogenic greenhouse gas emissions. In this thesis we present a newly developed global atmosphere-aerosol model, evaluate its performance against observations, and apply it to study the effectiveness and impacts of this possible form of climate engineering. We find that the achievable cooling effect per injected sulphur mass unit may decrease more drastically for larger injections than previously estimated and that injections at higher altitude or over larger areas do not result in a stronger cooling. The effectiveness of the method may therefore be rather limited, at least when using tropical injections of sulphur dioxide as in our model experiments. In addition, there are several potentially harmful side effects, such as stratospheric heating due to absorption of radiation by the aerosol causing strong perturbations in atmospheric dynamics, composition, and high-level clouds. Furthermore, we find that the radiative effects of stratospheric aerosol injection and marine cloud brightening, another proposed geoengineering technique, would be largely additive and complementary when applying them together. This might allow the design of portfolio approaches to achieve specific climate goals and reduce unintended side effects of climate engineering.
A Parametrization of Digital Planes by Least-Squares Fits and Generalizations

In this paper we prove that digital plane segments and their least-squares plane fit are in one-to-one correspondence, which gives a simple representation of a digital plane segment by its base description and coefficients of the least-squares plane fit. This leads to a constant space representation of digital rectangles in space. The method used is generalized and modified for constant space representation of sets which may consists of digital surface segments of different kinds.
Drivers of Variability in Public‐Supply Water Use Across the Contiguous United States

This study explores the relationship between municipal water use and an array of climate, economic, behavioral, and policy variables across the contiguous U.S. The relationship is explored using Bayesian‐hierarchical regression models for over 2,500 counties, 18 covariates, and three higher‐level grouping variables. Additionally, a second analysis is included for 83 cities where water price and water conservation policy information is available. A hierarchical model using the nine climate regions (product of National Oceanic and Atmospheric Administration) as the higher‐level groups results in the best out‐of‐sample performance, as estimated by the Widely Available Information Criterion, compared to counties grouped by urban continuum classification or primary economic activity. The regression coefficients indicate that the controls on water use are not uniform across the nation: e.g., counties in the Northeast and Northwest climate regions are more sensitive to social variables, whereas counties in the Southwest and East North Central climate regions are more sensitive to environmental variables. For the national city‐level model, it appears that arid cities with a high cost of living and relatively low water bills sell more water per customer, but as with the county‐level model, the effect of each variable depends heavily on where a city is located.
Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)

Neural Module Networks, originally proposed for the task of visual question answering, are a class of neural network architectures that involve human-specified neural modules, each designed for a specific form of reasoning. In current formulations of such networks only the parameters of the neural modules and/or the order of their execution is learned. In this work, we further expand this approach and also learn the underlying internal structure of modules in terms of the ordering and combination of simple and elementary arithmetic operators. We utilize a minimum amount of prior knowledge from the human-specified neural modules in the form of different input types and arithmetic operators used in these modules. Our results show that one is indeed able to simultaneously learn both internal module structure and module sequencing without extra supervisory signals for module execution sequencing. With this approach, we report performance comparable to models using hand-designed modules. In addition, we do a analysis of sensitivity of the learned modules w.r. t. the arithmetic operations and infer the analytical expressions of the learned modules.
Why Do Modeled and Observed Surface Wind Stress Climatologies Differ in the Trade Wind Regions

AbstractGlobal climate models (GCMs) exhibit stronger mean easterly zonal surface wind stress and near-surface winds in the Northern Hemisphere (NH) trade winds than observationally constrained reanalyses or other observational products. A comparison, between models and reanalyses, of the processes that contribute to the zonal-mean, vertically integrated balance of momentum reveals that this wind stress discrepancy cannot be explained by either the resolved dynamics or parameterized tendencies that are common to each. Rather, a substantial residual exists in the time-mean momentum balance of the reanalyses, pointing toward a role for the analysis increments. Indeed, they are found to systematically weaken the NH near-surface easterlies in winter, thereby reducing the diagnosed surface wind stress. Similar effects are found in the Southern Hemisphere, and further analysis of the spatial structure and seasonality of these increments demonstrates that they act to weaken the near-surface flow over much of the.
PUMA 560 T RAJECTORY C ONTROL USING NSGA-II T ECHNIQUE W ITH R EAL V ALUED O PERATORS

In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems. The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under M ATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively.
Transdimensional Bayesian Attenuation Tomography of the Upper Inner Core

Following the linearized attenuation tomography from our previous study (Pejić et al., 2017, https://doi.org/10.1002/2016JB013692), we perform hierarchical transdimensional Bayesian tomography of the upper ≈400 km of the inner core, using 398 globally distributed t∗ estimates. The results are in good agreement with the ones obtained through linearized tomography: they show more complex attenuation pattern than the purely hemispherical one, and the noise estimated from the hierarchical inversion is in good agreement with estimates obtained from the Discrepancy Principle in the previous study. The attenuation pattern we observe gives more weight to the geodynamical models that couple the thermal anomalies of the lowermost mantle to the inner core boundary.
of the 17th USENIX Symposium on Networked Systems Design and Implementation

Network management is becoming increasingly automated, and automation depends on detailed, explicit representations of data about the state of a network and about an operator’s intent for its networks. In particular, we must explicitly represent the desired and actual topology of a network. Almost all other network-management data either derives from its topology, constrains how to use a topology, or associates resources (e.g., addresses) with speciﬁc places in a topology. MALT, a Multi-Abstraction-Layer Topology representation, supports virtually all network management phases: design, deployment, conﬁguration, operation, measurement, and analysis. MALT provides interoperability across our network-management software, and its support for abstraction allows us to explicitly tie low-level network elements to high-level design intent. MALT supports a declarative style, simplifying what-if analysis and testbed support. We also describe the software base that supports efﬁ-cient use of MALT, as well as numerous, sometimes painful lessons we have learned about curating the taxonomy for a comprehensive, and evolving, representation for topology.
Automated Composition of Scientific Workflows in Mass Spectrometry-Based Proteomics

Numerous software utilities operating on mass spectrometry (MS) data are described in the literature that provide specific operations as building blocks for the assembly of purposespecific workflows. Working out which tools and combinations are applicable or optimal is often hard: insufficient annotation of tool functions and interfaces impedes finding viable tool combinations, and potentially compatible tools may not, in practice, operate together. Thus researchers face difficulties in selecting practical and effective data analysis pipelines for a specific experimental design.
Ewingite: Earth’s most complex mineral

The newly discovered mineral ewingite is the most structurally complex mineral known. Ewingite is found in the abandoned Plavno mine in the Jáchymov ore district, western Bohemia (Czech Republic), and was studied by synchrotron X-ray diffraction. The structure of ewingite contains nanometer-scale anionic uranyl carbonate cages that contain 24 uranyl polyhedra, as well as Ca and Mg cations and H2O groups located in interstitial regions inside and between the cages. The discovery of ewingite suggests that nanoscale uranyl carbonate cages could be aqueous species in some systems, and these may affect the geochemical behavior of uranium.
Mitigating DNS Random Subdomain DDoS Aacks by Distinct Heavy Hiers Sketches

Random Subdomain DDoS aacks on the Domain Name System (DNS) infrastructure are becoming a popular vector in recent aacks (e.g., recent Mirai aack on Dyn). In these aacks, many queries are sent for a single or a few victim domains, yet they include highly varying non-existent subdomains generated randomly. Motivated by these aacks we designed and implemented novel and ecient algorithms for distinct heavy hiers (dHH). A (classic) heavy hier (HH) in a stream of elements is a key (e.g., the domain of a query) which appears in many elements (e.g., requests). When stream elements consist of ¡key, subkey¿ pairs, (¡domain, subdomain¿) a distinct heavy hier (dhh) is a key that is paired with a large number of dierent subkeys. Our algorithms dominate previous designs in both the asymptotic (theoretical) sense and practicality. Specically the new xed-size algorithms are simple to code and with asymptotically optimal space accuracy tradeos. Based on these algorithms, we build and implement a system for detection and mitigation of Random Subdomain DDoS aacks. We perform experimental evaluation, demonstrating the eectiveness of our algorithms.
A Link-Layer Synchronization and Medium Access Control Protocol for Terahertz-Band Communication Networks

In this paper, a link-layer synchronization and medium access control (MAC) protocol for very-high-speed wireless communication networks in the Terahertz (THz) band is presented. The protocol relies on a receiver-initiated handshake to guarantee synchronization between transmitter and receiver. Two scenarios are considered, namely, a macroscale scenario, where nodes utilize rotating directional antennas to periodically sweep the space while overcoming the distance problem at THz frequencies, and a nanoscale scenario, where nano-devices require energy harvesting systems to operate. Both scenarios are implemented on a centralized and an ad-hoc network architecture. A carrier-based physical layer is considered for the macro-scenario, whereas the physical layer for the nano-scenario is based on a femtosecond-long pulse-based modulation scheme with packet interleaving. The performance of the proposed MAC protocol is analytically investigated in terms of delay, throughput and probability of successful packet delivery, and compared to that of an adapted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) with and without handshake. The results are validated by means of extensive simulations with ns-3, in which all the necessary THz elements have been implemented. The results show that the proposed protocol can maximize the successful packet delivery probability without compromising the achievable throughput in THz-band communication networks.
Optimizing Storage Performance with Calibrated Interrupts

After request completion, an I/O device must decide whether to minimize latency by immediately firing an interrupt or to optimize for throughput by delaying the interrupt, anticipating that more requests will complete soon and help amortize the interrupt cost. Devices employ adaptive interrupt coalescing heuristics that try to balance between these opposing goals. Unfortunately, because devices lack the semantic information about which I/O requests are latency-sensitive, these heuristics can sometimes lead to disastrous results. Instead, we propose addressing the root cause of the heuristics problem by allowing software to explicitly specify to the device if submitted requests are latency-sensitive. The device then “calibrates” its interrupts to completions of latency-sensitive requests. We focus on NVMe storage devices and show that it is natural to express these semantics in the kernel and the application and only requires a modest two-bit change to the device interface. Calibrated interrupts increase throughput by up to 35%, reduce CPU consumption by as much as 30%, and achieve up to 37% lower latency when interrupts are coalesced.
Minimizing Stall Time in Single and Parallel Disk Systems Using Multicommodity Network Flows

We study integrated prefetching and caching in single and parallel disk systems. A recent approach used linear programming to solve the problem. We show that integrated prefetching and caching can also be formulated as a min-cost multicommodity flow problem and, exploiting special properties of our network, can be solved using combinatorial techniques. Moreover, for parallel disk systems, we develop improved approximation algorithms, trading performance guarantee for running time. If the number of disks is constant, we achieve a 2-approximation.
Word Sense Disambiguation

Ambiguity and human language have been tangled since the rise of philological communication. One of the long established problems of Natural Language Processing (NLP) is Word Sense Disambiguation (WSD). Researchers have been diligently trying to deal with this problem since the birth of Machine Translation. Word sense disambiguation (WSD) can be defined as the aptitude to recognize the meaning of words in the given context in a computational manner. WSD is an AI-complete problem, that is, a problem having its solution at least as hard as the most difficult problems in the field of artificial intelligence.
Channel Models for Fixed Wireless Applications

Source(s) V. Erceg, Iospan Wireless Inc., USA K.V. S. Hari, Stanford University, USA M.S. Smith, Nortel Networks, GB D.S. Baum, Stanford University, USA K.P. Sheikh, Sprint, USA C. Tappenden, Nortel Networks, CND J.M. Costa, Nortel Networks, CND C. Bushue, Sprint, USA A. Sarajedini, BeamReach Networks R. Schwartz, BeamReach Networks D. Branlund, BeamReach Networks T. Kaitz, BreezeCOM D. Trinkwon Voice: 408-232-7551, verceg@iospanwireless.com Voice: 650-724-3640, hari@rascals.stanford.edu Voice: +44-127-940-2128, mss@nortelnetworks.com Voice: 650-724-3640, dsbaum@stanford.edu Voice: 913-315-9420, khurram.p.sheikh@mail.sprint. com Voice: 613-763-9894, ctappend@nortelnetworks.com Voice: 613-763-7574, costa@nortelnetworks.com Voice: 913-624-3090, carl.bushue@mail.sprint. com Voice: 650-944-1294, asarajedini@beamreachnetworks.com Voice: 650-988-4758, rschwartz@beamreachnetworks.com Voice: 650-988-4703, dbranlund@beamreachnetworks.com Voice: +972-3-645-6273, talk@breezecom.co.il Voice: 650-245-5650, trinkwon@compuserve.com