Miracle-dz/UniFAR-FTUs huggingface dataset:
Contextual equivalence for higher-order π-calculus revisited

The higher-order π-calculus is an extension of the π-calculus to allow communication of abstractions of processes rather than names alone. It has been studied intensively by Sangiorgi in his thesis where a characterisation of a contextual equivalence for higher-order π-calculus is provided using labelled transition systems and normal bisimulations. Unfortunately the proof technique used there requires a restriction of the language to only allow nite types. We revisit this calculus and o er an alternative presentation of the labelled transition system and a novel proof technique which allows us to provide a fully abstract characterisation of contextual equivalence using labelled transitions and bisimulations for higher-order π-calculus with recursive types also.
A Comprehensive Test Pattern Generation Approach Exploiting the SAT Attack for Logic Locking

The need for reducing manufacturing defect escape in today's safety-critical applications requires increased fault coverage. However, generating a test set using commercial automatic test pattern generation (ATPG) tools that lead to zero-defect escape is still an open problem. It is challenging to detect all stuck-at faults to reach 100% fault coverage. In parallel, the hardware security community has been actively involved in developing solutions for logic locking to prevent IP piracy. In logic locking, locks are inserted in different locations of the netlist to modify the original functionality. Unless the correct key is programmed into the IC, the circuit functions incorrectly. Unfortunately, the Boolean satisfiability (SAT) based attack, introduced in (Subramanyan et al. 2015), can determine the secret key efficiently, and break different logic locking schemes. In this article, we propose a novel test pattern generation approach using the powerful SAT attack on logic locking. A stuck-at fault is modeled as a locked gate with a secret key, where it can effectively deduce the satisfiable assignment with reduced backtracks under key initialization of the SAT attack. The input pattern that determines the key is a test for the stuck-at fault. We propose two different approaches for test pattern generation. First, a single stuck-at fault is targeted, and a corresponding locked circuit with one key bit is created. This approach generates one test pattern per fault. Second, we consider a group of faults and convert the circuit to its locked version with multiple key bits. The inputs obtained from the SAT attack tool are the test set for detecting this group of faults. Our approach can find test patterns for all hard-to-detect faults that were previously undetected in commercial ATPG tools. The proposed test pattern generation approach can efficiently detect redundant faults as well. We demonstrate the effectiveness of the approach on ITC’99 benchmarks. The results show that we can detect all the hard-to-detect faults and identify redundant faults and a 100% stuck fault coverage is achieved. In addition, we show that test generation time saving becomes significant for Approach 2 as multiple faults help reduce or remove conflicts.
EXPONENTIALLY WEIGHTED MOVING AVERAGE CONTROL SCHEMES FOR ASSESSING HOSPITAL ORGANIZATIONAL PERFORMANCE

Healthcare organizations are continuously involved in quality improvement programs and performance indicators are often suggested as key elements to capture information on clinical, organizational and financial processes. The challenge is thus to select useful statistical tools for accurately monitoring and providing benchmarks for such indicators. Statistical process control (SPC) provides a method both for assessing undisturbed, or steady-state, process behaviour and for detecting positive or negative changes in performance. The relevance of SPC in the healthcare context is confirmed in reviews by Benneyan et al. (2003) ,Thor et al. (2007), Noyez (2007) and Woodall et al. (2012); in books by Carey (2003), Winkel and Zhang (2007), Faltin et al. (2012), Morton et al. (2013) and Melo and Beck (2014); and in methodological notes by Mohammed et al. (2008), Mohammed and Worthington (2013) and Mohammed et al. (2013). Among the statistical process control tools, the exponentially weighted moving average (EWMA) control charts are widely used for industrial quality control, since they are especially suited for detecting small shifts in a process. EWMA control schemes have been found effective also for measuring and monitoring healthcare
Relevance of Innovations in Machine Learning to Scientometrics

Machine learning envisages building models that either classify, predict, cluster or determine the relative relevance of features to a problem and the associations between them. This paper briefy describes how these tasks are relevant to Scientometrics. Through this brief survey of selected tasks, it is observed that most solution approaches in Scientometric literature are built on the strong foundation of understanding and debating in uencing factors and the process of feature engineering, requiring the descriptors to be intuitive and methods used for classication, prediction, etc., to be amenable to interpretation. Recent trends in machine learning, particularly, deep learning methods, however, pose an interesting question: can we build models that automatically determine what features are important and thereby bypass the step of feature engineering? This paper discusses how such techniques could also be harnessed in Scientometrics.
Database Repairs and Analytic Tableaux

In this article, we characterize in terms of analytic tableaux the repairs of inconsistent relational databases, that is databases that do not satisfy a given set of integrity constraints. For this purpose we provide closing and opening criteria for branches in tableaux that are built for database instances and their integrity constraints. We use the tableaux based characterization as a basis for consistent query answering, that is for retrieving from the database answers to queries that are consistent wrt the integrity constraints.
C G ] 2 9 Fe b 20 08 A Simple Yao-Yao-Based Spanner of Bounded Degree ∗

We show that a simple modification to the Yao-Yao structure for Unit Disk Graphs yields a spanner Y Yk of maximum degree 2k and stretch factor t ≥ 1/(cos 4π/k − 2 sin 4π/k), for any k ≥ 28. Our algorithm has the same time and communication complexity as the original Yao-Yao algorithm. We also show that the Yao graph and its successor Yao-Yao have arbitrarily large weight.
Federated Identity Management for Research Collaborations

Identifying technology-agnostic requirements from an ever-evolving Federated Identity Management (FIM) landscape, which has shifted from its original conception of an arrangement that allows subscribers from multiple organisations to use the same identi! cation data to obtain access to the secured resources of all organisations in the group, to an ecosystem which now comprises an increasing variety of technologies provided by participating organisations.
Compositional Learning of Relation Path Embedding for Knowledge Base Completion

Nowadays, large-scale knowledge bases containing billions of facts have reached impressive sizes; however, they are still far from completion. In addition, most existing methods only consider the direct links between entities, ignoring the vital impact about the semantic of relation paths. In this paper, we study the problem of how to better embed entities and relations into different low dimensional spaces. A compositional learning model of relation paths embedding (RPE) is proposed to take full advantage of additional semantics expressed by relation paths. More specifically, using corresponding projection matrices, RPE can simultaneously embed entities into corresponding relation and path spaces. It is also suggested that type constraints could be extended from traditional relation-specific to the new proposed path-specific ones. Both of the two type constraints can be seamlessly incorporated into RPE and decrease the errors in prediction. Experiments are conducted on the benchmark datasets and the proposed model achieves significant and consistent improvements compared with the state-of-the-art algorithms for knowledge base completion.
Joint User Association and Power Control for Load Balancing in Downlink Heterogeneous Cellular Networks

Instead of achievable rate in the conventional association, we utilize the long-term rate to design two types of association strategies for load balancing in heterogeneous cellular networks (HCNs). In the first type, we try to balance the network loads by maximizing the weighted sum of long-term rates. Based on this type, we introduce a power control (PC) to mitigate the network interference and reduce the energy consumption in the second type. Since the long-term rate of any user associated with some base stations (BSs) mainly depends on the achievable rate received by this user from the selected BS and the load of this BS, it can be used as a key factor of association design for load balancing in HCNs. To solve the first association problem, we design a one-layer iterative algorithm by converting the original sum-of-ratio form into a parameterized polynomial one. By combining it with a PC algorithm, we give a two-layer iterative solution for the second problem. Specifically, the outer layer performs a user association using the algorithm mentioned in the first type, and the inner layer updates the transmit power of each BS using a power update function. At last, we give some convergence and complexity analyses for these proposed algorithms. As shown in numerical results, the proposed strategies have a superior performance than the conventional association, and the second association strategy achieves a higher load balancing gain and energy efficiency than conventional scheme and other offloading strategies.
Recursive robust PCA or recursive sparse recovery in large but structured noise

We study the recursive robust principal components' analysis (PCA) problem. Here, “robust” refers to robustness to both independent and correlated sparse outliers. If the outlier is the signal-of-interest, this problem can be interpreted as one of recursively recovering a time sequence of sparse vectors, S<sub>t</sub>, in the presence of large but structured noise, L<sub>t</sub>: the noise needs to lie in a “slowly changing” low dimensional subspace. We study a novel solution called Recursive Projected CS (ReProCS). Under mild assumptions, we show that, with high probability (w.h.p.), at all times, ReProCS can exactly recover the support set of S<sub>t</sub>; and the reconstruction errors of both S<sub>t</sub> and L<sub>t</sub> are upper bounded by a time-invariant and small value.
AN ODYSSEY INTO LOCAL REFINEMENT AND MULTILEVEL PRECONDITIONING I : OPTIMALITY OF THE BPX

In this article, we examine the Bramble-Pasciak-Xu (BPX) preconditioner in the setting of local 3D mesh refinement. While the available optimality results for the BPX preconditioner have been constructed primarily in the setting of uniformly refined meshes, a notable exception is the 2D result due to Dahmen and Kunoth, which established BPX optimality on meshes produced by a local 2D red-green refinement. The purpose of this article is to extend this original 2D optimality result to the local 3D red-green refinement procedure introduced by Bornemann-Erdmann-Kornhuber (BEK). The extension is reduced to establishing that locally enriched finite element subspaces allow for the construction of a scaled basis which is formally Riesz stable. This construction turns out to rest not only on shape regularity of the refined elements, but also critically on a number of geometrical properties we establish between neighboring simplices produced by the BEK refinement procedure. We also show that the number of degrees of freedom used for smoothing is bounded by a constant times the number of degrees of freedom introduced at that level of refinement, indicating that a practical implementable version of the resulting BPX preconditioner for the BEK refinement setting has provably optimal (linear) computational complexity per iteration, as well as having a uniformly bounded condition number. The theoretical framework supports arbitrary spatial dimension d ≥ 1, and we indicate clearly which geometrical properties established here must be re-established to show BPX optimality for spatial dimension d ≥ 4. The proof techniques require no coefficient smoothness assumptions beyond those required for well-posedness in H1.
Surveying the Landscape of Ethics-Focused Design Methods

Over the past decade, HCI researchers, design researchers, and practitioners have increasingly addressed ethics-focused issues through a range of theoretical, methodological and pragmatic contributions to the field. While many forms of design knowledge have been proposed and described, we focus explicitly on knowledge that has been codified as"methods,"which we define as any supports for everyday work practices of designers. In this paper, we identify, analyze, and map a collection of 63 existing ethics-focused methods intentionally designed for ethical impact. We present a content analysis, providing a descriptive record of how they operationalize ethics, their intended audience or context of use, their"core"or"script,"and the means by which these methods are formulated, articulated, and languaged. Building on these results, we provide an initial definition of ethics-focused methods, identifying potential opportunities for the development of future methods to support design practice and research.
Post 1 Get started on machine learning ! ! !

Social learning, i.e., students learning from each other through social interactions, has the potential to significantly scale up instruction in online education. In many cases, such as in massive open online courses (MOOCs), social learning is facilitated through discussion forums hosted by course providers. In this paper, we propose a probabilistic model for the process of learners posting on such forums, using point processes. Different from existing works, our method integrates topic modeling of the post text, timescale modeling of the decay in post activity over time, and learner topic interest modeling into a single model, and infers this information from user data. Our method also varies the excitation levels induced by posts according to the thread structure, to reflect typical notification settings in discussion forums. We experimentally validate the proposed model on three real-world MOOC datasets, with the largest one containing up to 6,000 learners making 40,000 posts in 5,000 threads. Results show that our model excels at thread recommendation, achieving significant improvement over a number of baselines, thus showing promise of being able to direct learners to threads that they are interested in more efficiently. Moreover, we demonstrate analytics that our model parameters can provide, such as the timescales of different topic categories in a course.
Joint Representations for the task of Product Recommendation omas

We propose a unied product embedded representation that is optimized for the task of retrieval-based product recommendation. To this end, we introduce a new way to fuse modality-specic product embeddings into a joint product embedding, in order to leverage both product content information, such as textual descriptions and images, and product collaborative ltering signal. By introducing the fusion step at the very end of our architecture, we are able to train each modality separately, allowing us to keep a modular architecture that is preferable in real-world recommendation deployments. We analyze our performance on normal and hard recommendation setups such as cold-start and cross-category recommendations and achieve good performance on a large product shopping dataset.
Feedback Coding Schemes for the Broadcast Channel With Mutual Secrecy Requirement at the Receivers

The broadcast channel with mutual secrecy requirement at the receivers (BC-MSR-R) is a basic model characterizing the physical layer security (PLS) in the down-link of the wireless communication systems, where one transmitter sends two independent messages to two receivers via a broadcast channel, and each receiver can successfully decode his/her intended message and wishes to overhear the other one’s message. This paper studies how to enhance the already existing secrecy rate region of the BC-MSR-R via receivers’ feedback. Specifically, we propose two feedback strategies for the BC-MSR-R, where one uses the feedback to generate pure secret keys protecting the transmitted messages, and the other uses the feedback to generate not only keys but also cooperative messages helping the receivers to improve their decoding performance. Different inner bounds on the secrecy capacity region of the BC-MSR-R with noiseless feedback are constructed according to different feedback strategies, and these bounds are further illustrated by a Dueck-type example.
Censored Exploration and the Dark Pool Problem

We introduce and analyze a natural algorithm for multi-venue exploration from censored data, which is motivated by the Dark Pool Problem of modern quantitative finance. We prove that our algorithm converges in polynomial time to a near-optimal allocation policy; prior results for similar problems in stochastic inventory control guaranteed only asymptotic convergence and examined variants in which each venue could be treated independently. Our analysis bears a strong resemblance to that of efficient exploration/ exploitation schemes in the reinforcement learning literature. We describe an extensive experimental evaluation of our algorithm on the Dark Pool Problem using real trading data.
Oo-line Parsability and the Well-foundedness of Subsumption

Typed feature structures are used extensively for the speciication of linguistic information in many formalisms. The subsumption relation orders TFSs by their information content. We prove that subsumption of acyclic TFSs is well-founded, whereas in the presence of cycles general TFS subsumption is not well-founded. We show an application of this result for parsing, where the well-foundedness of subsumption is used to guarantee termination for grammars that are oo-line parsable. We deene a new version of oo-line parsability that is less strict than the existing one; thus termination is guaranteed for parsing with a larger set of grammars. This paper has not been submitted elsewhere in identical or similar form
Positive definite matrices and the S-divergence

Positive definite matrices abound in a dazzling variety of applications. This ubiquity can be in part attributed to their rich geometric structure: positive definite matrices form a self-dual convex cone whose strict interior is a Riemannian manifold. The manifold view is endowed with a"natural"distance function while the conic view is not. Nevertheless, drawing motivation from the conic view, we introduce the S-Divergence as a"natural"distance-like function on the open cone of positive definite matrices. We motivate the S-divergence via a sequence of results that connect it to the Riemannian distance. In particular, we show that (a) this divergence is the square of a distance; and (b) that it has several geometric properties similar to those of the Riemannian distance, though without being computationally as demanding. The S-divergence is even more intriguing: although nonconvex, we can still compute matrix means and medians using it to global optimality. We complement our results with some numerical experiments illustrating our theorems and our optimization algorithm for computing matrix medians.
Extension of the Blahut–Arimoto Algorithm for Maximizing Directed Information

In this paper, we extend the Blahut-Arimoto algorithm for maximizing Massey's directed information. The algorithm can be used for estimating the capacity of channels with delayed feedback, where the feedback is a deterministic function of the output. In order to maximize the directed information, we apply the ideas from the regular Blahut-Arimoto algorithm, i.e., the alternating maximization procedure, to our new problem. We provide both upper and lower bound sequences that converge to the optimum global value. Our main insight in this paper is that in order to find the maximum of the directed information over a causal conditioning probability mass function, one can use a backward index time maximization combined with the alternating maximization procedure. We give a detailed description of the algorithm, showing its complexity and the memory needed, and present several numerical examples.
A General Framework for Graph Sparsification Ramesh Hariharan Strand Life Sciences

Given a weighted graph G and an error parameter ε > 0, the graph sparsification problem requires sampling edges in G and giving the sampled edges appropriate weights to obtain a sparse graph Gε with the following property: the weight of every cut in Gε is within a factor of (1± ε) of the weight of the corresponding cut in G. Benczúr and Karger [2] showed how to obtain Gε with O(n logn/ε2) edges in time O(m log3 n) for weighted graphs and O(m log2 n) for unweighted graphs using a combinatorial approach based on strong connectivity. Spielman et al [22] showed how to obtain Gε with O(n logn/ε2) edges in time O(m logc n) for some (large) constant c using an algebraic approach based on effective resistances. Our contributions are as below (all for weighted graphs G with n vertices and m edges having polynomial-sized weights, unless otherwise stated): • Benczúr and Karger [2] conjectured that using standard connectivity instead of strong connectivity for sampling would simplify the result substantially, and posed this as an open question. In this correspondence, we resolve this question by showing that sampling using standard connectivities also preserves cut weights and yields a Gε with O(n log2 n/ε2) edges. • We provide a very simple strictly linear time algorithm (i.e. O(m) time) for graph sparsification that yields a Gε with O(n log2 n/ε2) edges. • We provide another algorithm for graph sparsification that yields a Gε with O(n logn/ε2) edges in O(m log2 n) time (for unweighted graphs, this reduces to O(m logn) time). • Combining the above two results, we obtain the fastest known algorithm for obtaining a Gε with O(n logn/ε2) edges; this algorithm runs in time O(m + n log4 n/ε2) whereas the previous best bound is O(m log3 n). • If G has arbitrary edge weights, we give an O(m log2 n)-time algorithm that yields a Gε containing O(n log2 n/ε2) edges. The previous best bound is O(m log3 n) time for a Gε with O(n logn/ε2) edges. • Most importantly, we provide a generic framework that sets out sufficient conditions for any particular sampling scheme to result in good sparsifiers; all the above results can be obtained by simple instantiations of this framework, as can known results on sampling by strong connectivity and sampling by effective resistances1. Our algorithms are Monte-Carlo, i.e. work with high probability, as are all efficient algorithms for graph sparsification. A key ingredient of our proofs is a generalization of bounds on the number of small cuts in an undirected graph due to Karger [8]; this generalization might be of independent interest. 1with a Gε that is slightly denser than the best-known result for the effective resistance case.
On Post-Selection Inference in A/B Tests

When a large number of simultaneous statistical inferences are conducted, unbiased estimators become biased if we purposefully select a subset of results to draw conclusions based on certain selection criteria. This happens a lot in A/B tests when there are too many metrics and segments to choose from, and only statistically significant results are considered. This paper proposes two different approaches, one based on supervised learning techniques, and the other based on empirical Bayes. We claim these two views can be unified and conduct large scale simulation and empirical study to benchmark our proposals with different existing methods. Results show our methods make substantial improvement for both point estimation and confidence interval coverage.