Miracle-dz/UniFAR-FTUs huggingface dataset:
One-way communication complexity and the Neciporuk lower bound on formula size

In this paper the Neciporuk method for proving lower bounds on the size of Boolean formulae is reformulated in terms of one-way communication complexity. We investigate the scenarios of probabilistic formulae, nondeterministic formulae, and quantum formulae. In all cases we can use results about one-way communication complexity to prove lower bounds on formula size. In the latter two cases we newly develop the employed communication complexity bounds. The main results regarding formula size are as follows: A polynomial size gap between probabilistic/quantum and deterministic formulae. A near-quadratic size gap for nondeterministic formulae with limited access to nondeterministic bits. A near quadratic lower bound on quantum formula size, as well as a polynomial separation between the sizes of quantum formulae with and without multiple read random inputs. The methods for quantum and probabilistic formulae employ a variant of the Neciporuk bound in terms of the VC-dimension. Regarding communication complexity we give optimal separations between one-way and two-way protocols in the cases of limited nondeterministic and quantum communication, and we show that zero-error quantum one-way communication complexity asymptotically equals deterministic one-way communication complexity for total functions.
Approximate Graph Coloring by Semide nite ProgrammingDavid Karger

We consider the problem of coloring k-colorable graphs with the fewest possible colors. We present a randomized polynomial time algorithm that colors a 3-colorable graph on n vertices with minfO((1=3 log 1=2 log n); O(n 1=4 log 1=2 n)g colors where is the maximum degree of any vertex. Besides giving the best known approximation ratio in terms of n, this marks the rst non-trivial approximation result as a function of the maximum degree. This result can be generalized to k-colorable graphs to obtain a coloring using minfO((12=k log 1=2 log n); O(n 13=(k+1) log 1=2 n)g colors. Our results are inspired by the recent work of Goemans and Williamson who used an algorithm for semideenite optimization problems, which generalize linear programs, to obtain improved approximations for the MAX CUT and MAX 2-SAT problems. An intriguing outcome of our work is a duality relationship established between the value of the optimum solution to our semideenite program and the Lovv asz #-function. We show lower bounds on the gap between the optimum solution of our semideenite program and the actual chromatic number; by duality this also demonstrates interesting new facts about the #-function.
Approximate Graph Coloring by Semide nite ProgrammingDavid Karger

We consider the problem of coloring k-colorable graphs with the fewest possible colors. We present a randomized polynomial time algorithm that colors a 3-colorable graph on n vertices with minfO((1=3 log 1=2 log n); O(n 1=4 log 1=2 n)g colors where is the maximum degree of any vertex. Besides giving the best known approximation ratio in terms of n, this marks the rst non-trivial approximation result as a function of the maximum degree. This result can be generalized to k-colorable graphs to obtain a coloring using minfO((1? 2=k log 1=2 log n); O(n 1? 3=(k+1) log 1=2 n)g colors. Our results are inspired by the recent work of Goemans and Williamson who used an algorithm for semideenite optimization problems, which generalize linear programs, to obtain improved approximations for the MAX CUT and MAX 2-SAT problems. An intriguing outcome of our work is a duality relationship established between the value of the optimum solution to our semideenite program and the Lovv asz #-function. We show lower bounds on the gap between the optimum solution of our semideenite program and the actual chromatic number; by duality this also demonstrates interesting new facts about the #-function.
Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces

With the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting users' privacy. Although location synthesizers have been widely studied, existing synthesizers do not provide utility, privacy, or scalability sufficiently, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We simultaneously factorize these two tensors via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors using the MH (Metropolis-Hastings) algorithm. We comprehensively evaluate the proposed method using two datasets. Our experimental results show that the proposed method preserves various statistical features, provides plausible deniability, and synthesizes large-scale location traces in practical time. The proposed method also significantly outperforms the state-of-the-art with regard to the utility, privacy, and scalability.
D S ] 7 S ep 2 00 6 CR-precis : A deterministic summary structure for update data streams

We present the CR-precis structure, that is a general-purpose, deterministic and sub-linear data structure for summarizing update data streams. The CR-precis structure yields the first deterministic sub-linear space/time algorithms for update streams for answering a variety of fundamental stream queries, such as, (a) point queries, (b) range queries, (c) finding approximate frequent items, (d) finding approximate quantiles, (e) finding approximate hierarchical heavy hitters, (f) estimating inner-products, (g) near-optimal B-bucket histograms, etc.
Revealing the Transmission Dynamics of COVID-19 : A Bayesian Framework for ! ! Estimation

In epidemiological modelling, the instantaneous reproduction number, ! , is important to understand the transmission dynamics of infectious diseases. Current ! estimates often suffer from problems such as lagging, averaging and uncertainties demoting the usefulness of ! To address these problems, we propose a new method in the framework of sequential Bayesian inference where a Data Assimilation approach is taken for ! estimation, resulting in the stateof-the-art ‘DARt’ system for ! estimation. With DARt, the problem of time misalignment caused by lagging observations is tackled by incorporating observation delays into the joint inference of infections and ! ; the drawback of averaging is improved by instantaneous updating upon new observations and a model selection mechanism capturing abrupt changes caused by interventions; the uncertainty is quantified and reduced by employing Bayesian smoothing. We validate the performance of DARt through simulations and demonstrate its power in revealing the transmission dynamics of COVID-19.
SANNS: Scaling Up Secure Approximate k -Nearest Neighbors Search

—We present new secure protocols for approximate k -nearest neighbor search ( k -NNS) over the Euclidean distance in the semi-honest model, which scale to massive datasets. One of the new ingredients is a circuit for the approximate top-k selection from n numbers that is built from O ( n +poly( k )) comparators. Using this circuit as a subroutine, we design new k -NNS algorithms and two corresponding secure protocols: 1) optimized linear scan; 2) clustering-based sublinear time algorithm. The new secure protocols utilize a combination of additively-homomorphic encryption, garbled circuits and oblivious RAM. Along the way, we introduce various optimizations to these primitives, which drastically improve concrete efﬁciency. We evaluate the new protocols empirically and show that they are able to handle datasets that are signiﬁcantly larger than in the prior work. For instance, running on two standard Azure instances within the same availability zone, for a dataset of 96 - dimensional descriptors of 10 000 000 images, we can ﬁnd 10 nearest neighbors with average accuracy 0 . 9 in under 10 seconds improving upon prior work by at least two orders of magnitude .
Spatially Coupled Ensembles Universally Achieve Capacity Under Belief Propagation

We investigate spatially coupled code ensembles. For transmission over the binary erasure channel, it was recently shown that spatial coupling increases the belief propagation threshold of the ensemble to essentially the maximum a priori threshold of the underlying component ensemble. This explains why convolutional LDPC ensembles, originally introduced by Felström and Zigangirov, perform so well over this channel. We show that the equivalent result holds true for transmission over general binary-input memoryless output-symmetric channels. More precisely, given a desired error probability and a gap to capacity, we can construct a spatially coupled ensemble that fulfills these constraints universally on this class of channels under belief propagation decoding. In fact, most codes in this ensemble have this property. The quantifier universal refers to the single ensemble/code that is good for all channels but we assume that the channel is known at the receiver. The key technical result is a proof that, under belief-propagation decoding, spatially coupled ensembles achieve essentially the area threshold of the underlying uncoupled ensemble. We conclude by discussing some interesting open problems.
PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement Learning Policies

We propose a novel reinforcement learning based framework PoBRL for solving multi-document summarization. PoBRL jointly optimizes over the following three objectives necessary for a high-quality summary: importance, relevance, and length. Our strategy decouples this multi-objective optimization into different subproblems that can be solved individually by reinforcement learning. Utilizing PoBRL, we then blend each learned policies together to produce a summary that is a concise and complete representation of the original input. Our empirical analysis shows state-of-the-art performance on several multi-document datasets. Human evaluation also shows that our method produces high-quality output.
On the shape of the general error locator polynomial for cyclic codes

A general result on the explicit form of the general error locator polynomial for all cyclic codes is given, along with several specific results for classes of cyclic codes. From these, a theoretically justification of the sparsity of the general error locator polynomial is obtained for all cyclic codes with $t\leq 3$ and $n<63$, except for three cases.
0 50 30 87 v 3 2 9 M ar 2 00 6 On the Bias of Traceroute Sampling : or , Power-law Degree Distributions in Regular Graphs

Understanding the structure of the Internet graph is a crucial step for building accurate network models and designing efficient algorithms for Internet applications. Yet, obtaining its graph structure is a surprisingly difficult task, as edges cannot be explicitly queried. Instead, empirical studies rely on traceroutes to build what are essentially single-source, all-destinations, shortest-path trees. These trees only sample a fraction of the network’s edges, and a recent paper by Lakhina et al. found empirically that the resuting sample is intrinsically biased. For instance, the observed degree distribution under traceroute sampling exhibits a power law even when the underlying degree distribution is Poisson. In this paper, we study the bias of traceroute sampling systematically, and, for a very general class of underlying degree distributions, calculate the likely observed distributions explicitly. To do this, we use a continuous-time realization of the process of exposing the BFS tree of a random graph with a given degree distribution, calculate the expected degree distribution of the tree, and show that it is sharply concentrated. As example applications of our machinery, we show how traceroute sampling finds powerlaw degree distributions in both δ-regular and Poisson-distributed random graphs. Thus, our work puts the observations of Lakhina et al. on a rigorous footing, and extends them to nearly arbitrary degree distributions.
Mitigating Data Exfiltration in Storage-as-a-Service Clouds

Existing processes and methods for incident handling are geared towards infrastructures and operational models that will be increasingly outdated by cloud computing. Research has shown that to adapt incident handling to cloud computing environments, cloud customers must establish clarity about their requirements on Cloud Service Providers (CSPs) for successful handling of incidents and contract CSPs accordingly. Secondly, CSPs must strive to support these requirements and mirror them in their Service Level Agreements. Intrusion Detection Systems (IDS) have been used widely to detect malicious behaviors in network communication and hosts. Facing new application scenarios in Cloud Computing, the IDS approaches yield several problems since the operator of the IDS should be the user, not the administrator of the Cloud infrastructure. Cloud providers need to enable possibilities to deploy and configure IDS for the user - which poses its own challenges. Current research and commercial solutions primarily focus on protecting against Denial of Service attacks and attacks against the Cloud's virtual infrastructure. To counter these challenges, we propose a capability that aims to both detect and prevent the potential of data exfiltration by using a novel deception-based methodology. We also introduce a method of increasing the data protection level based on various threat conditions.
Efficient Cyber Attack Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA

Industrial control systems (ICSs) are widely used and vital to industry and society. Their failure can have severe impact on both the economy and human life. Hence, these systems have become an attractive target for physical and cyber attacks alike. In this article, we examine an attack detection method based on simple and lightweight neural networks, namely, 1D convolutional neural networks and autoencoders. We apply these networks to both the time and frequency domains of the data and discuss the pros and cons of each representation approach. The suggested method is evaluated on three popular public datasets, and detection rates matching or exceeding previously published detection results are achieved, while demonstrating a small footprint, short training and detection times, and generality. We also show the effectiveness of PCA, which, given proper data preprocessing and feature selection, can provide high attack detection rates in many settings. Finally, we study the proposed method’s robustness against adversarial attacks that exploit inherent blind spots of neural networks to evade detection while achieving their intended physical effect. Our results show that the proposed method is robust to such evasion attacks: in order to evade detection, the attacker is forced to sacrifice the desired physical impact on the system.
Susceptibility to Image Resolution in Face Recognition and Training Strategies to Enhance Robustness

Face recognition approaches often rely on equal image resolution for verifying faces on two images. However, in practical applications, those image resolutions are usually not in the same range due to different image capture mechanisms or sources. In this work, we first analyze the impact of image resolutions on face verification performance with a state-of-the-art face recognition model. For images synthetically reduced to $5\,\times\,5$ px resolution, the verification performance drops from $99.23\%$ increasingly down to almost $55\%$. Especially for cross-resolution image pairs (one high- and one low-resolution image), the verification accuracy decreases even further. We investigate this behavior more in-depth by looking at the feature distances for every 2-image test pair. To tackle this problem, we propose the following two methods: 1) Train a state-of-the-art face-recognition model straightforwardly with $50\%$ low-resolution images directly within each batch. 2) Train a siamese-network structure and add a cosine distance feature loss between high- and low-resolution features. Both methods show an improvement for cross-resolution scenarios and can increase the accuracy at very low resolution to approximately $70\%$. However, a disadvantage is that a specific model needs to be trained for every resolution pair. Thus, we extend the aforementioned methods by training them with multiple image resolutions at once. The performances for particular testing image resolutions are slightly worse, but the advantage is that this model can be applied to arbitrary resolution images and achieves an overall better performance ($97.72\%$ compared to $96.86\%$). Due to the lack of a benchmark for arbitrary resolution images for the cross-resolution and equal-resolution task, we propose an evaluation protocol for five well-known datasets, focusing on high, mid, and low-resolution images.
Error Exponents for Entanglement Concentration

Consider entanglement concentration schemes that convert n identical copies of a pure state into a maximally entangled state of a desired size with success probability being close to one in the asymptotic limit. We give the distillable entanglement, the number of Bell pairs distilled per copy, as a function of an error exponent, which represents the rate of decrease in failure probability as n tends to infinity. The formula fills the gap between the least upper bound of distillable entanglement in probabilistic concentration, which is the well-known entropy of entanglement, and the maximum attained in deterministic concentration. The method of types in information theory enables the detailed analysis of the distillable entanglement in terms of the error rate. In addition to the probabilistic argument, we consider another type of entanglement concentration scheme, where the initial state is deterministically transformed into a (possibly mixed) final state whose fidelity to a maximally entangled state of a desired size converges to one in the asymptotic limit. We show that the same formula as in the probabilistic argument is valid for the argument on fidelity by replacing the success probability with the fidelity. Furthermore, we also discuss entanglement yield when optimal success probability or optimal fidelity converges to zero in the asymptotic limit (strong converse), and give the explicit formulae for those cases.
The Mexican Hat Wavelet Family. Application to point source detection in cosmic microwave background maps

We propose a detection technique in 2D images based on an isotropic wavelet family. This family is naturally constructed as an extension of the Gaussian-Mexican Hat Wavelet pair and for that reason we call it the Mexican Hat Wavelet Family (MHWF). We show the performance of these wavelets for dealing with the detection of point extragalactic sources in cosmic microwave background (CMB) maps: a very important issue within the most general problem of the component separation of the microwave sky. In particular, simulations for one channel (44 GHz) of the forthcoming Planck mission have been analysed. We present the results and compare them with those obtained using the Mexican Hat Wavelet technique (MHW), which has been proven a suitable tool for detecting point sources. The MHWF provides a point source catalogue at 44 GHz of 690 sources. Under the same conditions, the MHW provides 604 sources.
University of Dundee Designing a semantic sketchbook to create opportunities for serendipity

Citation for published version (APA): Woods, M. , Maxwell, D. , Makri, S. , Bental, D. , Kefalidou, G. , & Sharples, S. (2012). Designing a semantic sketchbook to create opportunities for serendipity. In BCS-HCI '12 Proceedings of the 26th Annual BCS Interaction Specialist Group Conference on People and Computers (pp. 357-362). (Electronic Workshops in Computing (eWiC)). Swinton: British Computer Society.
Performance Comparison of Dual Connectivity and Hard Handover for LTE-5G Tight Integration in mmWave Cellular Networks

This Thesis will propose an LTE-5G tight integration architecture, based on mobile terminals' dual connectivity to LTE and 5G radio access networks, and will evaluate which are the new network procedures that will be needed to support it. Moreover, this new architecture will be implemented in the ns-3 simulator, and a thorough simulation campaign will be conducted in order to evaluate its performance, with respect to the baseline of handover between LTE and 5G
A Generalized Arc-Consistency Algorithm for a Class of Counting Constraints

This paper introduces the SEQ BIN meta-constraint with a polytime algorithm achieving generalized arc-consistency. SEQ BIN can be used for encoding counting constraints such as CHANGE, SMOOTH, or INCREASING NVALUE. For all of them the time and space complexity is linear in the sum of domain sizes, which improves or equals the best known results of the literature.
Markov Convexity and Local Rigidity of Distorted Metrics [ Extended Abstract ] ∗

The geometry of discrete tree metrics is studied from the following perspectives: (1) Markov p-convexity, which was shown by Lee, Naor, and Peres to be a property of p-convex Banach space, is shown here to be equivalent to p-convexity of Banach spaces. (2) On the other hand, there exists an example of a metric space which is not Markov p-convex for any p < ∞, but does not uniformly contain complete binary trees. Note that the previous item implies that Banach spaces contain complete binary trees uniformly if and only if they are not Markov p-convex for any p < ∞. (3) For every B > 4, a metric space X is constructed such that all tree metrics can be embedded in X with distortion at most B, but when large complete binary trees are embedded in X, the distortion tends to B. Therefore the class of finite tree metrics do exhibit a dichotomy in the distortions achievable when embedding them in other metric spaces. This is in contrast to the dichotomy exhibited by the class of finite subsets of L1, and the class of all finite metric spaces.
Manifold Optimization Methods for Hybrid beamforming in mmWave Dual-Function Radar-Communication System

—As a cost-effective alternative, hybrid analog and digital beamforming architecture is a promising scheme for mil-limeter wave (mmWave) system. This paper considers two hybrid beamforming architectures, i.e. the partially-connected and fully-connected structures, for mmWave dual-function radar communication (DFRC) system, where the transmitter communicates with the downlink users and detects radar targets simultaneously. The optimization problems are formulated by minimizing a weighted summation of radar and communication performance, subject to constant modulus and power constraints. To tackle the non-convexities caused by the two resultant problems, effective Riemannian optimization algorithms are proposed. Speciﬁcally, for the fully-connected structure, a manifold algorithm based on the alternating direction method of multipliers (ADMM) is developed. While for the partially-connected structure, a low-complexity Riemannian product manifold trust region (RPM-TR) algorithm is proposed to approach the near-optional solution. Numerical simulations are provided to demonstrate the effectiveness of the proposed methods.