The primary purpose of remote sensing interpretation is to acquire information on geological elements in the study area by collecting geological data and comparing remote sensing images [1]. This study employs Synthetic Aperture Radar Interferometry (InSAR) technology to monitor ground deformation around the Lao Cai area in Vietnam. Using 26 Sentinel-1 SAR images, we detected surface deformation occurring between October 2020 and August 2021, generating a regional InSAR deformation map. The results provide insights into post-mining surface deformation patterns, enable monitoring of mining-induced subsidence, assess geological environmental damage, and support early warning systems for geohazards.

In recent years, graph neural networks (GNNs) have emerged as a promising approach for classifying non-Euclidean structural data. However, the practical implementation of GNNs faces challenges related to their limited scalability due to the presence of multi-hop data dependencies. In order to tackle this issue, existing methods have employed teacher GNNs to generate labels, which are then used to train multilayer perceptrons (MLPs) based solely on node features, without considering any structural information. However, these methods primarily focus on the soft labels generated by the teacher GNNs, leading to suboptimal performance because they disregard significant features of the teacher GNNs. Additionally, since the structural information of the graph is omitted in the input of MLPs, it can be more susceptible to the influence of erroneous features. To address these limitations, this paper proposes a novel framework that incorporates a new distillation strategy to integrate soft feature similarity into MLPs, while also utilizing contrastive learning to enhance the training of student MLPs. Our model has accuracy, averaged over seven public datasets, 5.21% higher than state-of-the-art (SOTA) methods, and even 2.49% higher than teacher GNNs over five standard scaled datasets. At the same time, its inference time is only 1.17% of comparable GNNs.

Personalized exercise recommendation is an important task in educational data mining, aiming to recommend exercises that match students’ intentions and abilities. However, existing recommendation methods often ignore the dynamic changes and individual differences in students’ knowledge levels and face serious data sparsity problems. To address these limitations, we employ graph neural networks (GNNs) to learn node representations in exercise recommendation contexts and propose a new knowledge tracing-enhanced multitask exercise recommendation framework, called MLKT4Rec. Unlike previous graph-based approaches that focus on explicitly observed relationships in the data, we use implicit edges to augment the graph structure and incorporate exercise difficulty attributes, relative time intervals, and location coding to enrich the exercise representation. Based on this, we construct a knowledge tracing model to capture students’ knowledge levels and integrate it into the exercise sequential recommendation process for joint multitask training. Extensive experiments on four real datasets validate the effectiveness of the proposed model.

The advance in single-cell RNA-sequencing (scRNA-seq) sheds light on cell-specific transcriptomic studies of cell developments, complex diseases and cancers. Nevertheless, scRNA-seq techniques suffer from 'dropout' events, and imputation tools are proposed to address the sparsity. Here, rather than imputation, we propose a tool, SMURF, to extract the low-dimensional embeddings from cells and genes utilizing matrix factorization with a mixture of Poisson-Gamma divergent as objective while preserving self-consistency. SMURF exhibits feasible cell subpopulation discovery efficacy with obtained cell embeddings on replicated in silico and eight web lab scRNA datasets with ground truth cell types. Furthermore, SMURF can reduce the cell embedding to a 1D-oval space to recover the time course of cell cycle. SMURF can also serve as an imputation tool; the in silico data assessment shows that SMURF parades the most robust gene expression recovery power with low root mean square error and high Pearson correlation. Moreover, SMURF recovers the gene distribution for the WM989 Drop-seq data. SMURF is available at https://github.com/deepomicslab/SMURF.

While research has shown that the agile chip design methodology is promising to sustain the scaling of computing performance in a more efficient way, it is still of limited usage in actual applications due to two major obstacles: 1) Lack of tool-chain and developing framework supporting agile chip design, especially for large-scale modern processors. 2) The conventional verification methods are less agile and become a major bottleneck of the entire process. To tackle both issues, we propose MINJIE, an open-source platform supporting agile processor development flow. MINJIE integrates a broad set of tools for logic design, functional verification, performance modelling, pre-silicon validation and debugging for better development efficiency of state-of-the-art processor designs. We demonstrate the usage and effectiveness of MINJIE by building two generations of an open-source superscalar out-of-order RISC-V processor code-named XIANGSHAN using agile methodologies. We quantify the performance of XIANGSHAN using SPEC CPU2006 benchmarks and demonstrate that XIANGSHAN achieves industry-competitive performance.

Span extraction, aiming to extract text spans (such as words or phrases) from plain texts, is a fundamental process in Information Extraction.Recent works introduce the label knowledge to enhance the text representation by formalizing the span extraction task into a question answering problem (QA Formalization), which achieves state-of-the-art performance.However, QA Formalization does not fully exploit the label knowledge and suffers from low efficiency in training/inference.To address those problems, we introduce a new paradigm to integrate label knowledge and further propose a novel model to explicitly and efficiently integrate label knowledge into text representations.Specifically, it encodes texts and label annotations independently and then integrates label knowledge into text representation with an elaborate-designed semantics fusion module.We conduct extensive experiments on three typical span extraction tasks: flat NER, nested NER, and event detection.The empirical results show that 1) our method achieves state-of-the-art performance on four benchmarks, and 2) reduces training time and inference time by 76% and 77% on average, respectively, compared with the QA Formalization paradigm.Our code and data are available at https://github.com/ Akeepers/LEAR.

Even though earliest-deadline-first (EDF) is optimal in terms of uniprocessor schedulability, it is co-NP-hard to precisely verify uniprocessor schedulability for constrained-deadline task sets. The most efficient way to solve this problem in polynomial time is via a partially linear approximation of the demand bound function. Such approximation leads to a simple uniprocessor schedulability testing with speedup factor ρ. Such a result further leads to Deadline-Monotonic Partitioned-EDF on multi-processors with speedup factor of 1 + ρ − 1/m (where m is the number of processors). The current state of the art results indicate that ρ is within the range [1.5,14/9]. Especially, it has been a conjecture that ρ = 1.5.This paper improves the range of ρ to (1.5026,1.5380). The improved lower bound disproves the conjecture of lower bound 1.5. A novel technique is to construct an auxiliary function that is larger than the approximate demand bound function but keeps the supremum ρ unchanged. It solves the dilemma that beating the lower bound 1.5 requires extremely large task sets, while the large size makes it difficult to check the schedulability. This technique not only enables us to disprove 1.5 by a task set of only eight tasks, but also sheds light on future work in transferring/downsizing task sets and deriving utilization bound based tests for various workload abstraction models, such as DAG tasks.

With the frequent occurrence of unconventional global emergencies, the public security field has received more and more attention. As an unconventional emergency, terrorist attacks have aroused global attention. So, how should we extract useful information from a large number of terrorist attacks and find the law of the attack, so that we can effectively prevent or take early measures to reduce losses? To this end, we are based on the Global Terrorism Database (GTD), and aim to predict the next province or state a terrorist organization may attack at a specific time point by mining the terrorist organizations' historical records and other types of information availabl, such as incident information and so on. Then, Based on these incident information and spatiotemporal information, we propose a neural network called ATtention-based Fused-SpatialTemporal LSTM (ATFST-LSTM) to predict the next location which may be attacked. We test the efficiency of our models on GTD, experiments show that our models has achieved better results.

A crucial assumption in most statistical learning theory is that samples are independently and identically distributed (i.i.d.). However, for many real applications, the i.i.d. assumption does not hold. We consider learning problems in which examples are dependent and their dependency relation is characterized by a graph. To establish algorithm-dependent generalization theory for learning with non-i.i.d. data, we first prove novel McDiarmid-type concentration inequalities for Lipschitz functions of graph-dependent random variables. We show that concentration relies on the forest complexity of the graph, which characterizes the strength of the dependency. We demonstrate that for many types of dependent data, the forest complexity is small and thus implies good concentration. Based on our new inequalities we are able to build stability bounds for learning from graph-dependent data.

We consider the problem of online Min-cost Perfect Matching with Delays (MPMD) recently introduced by Emek et al, (STOC 2016). This problem is defined on an underlying $n$-point metric space. An adversary presents real-time requests online at points of the metric space, and the algorithm is required to match them, possibly after keeping them waiting for some time. The cost incurred is the sum of the distances between matched pairs of points (the connection cost), and the sum of the waiting times of the requests (the delay cost). We present an algorithm with a competitive ratio of $O(\log n)$, which improves the upper bound of $O(\log^2n+\logΔ)$ of Emek et al, by removing the dependence on $Δ$, the aspect ratio of the metric space (which can be unbounded as a function of $n$). The core of our algorithm is a deterministic algorithm for MPMD on metrics induced by edge-weighted trees of height $h$, whose cost is guaranteed to be at most $O(1)$ times the connection cost plus $O(h)$ times the delay cost of every feasible solution. The reduction from MPMD on arbitrary metrics to MPMD on trees is achieved using the result on embedding $n$-point metric spaces into distributions over weighted hierarchically separated trees of height $O(\log n)$, with distortion $O(\log n)$. We also prove a lower bound of $Ω(\sqrt{\log n})$ on the competitive ratio of any randomized algorithm. This is the first lower bound which increases with $n$, and is attained on the metric of $n$ equally spaced points on a line. The problem of Min-cost Bipartite Perfect Matching with Delays (MBPMD) is the same as MPMD except that every request is either positive or negative, and requests can be matched only if they have opposite polarity. We prove an upper bound of $O(\log n)$ and a lower bound of $Ω(\log^{1/3}n)$ on the competitive ratio of MBPMD with a more involved analysis.

Schedulability is a fundamental problem in real-time scheduling, but it has to be approximated due to the intrinsic computational hardness. As the most popular algorithm for deciding schedulability on multiprocess platforms, the speedup factor of partitioned-EDF is challenging to analyze and is far from being determined. Partitioned-EDF was first proposed in 2005 by Barush and Fisher [1], and was shown to have a speedup factor at most 3-1/m, meaning that if the input of sporadic tasks is feasible on m processors with speed one, partitioned-EDF will always succeed on m processors with speed 3-1/m. In 2011, this upper bound was improved to 2.6322-1/m by Chen and Chakraborty [2], and no more improvements have appeared ever since then. In this paper, we develop a novel method to discretize and regularize sporadic tasks, which enables us to improve, in the case of constrained deadlines, the speedup factor of partitioned-EDF to 2.5556-1/m, very close to the asymptotic lower bound 2.5 in [2].

A tight criterion under which the abstract version Lovász Local Lemma (abstract-LLL) holds was given by Shearer [41] decades ago. However, little is known about that of the variable version LLL (variable-LLL) where events are generated by independent random variables, though variable- LLL naturally models and is enough for almost all applications of LLL. We introduce a necessary and sufficient criterion for variable-LLL, in terms of the probabilities of the events and the event-variable graph specifying the dependency among the events. Based on this new criterion, we obtain boundaries for two families of event-variable graphs, namely, cyclic and treelike bigraphs. These are the first two non-trivial cases where the variable-LLL boundary is fully determined. As a byproduct, we also provide a universal constructive method to find a set of events whose union has the maximum probability, given the probability vector and the event-variable graph.Though it is #P-hard in general to determine variable- LLL boundaries, we can to some extent decide whether a gap exists between a variable-LLL boundary and the corresponding abstract-LLL boundary. In particular, we show that the gap existence can be decided without solving Shearer’s conditions or checking our variable-LLL criterion. Equipped with this powerful theorem, we show that there is no gap if the base graph of the event-variable graph is a tree, while gap appears if the base graph has an induced cycle of length at least 4. The problem is almost completely solved except when the base graph has only 3-cliques, in which case we also get partial solutions.A set of reduction rules are established that facilitate to infer gap existence of a event-variable graph from known ones. As an application, various event-variable graphs, in particular combinatorial ones, are shown to be gapful/gapless.

Borgs et al. [2016] investigated essential requirements for communities in preference networks. They defined six axioms on community functions, i.e., community detection rules. Though having elegant properties, the practicality of this axiomsystem is compromised by the intractability of checking twocritical axioms, so no nontrivial consistent community functionwas reported in [Borgs et al., 2016]. By adapting the two axioms in a natural way, we propose two new axioms that are efficiently-checkable. We show that most of the desirable properties of the original axiom system are preserved. More importantly, the new axioms provide a general approach to constructing consistent community functions. We further find a natural consistent community function that is also enumerable and samplable, answering an open problem in the literature.

In this paper, we study the block delivery delay of random linear network coding in two-hop single-unicast delay-tolerant networks with grid-based mobility. By block delivery delay, we mean how long it takes the destination to receive all the K information packets of a single block. Our work includes two parts. First, we give a general analysis of the dependency between packet spaces spanned by different nodes in a stochastic way. Then we simplify the result by means of the approximation. By the dependency analysis, we can accurately update nodes' innovativeness rank. Second, via tracking the innovativeness ranks of all nodes, we develop an analytic framework to iteratively compute the cumulative distribution function of the block delivery delay. Our simulation results verify that both parts of our analysis are sufficiently accurate. Copyright © 2013 John Wiley & Sons, Ltd.

Analyzing factors of affecting Web Service invocation performance is a hot topic. Among the factors, service interface complexity is a key one investigated by much research work. However, these researches mainly analyze the impact on the performance of primitives, some simple data structures like mesh interface object, or array of them. For the complex data structures, these works lack of a systematic approach to characterize the impact. This paper firstly makes a detailed statistics of service interface complexity based on a large sample space (10K+ WSDL files) on the World Wide Web, and we find about 41.6% services contain complex data structures. The statistic results guide us to conduct many experiments for finding out the correlation of service interface complexity to invocation performance. We discover an interesting feature on commonly used Web Service platforms (Axis/gSOAP/.Net), called Data Structural Form Unaware. As each parameter or return value can be represented as a tree with two kinds of nodes, structure type node and primitive type node, then the feature means that the overhead caused by parameters or return values is independent of the organization of nodes in the trees, but it is only related with the number and the type of nodes. By this feature, we present a simple model to quantify the impact of service interface complexity. Using our model, a Service Interface Performance Vector can be calculated by parsing a WSDL file to estimate the overhead of service interface design. This vector, together with the invocation probability of each operation, generates the Service Interface Performance Score, a comprehensive complexity indicator which can be used to evaluate and rank the service interfaces. At last, a rank report of services on the WWW is shown. Our work can play a guiding role in service interface designing, service interface performance predicting, and ranking.

It is a challenge to integrate and share resources securely across multiple autonomous administrative domains environment. We introduce the community-based model of vega enterprise information grid and its operation mechanism. The individuals and/or institutes forming different communities can not only implement diverse policies and autonomous management of resource sharing without any impact on the other communities, but also achieve a globally shared goal. Based on the model, the access control technique, including framework and uniform formalizing, is presented in detail. The static or dynamic role binding is used for entitling user's request for accessing resources within or across communities, which ensures a globally unified view for user. A prototype describes how these techniques are useful in building an enterprise information grid. The evaluation and future continuing works are presented in the conclusion.