Miracle-dz/UniFAR-FTUs huggingface dataset:
A fast direct solver for the advection-diffusion equation using low-rank approximation

Today’s most powerful supercomputers make use of many thousands or millions of parallel processors to achieve high speeds. However, some of the core solution algorithms in computational physics codes are not well suited to this environment. Linear scaling is generally lost as the number of processors becomes very large. New solution algorithms specifically designed for massively parallel execution are urgently needed. In this context, hierarchical algorithms such as the Fast Multipole Method (FMM) [1], H matrices [2] and H matrices [3] have considerable potential. They can solve elliptic PDEs with N degrees of freedom in O(N logN) or O(N) operations and demonstrate excellent parallel scaling [4, 5]. There is one catch, however: hierarchical methods are only useful for PDEs with a Green’s function that decays with increasing distance between two points. Then one can compute a low-rank approximation of the inverse of the system matrix by hierarchically compressing blocks far from the diagonal. Hyperbolic PDEs do not satisfy this criterion. However, Bull et al. [6] showed that by discretising the 1D periodic advection-diffusion equation in time such that the advective term was explicit and the diffusion term implicit, then the linear system was suitable for low-rank approximation. Simple non-hierarchical low-rank approximation methods were able to obtain linear scaling of CPU time with N on a serial processor. In this talk, the ideas are extended to two dimensions and non-periodic domains. New analysis and results are presented. Figure 1 shows the exact solution (left) and solution obtain with the direct solver (right) for propagation of a Gaussian initial condition on a non-periodic domain.
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information ﬂows in data communications networks. These preliminary models often rely on off-the-shelf learning models to predict from historical statistics while disregarding the physics governing the generating behaviors of these ﬂows. This paper instead introduces Flow N eural N etwork (FlowNN) to improve the feature representation with learned physical bias. This is implemented by an induction layer, working upon the embedding layer, to impose the physics connected data correlations, and a self-supervised learning strategy with stop-gradient to make the learned physics universal. For the short-timescale network prediction tasks, FlowNN achieves 17% ∼ 71% of loss decrease than the state-of-the-art baselines on both synthetic and real-world networking datasets, which shows the strength of this new approach. Code will be made available.
Object-oriented Method of Hierarchical Urban Building Extraction from High-resolution Remote-Sensing Imagery

An automatic urban building extraction method for high-resolution remote-sensing imagery,which combines building segmentation based on neighbor total variations with object-oriented analysis,is presented in this paper. Aimed at different extraction complexity from various buildings in the segmented image,a hierarchical building extraction strategy with multi-feature fusion is adopted. Firstly,we extract some rectangle buildings which remain intact after segmentation through shape analysis. Secondly,in order to ensure each candidate building target to be independent,multidirectional morphological road-filtering algorithm is designed which can separate buildings from the neighboring roads with similar spectrum. Finally,we take the extracted buildings and the excluded non-buildings as samples to establish probability model respectively,and Bayesian discriminating classifier is used for making judgment of the other candidate building objects to get the ultimate extraction result. The experimental results have shown that the approach is able to detect buildings with different structure and spectral features in the same image. The results of performance evaluation also support the robustness and precision of the approach developed.
Model-Checking of Linear-Time Properties in Multi-Valued Systems

In this paper, we study model-checking of linear-time properties in multi-valued systems. Safety property, invariant property, liveness property, persistence and dual-persistence properties in multi-valued logic systems are introduced. Some algorithms related to the above multi-valued linear-time properties are discussed. The verification of multi-valued regular safety properties and multi-valued $\omega$-regular properties using lattice-valued automata are thoroughly studied. Since the law of non-contradiction (i.e., $a\wedge \neg a=0$) and the law of excluded-middle (i.e., $a\vee \neg a=1$) do not hold in multi-valued logic, the linear-time properties introduced in this paper have the new forms compared to those in classical logic. Compared to those classical model checking methods, our methods to multi-valued model checking are more directly accordingly. A new form of multi-valued model checking with membership degree is also introduced. In particular, we show that multi-valued model-checking can be reduced to the classical model checking. The related verification algorithms are also presented. Some illustrative examples and case study are also provided.
Policy optimization for dynamic power management

Dynamic power management schemes (also called policies) reduce the power consumption of complex electronic systems by trading off performance for power in a controlled fashion, taking system workload into account. In a power-managed system it is possible to set components into different states, each characterized by performance and power consumption levels. The main function of a power management policy is to decide when to perform component state transitions and which transition should be performed, depending on system history, workload, and performance constraints. In the past, power management policies have been formulated heuristically. The main contribution of this paper is to introduce a finite-state, abstract system model for power-managed systems based on Markov decision processes. Under this model, the problem of finding policies that optimally tradeoff performance for power can be cast as a stochastic optimization problem and solved exactly and efficiently. The applicability and generality of the approach are assessed by formulating the Markov model and optimizing power management policies for several systems.
Evaluation of the efficacy of contact and systemic fungicides for management of potato late blight in Uganda

Judicious use of fungicides is one of the major components for integrated management of late blight and it involves reducing the frequency of application, proper timing of application and use of appropriate dosage. Three dosages of Dithane M45 (40, 80 and 120 gm/litre) and Ridomil MZ 63.5 (30, 60, and 120 gm/litre) with a control, and 3 spray intervals were evaluated at Kalengyere in South western Uganda, during 1999, 2000 and 2001. The design was a randomised complete block in a split-split plot arrangement. Disease severity was assessed on variety Victoria, based on percent leaf area affected (PLAA) and relative areas under disease progress curves (RAUDPCs) computed. During the first season of 2000, RAUDPCs were not significantly different under different dosages and spray intervals for both Dithane M45 and Ridomil MZ 63.5. The yield data followed the same trend. During the second season, the RAUDPCs were not significantly different for the dosages of 80 and 120 g/l of Dithane M 45 at all the spray intervals, and this was also true for Ridomil MZ 63.5 applied at 60 and 120 g/l. For Dithane M 45 the highest dosage gave the highest yields at all spray intervals. Ridomil MZ 63.5 significantly reduced RAUDPCs, two to three times as compared to the unsprayed control, and increased yield three to four times.
ETHANOL-FED OR SOLID-PHASE ORGANIC SULFATE REDUCING BIOREACTORS FOR THE NATIONAL TUNNEL DRAINAGE, CLEAR CREEK/CENTRAL CITY SUPERFUND SITE?

The U.S. Environmental Protection Agency (EPA) is planning to treat mining influenced water (MIW) from the National Tunnel Adit that discharges to North Clear Creek near the City of Blackhawk, Colorado. North Clear Creek is part of the Clear Creek/Central City Superfund Site, and the National Tunnel is a major contributor of contaminants to this tributary. The EPA would like to determine the trade-offs between two modes sulfate reducing bioreactor (SRBR). One is an ethanol-fed SRBR and the other is a solid substrate fed SRBR. Ethanol fed and solid substrate-fed SRBR were operated in parallel. The bioreactors were constructed from 55-gallon drums. The nominal hydraulic residence time for the bioreactors initially was approximately 3 days for the period of July through September 2006 and later increased to 9 days for the period of October through November 2006. Measurable sulfate removal (approximately 100 mg/L as S) was noted by the end of July for all bioreactors. Sulfate removal increased through August 2006 and on September 7, 2006 was 170 to 200 mg/L as S. Sulfate removal decreased to 50 to 100 mg/L by the end of September. This decrease coincided with a drop in MIW and ambient air temperatures. The flow was decreased by a factor of three at the beginning of October, which resulted in increased sulfate removal in October and November relative to the end of September. Removal of zinc was observed prior to the onset of sulfate reduction. There was some variability in removal of zinc but greater than 95% zinc removal was observed for all bioreactors after the end of July. Both types of SRBRs were capable of reducing zinc concentrations to below 0.1 mg/L. The effect of cold temperatures was greater in the solid phase substrate bioreactors than the ethanol fed bioreactors. This suggests that cellulolytic fermenters were affected to a greater extent than sulfate reducers by cold temperature because they indirectly provide the soluble substrate for the sulfate reducers. _______________________________ 1 Paper was presented at the 2007 National Meeting of the American Society of Mining and Reclamation, Gillette, WY, 30 Years of SMCRA and Beyond June 2-7, 2007. R.I. Barnhisel (Ed.) Published by ASMR, 3134 Montavesta Rd., Lexington, KY 40502. Emma Buccambuso, Environmental Scientist (II), Tetra Tech, Lakewood, CO 80228, Alison Ruhs, Environmental Scientist, USEPA NEIC, Denver, CO 80225, Linda Figueroa, Associate Professor, Colorado School of Mines, Golden, CO 80401, James J. Gusek, Sr. Consultant, Golder Associates Inc., Lakewood, CO 80228, Thomas R. Wildeman, Professor Emeritus, CSM; Michael Holmes, Remedial Project Manager, US EPA Region 8, Denver, CO 80202, David Reisman, Director ETSC, USEPA NRMRL , Cincinnati, OH 45268
A Safety-Critical Decision-Making and Control Framework Combining
 Machine-Learning-Based and Rule-Based Algorithms

While machine-learning-based methods suffer from a lack of transparency, rule-based (RB) methods dominate safety-critical systems. Yet the RB approaches cannot compete with the first ones in robustness to multiple system requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, this article proposes a decision-making and control framework which profits from the advantages of both the RB and machine-learning-based techniques while compensating for their disadvantages. The proposed method embodies two controllers operating in parallel, called Safety and Learned. An RB switching logic selects one of the actions transmitted from both controllers. The Safety controller is prioritized whenever the Learned one does not meet the safety constraint, and also directly participates in the Learned controller training. Decision-making and control in autonomous driving are chosen as the system case study, where an autonomous vehicle (AV) learns a multitask policy to safely execute an unprotected left turn. Multiple requirements (i.e., safety, efficiency, and comfort) are set to vehicle motion. A numerical simulation is performed for the proposed framework validation, where its ability to satisfy the requirements and robustness to changing environments is successfully demonstrated.
Biologically Inspired Hierarchical Model for Feature Extraction and Localization

Some of the most important problems of computer vision are feature extraction and subsequent localization of those features in a new image. Since it is computationally prohibitive to search for the features over all possible locations and scales, it is necessary to design an algorithm that can selectively focus on and process information from only some regions within the image. In this work we present such an algorithm that is biologically inspired and performs a hierarchical search, from coarse to fine, in order to minimize the computational costs. The algorithm is very robust to non-linear image transformations such as changes in scale, rotation, skew, addition of noise, and changes in brightness and contrast. We demonstrate the computational efficiency as well as the effectiveness of the algorithm on several real world images
Informe Técnico / Technical Report

The increasing adoption of process-aware information systems (PAISs), together with the variability of business processes (BPs), has resulted in large collections of related process model variants (i.e., process families). To effectively deal with process families, several proposals (e.g., C-EPC, Provop) exist that extend BP modeling languages with variability-specific constructs. While fostering reuse and reducing modeling efforts, respective constructs imply additional complexity and demand proper support for process designers when creating and modifying process families. Recently, generic and language-independent adaptation patterns were successfully introduced for creating and evolving single BP models. However, they are not sufficient to cope with the specific needs for modeling and evolving process families. This paper suggests a complementary set of generic and language-independent change patterns specifically tailored to the needs of process families. When used in combination with existing adaptation patterns, change patterns for process families will enable the modeling and evolution of process families at a high-level of abstraction. Further, they will serve as reference for implementing tools or comparing proposals managing process families.
Empirical Study on Deep Learning Models for Question Answering

In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation, Neural Turing Machine, and Memory Networks for a simulated QA data set. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.
Inequalities and tail bounds for elementary symmetric polynomial with applications

We study the extent of independence needed to approximate the product of bounded random variables in expectation, a natural question that has applications in pseudorandomness and min-wise independent hashing. For random variables whose absolute value is bounded by $1$, we give an error bound of the form $\sigma^{\Omega(k)}$ where $k$ is the amount of independence and $\sigma^2$ is the total variance of the sum. Previously known bounds only applied in more restricted settings, and were quanitively weaker. We use this to give a simpler and more modular analysis of a construction of min-wise independent hash functions and pseudorandom generators for combinatorial rectangles due to Gopalan et al., which also slightly improves their seed-length. Our proof relies on a new analytic inequality for the elementary symmetric polynomials $S_k(x)$ for $x \in \mathbb{R}^n$ which we believe to be of independent interest. We show that if $|S_k(x)|,|S_{k+1}(x)|$ are small relative to $|S_{k-1}(x)|$ for some $k>0$ then $|S_\ell(x)|$ is also small for all $\ell > k$. From these, we derive tail bounds for the elementary symmetric polynomials when the inputs are only $k$-wise independent.
Analysis of Width-w Non-Adjacent Forms to Imaginary Quadratic Bases Clemens Heuberger and Daniel Krenn Project Area ( s ) : Analysis of Digital Expansions with Applications in Cryptography

We consider digital expansions to the base of τ , where τ is an algebraic integer. For a w ≥ 2, the set of admissible digits consists of 0 and one representative of every residue class modulo τw which is not divisible by τ . The resulting redundancy is avoided by imposing the width w-NAF condition, i.e., in an expansion every block of w consecutive digits contains at most one non-zero digit. Such constructs can be efficiently used in elliptic curve cryptography in conjunction with Koblitz curves. The present work deals with analysing the number of occurrences of a fixed non-zero digit. In the general setting, we study all w-NAFs of given length of the expansion. We give an explicit expression for the expectation and the variance of the occurrence of such a digit in all expansions. Further a central limit theorem is proved. In the case of an imaginary quadratic τ and the digit set of minimal norm representatives, the analysis is much more refined: We give an asymptotic formula for the number of occurrence of a digit in the w-NAFs of all elements of Z[τ ] in some region (e.g. a disc). The main term coincides with the full block length analysis, but a periodic fluctuation in the second order term is also exhibited. The proof follows Delange’s method. We also show that in the case of imaginary quadratic τ and w ≥ 2, the digit set of minimal norm representatives leads to w-NAFs for all elements of Z[τ ]. Additionally some properties of fundamental domain are stated.
An independent evaluation of subspace facial recognition algorithms

This paper explores a comparative study of both the linear and kernel implementations of three of the most popular Appearance-based Face Recognition projection classes, these being the methodologies of Principal Component Analysis, Linear Discriminant Analysis and Independent Component Analysis. The experimental procedure provides a platform of equal working conditions and examines the ten algorithms in the categories of expression, illumination, occlusion and temporal delay. The results are then evaluated based on a sequential combination of assessment tools that facilitate both intuitive and statistical decisiveness among the intra and interclass comparisons. The best categorical algorithms are then incorporated into a hybrid methodology, where the advantageous effects of fusion strategies are considered.
Computing explicit isomorphisms with full matrix algebras over $\mathbb{F}_q(x)$

We propose a polynomial time $f$-algorithm (a deterministic algorithm which uses an oracle for factoring univariate polynomials over $\mathbb{F}_q$) for computing an isomorphism (if there is any) of a finite dimensional $\mathbb{F}_q(x)$-algebra $A$ given by structure constants with the algebra of $n$ by $n$ matrices with entries from $\mathbb{F}_q(x)$. The method is based on computing a finite $\mathbb{F}_q$-subalgebra of $A$ which is the intersection of a maximal $\mathbb{F}_q[x]$-order and a maximal $R$-order, where $R$ is the subring of $\mathbb{F}_q(x)$ consisting of fractions of polynomials with denominator having degree not less than that of the numerator.
Base-station assisted device-to-device communications for high-throughput wireless video networks

We propose a new scheme for increasing the throughput of video files in cellular communications systems. This scheme exploits (i) the redundancy of user requests as well as (ii) the considerable storage capacity of smartphones and tablets. Users cache popular video files and - after receiving requests from other users - serve these requests via device-to-device localized transmissions. We investigate what is the optimal collaboration distance, trading off frequency reuse with the probability of finding a requested file within the collaboration distance. We show that an improvement of spectral efficiency of one to two orders of magnitude is possible, even if there is not very high redundancy in video requests.
Bilinear Attention Linear + Softmax Class Probabilities General Purpose Transformer Representation Target Entity Representation Post Conditioning Process Tokens Positional embedding Contextual vectors + Linear + Softmax Class Probabilities

Tracking entities in procedural language requires understanding the transformations arising from actions on entities as well as those entities’ interactions. While self-attention-based pre-trained language encoders like GPT and BERT have been successfully applied across a range of natural language understanding tasks, their ability to handle the nuances of procedural texts is still untested. In this paper, we explore the use of pre-trained transformer networks for entity tracking tasks in procedural text. First, we test standard lightweight approaches for prediction with pre-trained transformers, and find that these approaches underperform even simple baselines. We show that much stronger results can be attained by restructuring the input to guide the transformer model to focus on a particular entity. Second, we assess the degree to which transformer networks capture the process dynamics, investigating such factors as merged entities and oblique entity references. On two different tasks, ingredient detection in recipes and QA over scientific processes, we achieve state-ofthe-art results, but our models still largely attend to shallow context clues and do not form complex representations of intermediate entity or process state.1
Rate Region of the Vector Gaussian One-Helper Source-Coding Problem

We determine the rate region of the vector Gaussian one-helper source-coding problem under a covariance matrix distortion constraint. The rate region is achieved by a simple scheme that separates the lossy vector quantization from the lossless spatial compression. The converse is established by extending and combining three analysis techniques that have been employed in the past to obtain partial results for the problem.
Clusters, Currents and Whitehead's Algorithm

Using geodesic currents, we provide a theoretical justification for some of the experimental results, obtained by Haralick, Miasnikov and Myasnikov via pattern recognition methods, regarding the behavior of Whitehead’s algorithm on non-minimal inputs. In particular we prove that the images of “random” elements of a free group F under the automorphisms of F form “clusters” that share similar normalized Whitehead graphs and similar behavior with respect to Whitehead’s algorithm.
An ISIS screening approach involving threshold/partition for variable selection in linear regression

In linear regression, one can select a predictor if the absolute sample correlation between the predictor and the response variable is large. This screening approach is called SIS (sure independence screening) in Fan and Lv (2008). We propose a threshold for SIS and show that using SIS with this threshold can select all important predictors with with probability tending to one as $n \rightarrow \infty$ when $p=O(n^{\delta})$ for some $\delta \in (0, 2)$. For $p$ is of moderate size, we propose an iterative SIS procedure for predictor selection using the proposed threshold. For very large $p$, we propose to include a partitioning step to divide predictors into smaller groups to perform screening. Simulation results show that the first procedure works well for moderate $p$ and the second procedure works well for very large $p$.