Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving efficacy of concurrent internal binary search trees using local recovery

Binary Search Tree (BST) is an important data structure for managing ordered data. Many algorithms have been proposed for concurrent manipulation of a binary search tree in an asynchronous shared memory system that supports search, insert and delete operations based on both external and internal representations of a search tree. An important step in executing an operation on a tree is to traverse the tree from top-to-down in order to locate the operation's window. A process may need to perform this traversal several times to handle failures occurring due to other processes performing concurrent updates on the tree. Most concurrent algorithms that have been proposed so far use a naïve approach and simply restart the traversal from the root of the tree. In this work, we present a new approach to recover from such failures more efficiently for certain types of concurrent binary search tree algorithms using local recovery by restarting the traversal from the "middle" of the tree in order to locate an operation's window (tree section of interest). Our approach is sufficiently general in the sense that it can be applied to most of the existing concurrent binary search trees using internal representation based on both blocking as well as non-blocking approaches. Using experimental evaluation, we demonstrate that our local recovery approach can significantly improve the performance of a concurrent internal BST by up to 124.7% when the contention is high.
Patterns for Discussing and Modelling Variability in Business Processes

Expressing variability in graphical process models can lead to large and complicated models even for expressing rather simple situations. However, expressing variability in process models is important in many processes. During the execution of a process, it is not uncommon that knowledge workers can decide for additional steps, change the execution order or skip a task. In this paper, we propose a set of business process variability patterns to express those situations. When communicating with business experts, the patterns can be used as building blocks which serve as placeholder for syntactically well-defined (but more complicated) model fragments in a formal language such as BPMN or CMMN. Those building bloc ks can be used for communication and later be transformed into a formal modelling language. We believe that the patterns can be useful for supporting the communication between process analysts and stakeholders who are not familiar with formal process modeling languages. If necessary, the informal models that are created in a lightweight modeling language can later be transformed into more formal models.
Region Proposal by Guided Anchoring

Region anchors are the cornerstone of modern object detection techniques. State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios. In this paper, we revisit this foundational stage. Our study shows that it can be done much more effectively and efficiently. Specifically, we present an alternative scheme, named Guided Anchoring, which leverages semantic features to guide the anchoring. The proposed method jointly predicts the locations where the center of objects of interest are likely to exist as well as the scales and aspect ratios at different locations. On top of predicted anchor shapes, we mitigate the feature inconsistency with a feature adaption module. We also study the use of high-quality proposals to improve detection performance. The anchoring scheme can be seamlessly integrated into proposal methods and detectors. With Guided Anchoring, we achieve 9.1% higher recall on MS COCO with 90% fewer anchors than the RPN baseline. We also adopt Guided Anchoring in Fast R-CNN, Faster R-CNN and RetinaNet, respectively improving the detection mAP by 2.2%, 2.7% and 1.2%. Code is available at https://github.com/open-mmlab/mmdetection.
An Improved Cooperative Repair Scheme for Reed-Solomon Codes

Dau et al. recently extend Guruswami and Wootters’ scheme (STOC’2016) to cooperatively repair two or three erasures in Reed-Solomon (RS) codes. However, their schemes restrict to either the case that the characteristic of F divides the extension degree [F : B] or some special failure patterns, where F is the base field of the RS code and B is the subfield of the repair symbols. In this paper, we derive improved cooperative repair schemes for RS codes that remove all these restrictions. Specifically, we obtain a one-round cooperative repair scheme for any two erasures and a three-round scheme for any three erasures. When restricted to a wide class of failure patterns, we also achieve a one-round cooperative repair scheme for three erasures. The repair bandwidth of our schemes remains the same with that of Dau et al.’s schemes.
A Unified Framework of State Evolution for Message-Passing Algorithms

This paper presents a unified framework to understand the dynamics of message-passing algorithms in compressed sensing. State evolution is rigorously analyzed for a general error model that contains the error model of approximate message-passing (AMP), as well as that of orthogonal AMP. As a byproduct, AMP is proved to converge asymptotically if the sensing matrix is orthogonally invariant and if the moment sequence of its asymptotic singular-value distribution coincide with that of the Marčhenko-Pastur distribution up to the order that is at most twice as large as the maximum number of iterations.
An Optimal Linear Error Correcting Delivery Scheme for Coded Caching with Shared Caches

Classical coded caching setting avails each user to have one dedicated cache. This is generalized to a more general shared cache scheme and the exact expression for the worst case rate was derived in [E. Parrinello, A. Unsal, P. Elia, " Fundamental Limits of Caching in Heterogeneous Networks with Uncoded Prefetching," available on arXiv:1811.06247 [cs.IT], Nov. 2018]. For this case, an optimal linear error correcting delivery scheme is proposed and an expression for the peak rate is established for the same. Furthermore, a new delivery scheme is proposed, which gives an improved rate for the case when the demands are not distinct.
A Reduced-Complexity Projection Algorithm for ADMM-Based LP Decoding

The alternating direction method of multipliers has recently been adapted for linear programming decoding of low-density parity-check codes. The computation of the projection onto the parity polytope is the core of this algorithm and usually involves a sorting operation, which is the main effort of the projection. In this paper, we present an algorithm with low complexity to compute this projection. The algorithm relies on new findings in the recursive structure of the parity polytope and iteratively fixes selected components. As shown in our realistic simulation setup, it requires up to 37% less arithmetical operations compared with state-of-the-art projections. Additionally, it does not involve a sorting operation, which is needed in all exact state-of-the-art projection algorithms. These two benefits make it appealing for efficient hardware and software implementations.
Report of the Third Global Experimentation for Future Internet (GEFI 2018) Workshop

The third Global Experimentation for Future Internet (GEFI 2018) workshop was held October 25-26, 2018 in Tokyo, Japan, hosted by the University of Tokyo. A total of forty-four participants attended, representing Belgium, Brazil, China, Denmark, France, Ireland, Japan, the Republic of Korea, and the United States. The workshop employed a mixed format of presentations and open group discussions to advance multi-national coordination and interoperation of research infrastructure for advanced networking and computer science research. Major topic areas included: softwareization and virtualization of radios and networks; testbed support for networking experiments; EdgeNet; a federated testbed of elastic optical networks; and reproducibility in experimentation. Workshop goals included both the formulation of specific new research collaborations and strategies for coordination and interoperation of research testbeds. Workshop outcomes include a variety of new and ongoing collaborative efforts, ranging from an agreement to pursue the development of optical "white boxes" in support of elastic optical testbeds to the identification of strategies for effective use of open-source software and hardware platforms in future research infrastructure.
Skeletonisation Algorithms for Unorganised Point Clouds with Theoretical Guarantees

Real datasets often come in the form of an unorganised cloud of points. An overall shape of such data is hard to visualise when data points are given by coordinates in a high-dimensional Euclidean space. More general data can be represented by an abstract graph with weights or distances on links between data points. The skeletonisation problem for a point cloud is to find a graph or a skeleton that correctly represents the shape of a cloud. This paper compares three algorithms that solve the data skeletonisation problem for a general cloud with topological and geometric guarantees. First, the Mapper algorithm outputs a network of interlinked clusters. Second, the $\alpha$-Reeb algorithm discretises the classical Reeb graph and can be applied to discrete clouds at different scales $\alpha$. The third algorithm HoPeS produces a Homologically Persistent Skeleton without any extra parameters. HoPeS represents the 1-dimensional shape of a cloud at any scale by the Optimality Theorem. The Reconstruction Theorem gives conditions on a noisy sample of a graph when HoPeS provides a reconstructed graph with a correct homotopy type and within a small offset of the sample. The experiments on synthetic and real data show that HoPeS outperforms the other algorithms on several topological and geometric measures.
Capacity of Distributed Storage Systems with Clusters and Separate Nodes

In distributed storage systems (DSSs), the optimal tradeoff between node storage and repair bandwidth is an important issue for designing distributed coding strategies to ensure large scale data reliability. The capacity of DSSs is obtained as a function of node storage and repair bandwidth parameters, characterizing the tradeoff. There are lots of works on DSSs with clusters (racks) where the repair bandwidths from intra-cluster and cross-cluster are differentiated. However, separate nodes are also prevalent in the realistic DSSs, but the works on DSSs with clusters and separate nodes (CSN-DSSs) are insufficient. In this paper, we formulate the capacity of CSN-DSSs with one separate node for the first time where the bandwidth to repair a separate node is of cross-cluster. Consequently, the optimal tradeoff between node storage and repair bandwidth are derived and compared with cluster DSSs. A regenerating code instance is constructed based on the tradeoff. Furthermore, the influence of adding a separate node is analyzed and formulated theoretically. We prove that when each cluster contains R nodes and any k nodes suffice to recover the original file (MDS property), adding an extra separate node will keep the capacity if R|k, and reduce the capacity otherwise.
System-of-Systems Modeling, Analysis and Optimization of Hybrid Vehicular Traffic

While the development of fully autonomous vehicles is one of the major research fields in the Intelligent Transportation Systems (ITSs) domain, the upcoming long-term transition period – the hybrid vehicular traffic – is often neglected. However, within the next decades, automotive systems with heterogeneous autonomy levels will share the same road network, resulting in new problems for traffic management systems and communication network infrastructure providers. In this paper, we identify key challenges of the upcoming hybrid traffic scenario and present a system-of-systems model, which brings together approaches and methods from traffic modeling, data science, and communication engineering in order to allow data-driven traffic flow optimization. The proposed model consists of data acquisition, data transfer, data analysis, and data exploitation and exploits real world sensor data as well as simulative optimization methods. Based on the results of multiple case studies, which focus on individual challenges (e.g., resource-efficient data transfer and dynamic routing of vehicles), we point out approaches for using the existing infrastructure with a higher grade of efficiency.
Dynamic EEE Coalescing: Techniques and Bounds

Frame coalescing is one of the most efficient techniques to manage the low power idle (LPI) mode supported by energy efficient Ethernet (EEE) interfaces. This technique enables EEE interfaces to remain in the LPI mode for a certain amount of time upon the arrival of the first frame (time-based coalescing) or until a predefined amount of traffic accumulates in the transmission buffer (size-based coalescing). This paper provides new insights on the practical efficiency limits of both coalescing techniques. In particular, we derive the fundamental limits on the maximum energy savings considering a target average frame delay. Additionally, we present new open-loop adaptive variants of both time-based and size-based coalescing techniques. These proposals dynamically adjust the length of the sleeping periods in accordance with actual traffic conditions to reduce energy consumption while keeping the average delay near a predefined value simultaneously. Analytical and simulation results show that the energy consumption of both proposals is comparable to the fundamental limits. Consequently, we recommend the usage of the time-based algorithm in most scenarios because of its simplicity as well as its ability to bound the maximum frame delay at the same time.
A note on powers of Boolean spaces with internal semigroups

Boolean spaces with internal semigroups generalize profinite semigroups and are pertinent for the recognition of not-necessarily regular languages. Via recognition, the study of existential quantification in logic on words amounts to the study of certain spans of Boolean spaces with internal semigroups. In turn, these can be understood as the superposition of a span of Boolean spaces and a span of semigroups. In this note, we first study these separately. More precisely, we identify the conditions under which each of these spans gives rise to a morphism into the respective power or Vietoris construction of the corresponding structure. Combining these characterizations, we obtain such a characterization for spans of Boolean spaces with internal semigroups which we use to describe the topo-algebraic counterpart of monadic second-order existential quantification. This is closely related to a part of the earlier work on existential quantification in first-order logic on words by Gehrke, Petri\c san and Reggio. The observation that certain morphisms lift contravariantly to the appropriate power structures makes our analysis very simple.
Quantum-inspired classical sublinear-time algorithm for solving low-rank semidefinite programming via sampling approaches

Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. Recently, quantum algorithms with superpolynomial speedups for solving SDPs have been proposed assuming access to its constraint matrices in quantum superposition. Mutually inspired by both classical and quantum SDP solvers, in this paper we present a sublinear classical algorithm for solving low-rank SDPs which is asymptotically as good as existing quantum algorithms. Specifically, given an SDP with $m$ constraint matrices, each of dimension $n$ and rank $\mathrm{poly}(\log n)$, our algorithm gives a succinct description and any entry of the solution matrix in time $O(m\cdot\mathrm{poly}(\log n,1/\varepsilon))$ given access to a sample-based low-overhead data structure of the constraint matrices, where $\varepsilon$ is the precision of the solution. In addition, we apply our algorithm to a quantum state learning task as an application. Technically, our approach aligns with both the SDP solvers based on the matrix multiplicative weight (MMW) framework and the recent studies of quantum-inspired machine learning algorithms. The cost of solving SDPs by MMW mainly comes from the exponentiation of Hermitian matrices, and we propose two new technical ingredients (compared to previous sample-based algorithms) for this task that may be of independent interest: $\bullet$ Weighted sampling: assuming sampling access to each individual constraint matrix $A_{1},\ldots,A_{\tau}$, we propose a procedure that gives a good approximation of $A=A_{1}+\cdots+A_{\tau}$. $\bullet$ Symmetric approximation: we propose a sampling procedure that gives low-rank spectral decomposition of a Hermitian matrix $A$. This improves upon previous sampling procedures that only give low-rank singular value decompositions, losing the signs of eigenvalues.
Automated Software Protection for the Masses Against Side-Channel Attacks

We present an approach and a tool to answer the need for effective, generic, and easily applicable protections against side-channel attacks. The protection mechanism is based on code polymorphism, so that the observable behaviour of the protected component is variable and unpredictable to the attacker. Our approach combines lightweight specialized runtime code generation with the optimization capabilities of static compilation. It is extensively configurable. Experimental results show that programs secured by our approach present strong security levels and meet the performance requirements of constrained systems.
Sensation Seeking, Deviant Peer Affiliation, and Internet Gaming Addiction Among Chinese Adolescents: The Moderating Effect of Parental Knowledge

Although there is abundant evidence that an association between sensation seeking and adolescent Internet gaming addiction (IGA) exists, research has provided little insight into why adolescents with high sensation seeking are more likely to be focused on Internet and video games. Grounded in the social development model and ecological systems theory, this study investigated whether deviant peer affiliation mediated the relationship between sensation seeking and adolescent IGA, and whether this indirect link was moderated by parental knowledge. Participants were 1293 Chinese adolescents (49.65% male, Mage = 12.89 ± 0.52 years) who completed questionnaires assessing sensation seeking, deviant peer affiliation, parental knowledge, and IGA. Structural equation models revealed that the positive association between sensation seeking and adolescent IGA was partially mediated by deviant peer affiliation. In addition, this indirect link was significantly moderated by parental knowledge. Specifically, the indirect path from sensation seeking to adolescent IGA was stronger for adolescents with low parental knowledge than for those with high parental knowledge. Identifying the role of peers and parents in the onset of adolescent IGA has key implications for prevention and intervention.
Distinct Stress Response and Altered Striatal Transcriptome in Alpha-Synuclein Overexpressing Mice

Parkinson’s disease (PD) is a progressive neurodegenerative disorder with motor symptoms and a plethora of non-motor and neuropsychiatric features that accompany the disease from prodromal to advanced stages. While several genetic defects have been identified in familial forms of PD, the predominance of cases are sporadic and result from a complex interplay of genetic and non-genetic factors. Clinical evidence, moreover, indicates a role of environmental stress in PD, supported by analogies between stress-induced pathological consequences and neuronal deterioration observed in PD. From this perspective, we set out to investigate the effects of chronic stress exposure in the context of PD by using a genetic mouse model that overexpresses human wildtype SNCA. Mimicking chronic stress was achieved by adapting a chronic unpredictable mild stress protocol (CUMS) comprising eight different stressors that were applied randomly over a period of eight weeks starting at an age of four months. A distinctive stress response with an impact on anxiety-related behavior was observed upon SNCA overexpression and CUMS exposure. SNCA-overexpressing mice showed prolonged elevation of cortisol metabolites during CUMS exposure, altered anxiety-related traits, and declined motor skills surfacing with advanced age. To relate our phenotypic observations to molecular events, we profiled the striatal and hippocampal transcriptome and used a 2 × 2 factorial design opposing genotype and environment to determine differentially expressed genes. Disturbed striatal gene expression and minor hippocampal gene expression changes were observed in SNCA-overexpressing mice at six months of age. Irrespective of the CUMS-exposure, genes attributed to the terms neuroinflammation, Parkinson’s signaling, and plasticity of synapses were altered in the striatum of SNCA-overexpressing mice.
The Role of BDNF in Peripheral Nerve Regeneration: Activity-Dependent Treatments and Val66Met

Despite the ability of peripheral nerves to spontaneously regenerate after injury, recovery is generally very poor. The neurotrophins have emerged as an important modulator of axon regeneration, particularly brain derived neurotrophic factor (BDNF). BDNF regulation and signaling, as well as its role in activity-dependent treatments including electrical stimulation, exercise, and optogenetic stimulation are discussed here. The importance of a single nucleotide polymorphism in the BDNF gene, Val66Met, which is present in 30% of the human population and may hinder the efficacy of these treatments in enhancing regeneration after injury is considered. Preliminary data are presented on the effectiveness of one such activity-dependent treatment, electrical stimulation, in enhancing axon regeneration in mice expressing the met allele of the Val66Met polymorphism.