Miracle-dz/UniFAR-FTUs huggingface dataset:
Event-Response Ellipses: A Method to Quantify and Compare the Role of Dynamic Storage at the Catchment Scale in Snowmelt-Dominated Systems

A method for quantifying the role of dynamic storage as a physical buffer between snowmelt and streamflow at the catchment scale is introduced in this paper. The method describes a quantitative relation between hydrologic events (e.g., snowmelt) and responses (e.g., streamflow) by generating event-response ellipses that can be used to (a) characterize and compare catchment-scale dynamic storage processes, and (b) assess the closure of the water balance. Event-response ellipses allow for the role of dynamic, short-term storage to be quantified and compared between seasons and between catchments. This method is presented as an idealization of the system: a time series of a snowmelt event as a portion of a sinusoidal wave function. The event function is then related to a response function, which is the original event function modified mathematically through phase and magnitude shifts to represent the streamflow response. The direct relation of these two functions creates an event-response ellipse with measurable characteristics (e.g., eccentricity, angle). The ellipse characteristics integrate the timing and magnitude difference between the hydrologic event and response to quantify physical buffering through dynamic storage. Next, method is applied to eleven snowmelt seasons in two well-instrumented headwater snowmelt-dominated catchments with known differences in storage capacities. Results show the time-period average daily values produce different event-response ellipse characteristics for the two catchments. Event-response ellipses were also generated for individual snowmelt seasons; however, these annual applications of the method show more scatter relative to the time period averaged values. The event-response ellipse method provides a method to compare and evaluate the connectivity between snowmelt and streamflow as well as assumptions of water balance.
GEOMETRY OF HOLOCENE TIDAL NOTCHES – SEA LEVEL MARKERS AT PERACHORA PENINSULA, GULF OF CORINTH, GREECE

Tidal notches are a generally accepted sea level indicator that, when different from mean sea level, witness tectonic activity at or near coastlines. However, how to infer related information is controversial since tectonic uplift from a single seismic event is not likely to exceed several decimetres. High resolution laser scanning offers the availability of close-up views on exposures and to detect evidence for multiple sea level indicators in between major emergence. Statistically representative profiles along exposure were analysed in order to prove for already described tidal notches and to highlight similar shapes in consistent geometries along coastal cliffs of Perachora Peninsula.
Gene expression Incorporating biological knowledge into distance-based clustering analysis of microarray gene expression data

Motivation: Because co-expressed genes are likely to share the same biological function, cluster analysis of gene expression profiles has been applied for gene function discovery. Most existing clustering methods ignore known gene functions in the process of clustering. Results: To take advantage of accumulating gene functional annotations, we propose incorporating known gene functions into a new distance metric, which shrinks a gene expression-based distance towards 0 if and only if the two genes share a common gene function. A two-step procedure is used. First, the shrinkage distance metric is used in any distance-based clustering method, e.g. K-medoids or hierarchical clustering, to cluster the genes with known functions. Second, while keeping the clustering results from the first step for the genes with known functions, the expression-based distance metric is used to cluster the remaining genes of unknown function, assigning each of them to either one of the clusters obtained in the first step or some new clusters. A simulation study and an application to gene function prediction for the yeast demonstrate the advantage of our proposal over the standard method. Contact: weip@biostat.umn. edu
Continuously Rounding in Peer-to-Peer Networks

We quantify the effectiveness of random walks for searching and construction of unstructured peer-to- peer (P2P) networks. For searching, we argue that random walks achieve improvement over loading in the case of clustered overlay topologies and in the case of re-issuing the same request several times. For construction, we argue that an expander can be maintained dynamically with constant operations per addition. The key technical ingredient of our approach is a deep result of stochastic processes indicating that samples taken from consecutive steps of a random walk can achieve statistical properties similar to independent sampling.
MAVE-NN: Quantitative Modeling of Genotype-Phenotype Maps as Information Bottlenecks

Multiplex assays of variant eﬀect (MAVEs) are being rapidly adopted in many areas of biology including gene regulation, protein science, and evolution. However, inferring quantitative models of genotype-phenotype maps from MAVE data remains a challenge. Here we introduce MAVE-NN, a neural-network-based Python package that addresses this problem by conceptualizing genotype-phenotype maps as information bottlenecks. We demonstrate the versatility, performance, and speed of MAVE-NN on a diverse range of published MAVE datasets. MAVE-NN is easy to install and is thoroughly documented at https://mavenn.readthedocs. io .
Supplementary Material Unsupervised Learning of Object Landmarks via Self-Training Correspondence

Experimental setup for ablation study: We report a set of experiments on the ablation study investigating robustness to label noise and impact of number of clusters (Section 4 in the main submission). To perform these experiments, we populate each image with an initial set of points, mixed from a pool of ground-truth landmarks and a set of randomly chosen points. The initial number of points per image is randomly chosen to be between 12 and 24, so as to simulate the number of keypoints that are usually returned by SuperPoint. The ground-truth keypoint locations are sampled uniformly from a subset of 15 facial landmarks (3 on the nose and mouth, 2 on each eye and eyebrow and 1 on the jaw), while the remaining points are random 2D locations inside the facial bounding box. The ground-truth points are also randomly distorted within a 3 pixels radius.
Fuzzy Control of a Three Phase Thyristorized Induction Motor V

frequency is one of the traditional and low expense methods in order to control the speed of induction motors near its nominal speed. The torque of induction motor is a nonlinear function of the firing angle, phase angle and speed. In this paper the speed control of induction motor regarding various load torque and under different conditions will be investigated based on a fuzzy controller with inverse training.
Now IT ' s Personal : Offshoring and the Shifting Skill Composition of the U . S . Information Technology Workforce

We combine new information technology (IT) offshoring and IT workforce microdata to investigate how the use of IT offshore captive centers is affecting the skill composition of the U.S. onshore IT workforce. The analysis is based on the theory that occupations involving tasks that are “tradable,” such as tasks that require little personal communication or hands-on interaction with U.S.-based objects, are vulnerable to being moved offshore. Consistent with this theory, we find that firms that have offshore IT captive centers have 8% less of their onshore IT workforce involved in tradable occupations; those without offshore captive centers have increased the proportion of onshore employment in these same occupations by 3%. In addition, we find that hourly IT workers (e.g., IT contractors) are disproportionately employed in tradable jobs, and their onshore employment is 2%–3% lower in firms with offshore captive centers. These findings persist after considering different measures of employment composition, including controls for human capital, firm performance, domestic outsourcing, and whether firms choose to build or buy software. Instrumental variables and corroborating regressions suggest that our estimates are conservative—the magnitude of the effect generally rises after accounting for reverse causality and measurement error.
One-Shot Image Recognition Using Prototypical Encoders with Reduced Hubness Supplementary Materials

For a fair comparison, the results reported in this work on two experimental scenarios (Belga –>Flickr32, and Belga –>Toplogos) given in Table 1 and 2 are acquired using the same data augmentation techniques as [3], which only includes random rotation and horizontal flipping. Since the self-supervised contrastive learning benefits from data augmentation [1], the performance can be further boosted by incorporating additional data variations. The following ablation study is conducted on Belga–>Flickr32 and Belga –>Toplogos scenarios. We applied color jittering on top of the existing data augmentation techniques, including 1) Uniformly jittering image brightness, contrast, saturation from -80% to 80%, and 2) Uniformly jittering image hue from -20% to 20%. We compare the performance before and after applying the additional data augmentation in Table 5. Results show that the additional data augmentation can lead to superior results.
Process-based environmental models tree transpiration: A case study of rubber tree (Hevea brasiliensis)

Nowadays, tree transpiration (ETree) limitation is investigated in several plant species especially the commercial trees such as the rubber tree. Exceptional tree physiology responds to droughts, the modeling for prediction of ETree is also interest. The aim of this investigation was to evaluate the environmental model for ETree estimation in all leaf phenology under a wide range of soil water availability and evaporative demand. The results showed that the environmental model called ‘Fsoil_Emax’ (which considered only soil water available affect) produced a reduction of estimated ETree the same as the reducing pattern of actual measured in drought conditions. But there was no variability of estimated ETree when evaporative demand changed. The improving of the environmental model by added the reference evapotranspiration (ET0 ) which alternated in minimum value between ET0 and Emax, called Fsoil*MIN(Emax;ET0 ) model, produced a reduction in estimated ETree in both soil drought and low evaporative demand conditions at fully mature leaves stage. Therefore, this model was optimum for estimating transpiration under various conditions. Moreover, the annual accumulated ETree of the improved model slightly overestimated the measured value by 20 mm. However, this model produced estimated ETree at fully mature leaves stage better than during leaf shedding-flushing stage.
Impact of gate operating modes of sea dikes on hydrodynamic regime and inundated area in Can Gio Bay

ABSTRACT Ho Chi Minh City gets severely inundated by upstream flooding and a combination of rising spring tides and rain, owing to the combined impacts of rapid urbanization and sea level changes. A sea dike to close Can Gio Bay has been proposed to regulate storage and control water levels in the drainage and sewer system of Ho Chi Minh City. However, this proposed sea dike and different gate operating modes could significantly change the hydrodynamic regime in Can Gio Bay. In this study, a two-dimensional hydrodynamic model was developed to assess changes in the hydrodynamic regime after construction of the sea dike, including fluctuations in the inundated areas in Can Gio Bay mangrove forests. The results show that sea dike construction is an effective solution for controlling water levels and reducing inundation, especially considering a sea level rise scenario in the year 2050. Further, the gate operation mode also affects the effectiveness of inundation reduction, which is approximately 10% better than the same sea dike type without gate operation. However, the sea dike was less effective in a sea water level rise scenario in 2100 for all scenarios.
Temporal Characteristics of Actual Evapotranspiration Over China Under Global Warming

In this study, an assessment is made of the trends of actual evapotranspiration (AE) over China under global warming from an ensemble of the following data sets: National Centers for Environmental Prediction‐National Center for Atmospheric Research reanalysis I, National Centers for Environmental Prediction‐Department of Energy reanalysis II, Modern‐Era Retrospective Analysis for Research and Applications (MERRA), MERRA Version 2, European Centre for Medium‐Range Weather Forecasts Interim Re‐Analysis, and Japanese 55‐year Reanalysis. For China as a whole, annual AE exhibited a significant increasing trend from 1979 to 2015, and the rate of increase was highest in autumn and lowest in summer. By subdividing China into six climatic regions using the aridity index, significant increasing trends in AE are found over the hyperarid, arid, and humid regions, but slightly decrease over the dry subhumid and subhumid regions. For the hyperarid and arid regions, the increases in potential evapotranspiration and the water supply from melting glaciers due to both climate warming and human activities might be the main contributors to the AE increases. An increase in potential evapotranspiration driven by global warming is the main cause of the increased AE over the humid region. On a seasonal basis, most AE over China occurs in summer. However, increasing AE trends have been observed mainly in autumn and winter, and the increase is statistically significant over all of China's six climatic regions in winter. In view of data availability, reanalyses are compared with the results from Mezentsev‐Choudhury‐Yang equation (AE_Budyko). The correlations between individual reanalyses and AE_Budyko tend to be higher over the arid region.
Design, fabrication and application of a microfluidic nanofiltration module for separation and purification of macromolecules and nanoparticles

approved: ______________________________________________________ Vincent T. Remcho A microfluidic nanofiltration module has been designed, fabricated and applied to the continuous-flow, pressure-driven, post-synthetic purification of macromolecules and nanoparticles via diafiltration using a commercially available organic solvent resistant nanofiltration membrane, STARMEM 122. This module will readily interface with other microscale components within a “nanofactory” for the rapid synthesis, purification and delivery of highly dispersed macromolecules and nanoparticles. The microfluidic nanofiltration module (MNM) fully integrates the membrane into an all-polymer format constructed of materials that are relatively inexpensive, chemically compatible with the targeted compounds and transmissive to visible light allowing optical monitoring of the system. A molecular weight cutoff of 2.3 kDa was determined for the MNM using half-generation poly(amidoamine) dendrimer standards in a simulated postsynthetic mixture of 4 mM methyl acrylate in methanol. The membrane was also characterized within a macroscale test fixture (MTF), constructed of reusable parts that are easily disassembled for rapid replacement of the membrane. Rejections of 95% to 97% were observed in the MTF for triphenylphosphine (PPh3) stabilized gold-eleven (Au11) nanoparticle standards. Rejections of 12% to 58% were also observed for PPh3, an anticipated post-synthetic byproduct. Purification via diafiltration of realworld post-synthetic Au11 mixtures was demonstrated in the MTF. Au11 rejections > 99% were observed along with expected decreases in PPh3 concentrations. Stable permeances of 2 L m h bar were also observed, comparable to those reported in the literature and claimed by the membrane manufacturer. The purity of the Au11 final product was comparable to that of the crystalline Au11 standard purified by traditional means. These results suggest that a diafiltration system incorporating an organic solvent resistant nanofiltration membrane would be practical for rapid purification and high product recovery of gold nanoparticles in an organic solvent environment immediately downstream of a microreactor within a nanofactory architecture. ©Copyright by Jack T. Rundel January 16, 2008 All Rights Reserved Design, Fabrication and Application of a Microfluidic Nanofiltration Module for Separation and Purification of Macromolecules and Nanoparticles
A Short Review of Neural Network Techniques in Visual Servoing of Robotic Manipulators

Robotics is one of the most challenging applications of soft computing techniques. It is characterized by direct interaction with a real world, sensory feedback and a complex control system. This paper reviews the application of soft computing approaches, particularly neural networks, in the domain of visual servoing of robotic manipulators. Various robotic tasks within the scope of visual servoing are identiﬁed, and the issues involving the application of soft computing approaches in solving these problems are discussed. The paper provides some practical suggestions in applying neural networks for these tasks.
Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction

Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News. Another challenge is that the stocks in the market are not independent, which is omitted by previous works. To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Extensive experiment results show that our model outperforms the baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.
Symbolic Reasoning for Hearthstone

—Trading-Card-Games are an interesting problem domain for Game AI, as they feature some challenges, such as highly variable game mechanics, that are not encountered in this intensity in many other genres. We present an expert system forming a player-level AI for the digital Trading-Card-Game Hearthstone. The bot uses a symbolic approach with a semantic structure, acting as an ontology, to represent both static descriptions of the game mechanics and dynamic game-state memories. Methods are introduced to reduce the amount of expert knowledge, such as popular moves or strategies, represented in the ontology, as the bot should derive such decisions in a symbolic way from its knowledge base. We narrow down the problem domain, selecting the relevant aspects for a play-to-win bot approach and comparing an ontology-driven approach to other approaches such as machine learning and case-based reasoning. Upon this basis, we describe how the semantic structure is linked with the game-state and how different aspects, such as memories, are encoded. An example will illustrate how the bot, at runtime, uses rules and queries on the semantic structure combined with a simple utility system to do reasoning and strategic planning. Finally, an evaluation is presented that was conducted by fielding the bot against the stock “Expert” AI that Hearthstone is shipped with, as well as Human opponents of various skill levels in order to assess how well the bot plays. Evaluating how believable the bot reasons is assessed through a Pseudo-Turing test.
A Comparison Between Three SDLC Models Waterfall Model, Spiral Model, and Incremental/Iterative Model

The computer has become indispensable in today’s life, and it is widely used in many fields of life such as commerce, education, industry…etc. The computer saves time in regarding to help solving complex, long, repeated processes in a short time and high speed. As the software programs need to handle these features, many companies produce software programs to facilitate the works for administrations, banks, offices, etc. Moreover, software has been in used for analyzing information or solving problems for more than four decades. Creating a suitable work to develop programs of high quality is the main goal of the software engineering. Usually, clients seek the assistance from computer and software engineers to solve and handle their problems. There are various models have been widely in used to develop software products. Common models will be described in this paper.
Multiquantal Release Underlies the Distribution of Synaptic Efficacies in the Neocortex

Inter-pyramidal synaptic connections are characterized by a wide range of EPSP amplitudes. Although repeatedly observed at different brain regions and across layers, little is known about the synaptic characteristics that contribute to this wide range. In particular, the range could potentially be accounted for by differences in all three parameters of the quantal model of synaptic transmission, i.e. the number of release sites, release probability and quantal size. Here, we present a rigorous statistical analysis of the transmission properties of excitatory synaptic connections between layer-5 pyramidal neurons of the somato-sensory cortex. Our central finding is that the EPSP amplitude is strongly correlated with the number of estimated release sites, but not with the release probability or quantal size. In addition, we found that the number of release sites can be more than an order of magnitude higher than the typical number of synaptic contacts for this type of connection. Our findings indicate that transmission at stronger synaptic connections is mediated by multiquantal release from their synaptic contacts. We propose that modulating the number of release sites could be an important mechanism in regulating neocortical synaptic transmission.
Ancient-Modern Chinese Translation with a Large Training Dataset

Ancient Chinese brings the wisdom and spirit culture of the Chinese nation. Automatically translation from ancient Chinese to modern Chinese helps to inherit and carry forward the quintessence of the ancients. In this paper, we propose an Ancient-Modern Chinese clause alignment approach and apply it to create a large scale Ancient-Modern Chinese parallel corpus which contains about 1.24M bilingual pairs. To our best knowledge, this is the first large high-quality Ancient-Modern Chinese dataset. Furthermore, we train the SMT and various NMT based models on this dataset and provide a strong baseline for this task
A Probe Placement Method for Efficient Electromagnetic Attacks

Electromagnetic (EM) emissions have been explored as an effective means for non-invasive side-channel attacks. The leaked EM field from the memory bus when the data is loaded from the on-chip memory has received considerable attention in literature. Meanwhile, off-chip memory buses gradually become the new attack target due to the relative ease of access in the modern system in package technologies, such as 2.5-D integration where processing and memory chips are integrated, for example, on a silicon interposer. This paper, therefore, investigates EM snooping attacks on interposer-based off-chip memory buses. A gradient-search algorithm is proposed to locate fast (i.e. O(N)) the most efficient attack point. The effectiveness of the search algorithm and attack efficiency is evaluated on a 64-bit bus. It is demonstrated that at the optimal attack point, EM attacks can succeed with more than 10× fewer traces, compared to placing the probe to sub-optimal locations.