Miracle-dz/UniFAR-FTUs huggingface dataset:
Public Computation & Boundary Play

In this paper, we introduce 'public computation' as a genre of learning environments that can be used to radically broaden public participation in authentic, computation-enabled STEM disciplinary practices. Our paradigmatic approach utilizes open source software designed for professional scientists, engineers and digital artists, and situates them in an undiluted form, alongside live and archived expert support, in a public space. We present a case study of DigiPlay, a prototypical public computation space we designed at the University of Calgary, where users can interact directly with scientific simulations as well as the underlying open source code using an array of massive multi- touch screens. We argue that in such a space, public interactions with the code can be thought of as boundary work and play, through which public participation becomes legitimate scientific act, as the public engages in scientific creation through truly open-ended explorations with the code.
Modeling Diffusion of Competing Products and Conventions in Social Media

The emergence and wide-spread use of social networks and microblogging sites has led to a dramatic increase on the availability of users’ activity data. Importantly, this data can be exploited to solve some of the problems that have captured the attention of economists and marketers for decades as, e.g., product adoption, product competition and product life cycle. In this paper, we leverage on users’ activity data from a popular microblogging site to model and predict the competing dynamics of products and social conventions adoptions. To this aim, we propose a data-driven model, based on continuous-time Hawkes processes, for the adoption and frequency of use of competing products and conventions. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our method over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only present a good predictive power but also provides interpretable model parameters, which allow us to gain insights into the fundamental principles that drive product and convention adoptions.
Reconstruction of correlated sources with energy harvesting constraints

In this paper, we investigate how to minimize the distortion in the reconstruction of correlated sources.We consider a communication scenario where a sensor node is capable of harvesting energy from the environment and where the Fusion Center (FC), in order to exploit correlation, uses past observations as side information for decoding. We provide a convex formulation of the problem and derive the optimal transmission policies (i.e., power and rate allocation). We also propose an iterative procedure based on the subgradient method by means of which a solution can be iteratively found. Interestingly, each iteration entails the interaction (coupling) of a directional water-filling and a reverse water-filling schemes. Numerical results are provided in order to illustrate the impact of correlation in the resulting transmission policies.
D S ] 1 7 N ov 2 00 4 Lower-Stretch Spanning Trees

In 1991, Alon, Karp, Peleg, and West proved that every weighted connected graph G contains as a subgraph a spanning tree into which the edges of G can be embedded with average stretch exp ( O( √ log n log log n) ) , and that there exists an n-vertex graph G such that all its spanning trees have average stretch Ω(log n). Closing the exponential gap between these upper and lower bounds is listed as one of the long-standing open questions in the area of low-distortion embeddings of metrics (Matousek 2002). We significantly reduce this gap by constructing a spanning tree in G of average stretch O((log n log log n)). Moreover, we show that this tree can be constructed in time O(m log n) in general, and in time O(m log n) if the input graph is unweighted. The main ingredient in our construction is a novel graph decomposition technique. Our new algorithm can be immediately used to improve the running time of the recent solver for diagonally dominant linear systems of Spielman and Teng from m2( √ log n log log n)) log(1/ǫ) to m log n log(1/ǫ), and to O(n(log n log log n) log(1/ǫ)) when the system is planar. Applying a recent reduction of Boman, Hendrickson and Vavasis, this provides an O(n(log n log log n) log(1/ǫ)) time algorithm for solving the linear systems that arise when applying the finite element method to solve twodimensional elliptic partial differential equations. Our result can also be used to improve several earlier approximation algorithms that use low-stretch spanning trees. Part of this work was done in Yale University, and was was supported by the DoD University Research Initiative (URI) administered by the Office of Naval Research under Grant N00014-01-1-0795. The work was also partially supported by the Lynn and William Frankel Center for Computer Sciences. Partially supported by NSF grant CCR-0324914. Part of this work was done at Yale University. Partially supported by NSF grants CCR-0311430 and ITR CCR-0325630.
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching

Designing the structure of neural networks is considered one of the most challenging tasks in deep learning. Recently, a few approaches have been proposed to automatically search for the optimal structure of neural networks, however, they suffer from either prohibitive computation cost (e.g., 256 Hours on 250 GPU in [1]) or unsatisfactory performance compared to those of hand-crafted neural networks. In this paper, we propose an Ecologically-Inspired GENetic approach for neural network structure search (EIGEN), that includes succession, mimicry and gene duplication. Specifically, we first use primary succession to rapidly evolve a community of poor initialized neural network structures into a more diverse community, followed by a secondary succession stage for fine-grained searching based on the networks from the primary succession. Extinction is applied in both stages to reduce computational cost. Mimicry is employed during the entire evolution process to help the inferior networks imitate the behavior of a superior network and gene duplication is utilized to duplicate the learned blocks of novel structures, both of which help to find the better network structures. Extensive experimental results show that our proposed approach can achieve the similar or better performance compared to the existing genetic approaches with dramatically reduced computation cost. For example, the network discovered by our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [1].
Agnostic System Identification for Model-Based Reinforcement Learning Supplementary Material

Additional Notation: We first introduce additional notation not used in the paper that is useful in some proofs. In particular, we define dω,π the distribution of states at time t if we executed π from time step 1 to t−1, starting from distribution ω at time 1, and dω,π = (1 − γ) ∑∞ t=1 γ dω,π the discounted distribution of states over the infinite horizon if we follow π, starting in ω at time 1.
Stable-Predictive Optimistic Counterfactual Regret Minimization

Furthermore, let L := ∑t τ=1 ` τ . With this notation, the decisions produced by OFTRL, as defined in (8), can be expressed as x = x̃(Lt−1 + m). Continuity of the argmin-function. The first step in the proof is to study the continuity of the argmin-function x̃. Intuitively, the role of the regularizer R is to smooth out the linear objective function 〈·,L〉. So, it seems only reasonable to expect that, the higher the constant that multiplies R, the less the argmin x̃(L) is affected by small changes of L. In fact, the following holds: Lemma 5. The argmin-function x̃ is η-Lipschitz continuous with respect to the dual norm, that is ‖x̃(L)− x̃(L′)‖ ≤ η‖L−L′‖∗.
Single Machine Graph Analytics on Massive Datasets Using Intel ® Optane TM DC Persistent Memory

IntelOptaneDCPersistentMemoryisanewkindofbyte-addressable memorywithhigherdensityandlowercostthanDRAM.Thisen-ablesaffordablesystemsthatsupportupto6TBofmemory.Inthis paper,weusesuchasystemformassivegraphsanalytics.Wedis-cusshowsuchasystemshouldbedeployedforsuchapplications, andevaluatethreeexistingshared-memorygraphframeworks, Galois,GAP,andGraphIt,onlargereal-worldweb-crawls.Werec-ommendalgorithmsandruntimeoptimizationsforgettingthebest performanceonsuchsystemsusingtheresultsofourstudy.We alsoshowthatfortheseapplications,theGaloisframeworkon OptaneDCPMMisonaverage2 × faster than D-Galois, the state-of-the-art distributed graph framework, on a production cluster with similar compute power. Thus, Optane DC PMM yields benefits in productivity, performance, and cost for massive graph analytics.
A Specification for Typed Template Haskell

Multi-stage programming is a proven technique that provides predictable performance characteristics by controlling code generation. We propose a core semantics for Typed Template Haskell, an extension of Haskell that supports multi staged programming that interacts well with polymorphism and qualified types. Our semantics relates a declarative source language with qualified types to a core language based on the the polymorphic lambda calculus augmented with multi-stage constructs.
Improving Christofides' Algorithm for the s-t Path TSP

We present a deterministic (1+√5/2)-approximation algorithm for the s-t path TSP for an arbitrary metric. Given a symmetric metric cost on n vertices including two prespecified endpoints, the problem is to find a shortest Hamiltonian path between the two endpoints; Hoogeveen showed that the natural variant of Christofides' algorithm is a 5/3-approximation algorithm for this problem, and this asymptotically tight bound in fact has been the best approximation ratio known until now. We modify this algorithm so that it chooses the initial spanning tree based on an optimal solution to the Held-Karp relaxation rather than a minimum spanning tree; we prove this simple but crucial modification leads to an improved approximation ratio, surpassing the 20-year-old ratio set by the natural Christofides' algorithm variant. Our algorithm also proves an upper bound of 1+√5/2 on the integrality gap of the path-variant Held-Karp relaxation. The techniques devised in this article can be applied to other optimization problems as well: these applications include improved approximation algorithms and improved LP integrality gap upper bounds for the prize-collecting s-t path problem and the unit-weight graphical metric s-t path TSP.
Rule-based Knowledge Representation for Service Level Agreement

Automated management and monitoring of service contracts like Service Level Agreements (SLAs) or higher-level policies is vital for efficient and reliable distributed service-oriented architectures (SOA) with high quality of ser-vice (QoS) levels. IT service provider need to manage, execute and maintain thousands of SLAs for different customers and different types of services, which needs new levels of flexibility and automation not available with the current technol-ogy. I propose a novel rule-based knowledge representation (KR) for SLA rules and a respective rule-based service level management (RBSLM) framework. My rule-based approach based on logic programming provides several advantages including automated rule chaining allowing for compact knowledge representation and high levels of automation as well as flexibility to adapt to rapidly changing business requirements. Therewith, I address an urgent need service-oriented busi-nesses do have nowadays which is to dynamically change their business and contractual logic in order to adapt to rapidly changing business environments and to overcome the restricting nature of slow change cycles.
Title : Authentication of Quantum Messages

Authentication is a well-studied area of classical cryptography: a sender and a receiver sharing a classical private key want to exchange a classical message with the guarantee that the message has not been modified or replaced by a dishonest party with control of the communication line. In this paper we study the authentication of messages composed of quantum states. We give a formal definition of authentication in the quantum setting. Assuming and have access to an insecure quantum channel and share a private, classical random key, we provide a non-interactive scheme that both enables to encrypt and authenticate (with unconditional security) an qubit message by encoding it into qubits, where the probability decreases exponentially in the security parameter . The scheme requires a private key of size . To achieve this, we give a highly efficient protocol for testing the purity of shared EPR pairs. It has long been known that learning information about a general quantum state will necessarily disturb it. We refine this result to show that such a disturbance can be done with few side effects, allowing it to circumvent cryptographic protections. Consequently, any scheme to authenticate quantum messages must also encrypt them. In contrast, no such constraint exists classically: authentication and encryption are independent tasks, and one can authenticate a message while leaving it publicly readable. This reasoning has two important consequences: On one hand, it allows us to give a lower bound of key bits for authenticating qubits, which makes our protocol asymptotically optimal. On the other hand, we use it to show that digitally signing quantum states is impossible, even with only computational security.
Batch Codes Through Dense Graphs Without Short Cycles

Consider a large database of n data items that need to be stored using m servers. We study how to encode information so that a large number k of read requests can be performed in parallel, while the rate remains constant (and ideally approaches one). This problem is equivalent to the design of multiset batch codes introduced by Ishai et al. We give the families of multiset batch codes with asymptotically optimal rates of the form 1-1/poly(k) and a number of servers m scaling polynomially in the number of read requests k. An advantage of our batch code constructions over most previously known multiset batch codes is explicit and deterministic decoding algorithms and asymptotically optimal fault tolerance. Our main technical innovation is a graphtheoretic method of designing multiset batch codes using dense bipartite graphs with no small cycles. We modify prior graph constructions of dense, high-girth graphs to obtain our batch code results. We achieve close-to-optimal tradeoffs between the parameters for bipartite graph-based batch codes.
Numerical Stability of Lanczos Methods

The Lanczos algorithm for matrix tridiagonalisation suffers from strong numerical instability in finite precision arithmetic when applied to evaluate matrix eigenvalues. The mechanism by which this instability arises is well documented in the literature. A recent application of the Lanczos algorithm proposed by Bai, Fahey and Golub allows quadrature evaluation of inner products of the form ψ · g(A) ·ψ. We show that this quadrature evaluation is numerically stable (accurate to machine precision) and explain how the numerical errors which are such a fundamental element of the finite precision Lanczos tridiagonalisation procedure are automatically and exactly compensated in the Bai, Fahey and Golub algorithm. In the process, we shed new light on the mechanism by which roundoff error corrupts the Lanczos procedure.
Network Beamforming using Relays with Perfect Channel Information

This paper is on beamforming in wireless relay networks with perfect channel information at the receiver and relays. It is assumed that every node in the network has its own power constraint. An amplify-and-forward protocol is used. Relays use not only the channel direction information to form a beam at the receiver but also the channel strength information to adaptively adjust their transmit powers. Our results show that the optimal power used at a relay is not a binary function. It can take any value between zero and its maximal transmit power. Also, surprisingly, this value depends on the quality of all other channels in addition to the relay's own channels.
The heterogeneity of inter-contact time distributions : its importance for routing in delay tolerant networks

Prior work on routing in delay tolerant networks (DTNs) has commonly made the assumption that each pair of nodes shares the same inter-contact time distribution as every other pair. The main argument in this paper is that researchers should also be looking at heterogeneous inter-contact time distributions. We demonstrate the presence of such heterogeneity in the often-used Dartmouth Wi-Fi data set. We show that the heavy-tailed distribution across all nodes, observed in previous work, can be explained as a composition of the distributions for each pair of nodes, and that these individual distributions are rarely heavy-tailed. We also show that DTN routing can benefit from knowing these distributions. We first introduce a new stochastic model focusing on the intercontact time distributions between all pairs of nodes, which we validate on real mobility data. We then analytically derive the mean delivery time for a bundle of information traversing the network for simple single copy routing schemes. The purpose is to examine the theoretic impact of heterogeneous inter-contact time distributions . Finally, we show that we can exploit this user diversity to improve routing performance. Based on the analytic model, we define an improved single copy “Spray and Wait” scheme that we compare to other routing schemes on the same real mobility data.
DeRPN: Taking a further step toward more general object detection

Most current detection methods have adopted anchor boxes as regression references. However, the detection performance is sensitive to the setting of the anchor boxes. A proper setting of anchor boxes may vary significantly across different datasets, which severely limits the universality of the detectors. To improve the adaptivity of the detectors, in this paper, we present a novel dimension-decomposition region proposal network (DeRPN) that can perfectly displace the traditional Region Proposal Network (RPN). DeRPN utilizes an anchor string mechanism to independently match object widths and heights, which is conducive to treating variant object shapes. In addition, a novel scale-sensitive loss is designed to address the imbalanced loss computations of different scaled objects, which can avoid the small objects being overwhelmed by larger ones. Comprehensive experiments conducted on both general object detection datasets (Pascal VOC 2007, 2012 and MS COCO) and scene text detection datasets (ICDAR 2013 and COCO-Text) all prove that our DeRPN can significantly outperform RPN. It is worth mentioning that the proposed DeRPN can be employed directly on different models, tasks, and datasets without any modifications of hyperparameters or specialized optimization, which further demonstrates its adaptivity. The code has been released at https://github.com/HCIILAB/DeRPN.
Scheduling and Codeword Length Optimization in Time Varying Wireless Networks

In this paper, a downlink scenario in which a single-antenna base station communicates with K single antenna users, over a time-correlated fading channel, is considered. It is assumed that channel state information is perfectly known at each receiver, while the statistical characteristics of the fading process and the fading gain at the beginning of each frame are known to the transmitter. By evaluating the random coding error exponent of the time-correlated fading channel, we show that there is an optimal codeword length which maximizes the throughput. We examine the throughput of the conventional scheduling that transmits to the user with the maximum signal to noise ratio using both fixed length codewords and variable length codewords. Although optimizing the codeword length improves the performance, it is shown that using the conventional scheduling, a gap of Omega(radiclog log log K) exists between the achievable throughput and the maximum possible throughput of the system. We propose a simple scheduling that considers both the signal to noise ratio and the channel time variation. We show that by using this scheduling, the gap between the achievable throughput and the maximum throughput of the system approaches zero
F eb 2 01 6 Network Inference by Learned Node-Specific Degree Prior

Qingming Tang ∗ Toyota Technological Institute at Chicago 6045 S. Kenwood Ave. Chicago, Illinois 60637 qmtang@ttic.edu Lifu Tu ∗ Toyota Technological Institute at Chicago 6045 S. Kenwood Ave. Chicago, IL lifu@ttic.edu Weiran Wang ∗ Toyota Technological Institute at Chicago 6045 S. Kenwood Ave. Chicago, IL weiranwang@ttic.edu Jinbo Xu Toyota Technological Institute at Chicago 6045 S. Kenwood Ave. Chicago, Illinois 60637 jinbo.xu@gmail.com
DARLENE – Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions

Background: Augmented reality (AR) and artificial intelligence (AI) are highly disruptive technologies that have revolutionised practices in a wide range of domains. Their potential has not gone unnoticed in the security sector with several law enforcement agencies (LEAs) employing AI applications in their daily operations for forensics and surveillance. In this paper, we present the DARLENE ecosystem, which aims to bridge existing gaps in applying AR and AI technologies for rapid tactical decision-making in situ with minimal error margin, thus enhancing LEAs’ efficiency and Situational Awareness (SA). Methods: DARLENE incorporates novel AI techniques for computer vision tasks such as activity recognition and pose estimation, while also building an AR framework for visualization of the inferenced results via dynamic content adaptation according to each individual officer’s stress level and current context. The concept has been validated with end-users through co-creation workshops, while the decision-making mechanism for enhancing LEAs’ SA has been assessed with experts. Regarding computer vision components, preliminary tests of the instance segmentation method for humans’ and objects’ detection have been conducted on a subset of videos from the RWF-2000 dataset for violence detection, which have also been used to test a human pose estimation method that has so far exhibited impressive results and will constitute the basis of further developments in DARLENE. Results: Evaluation results highlight that target users are positive towards the adoption of the proposed solution in field operations, and that the SA decision-making mechanism produces highly acceptable outcomes. Evaluation of the computer vision components yielded promising results and identified opportunities for improvement. Conclusions: This work provides the context of the DARLENE ecosystem and presents the DARLENE architecture, analyses its individual technologies, and demonstrates preliminary results, which are positive both in terms of technological achievements and user acceptance of the proposed solution.
A new efficient asymmetric cryptosystem based on the integer factorization problem of N=p2q

In this paper, we introduce a new scheme based on the hardness of factoring integers of the shape ��=�� 2 �� . Our scheme uses a combination of modular linear and modular squaring. We show that the decryption is 1-to-1 which is a great advantage over Rabin's cryptosystem. Its encryption speed has a complexity order faster than RSA and ECC. For decryption its speed is better than RSA and is marginally behind ECC. Constructed using a simple mathematical structure, it has low computational requirements and would enable communication devices with low computing power to deploy secure communication procedures efficiently.