Flow Trees: A Lower Bound Computation Tool with Applications to Rearrangeable Multihop Lightwave Network Optimization

This paper presents a new method for computing the lower bounds for multihop network design problems which is particularly well suited to optical networks. More specifically, given N stations each with d transceivers and pairwise average traffic values of the stations, the method provides a lower bound for the combined problem of finding optimum (i) allocation of wavelengths to the stations to determine a configuration, and (ii) routing of the traffic on this configuration while minimizing congestion - defined as the maximum flow assigned on any link. The lower bounds can be computed in time polynomial in the network size. Consequently, the results in this work yield a tool which can be used in (i) evaluating the quality of heuristic design algorithms, and (ii) determining a termination criteria during minimization. The lower bound computation is based on first building flow trees to find a lower bound on the total flow, and then distributing the total flow over the links to minimize the congestion

Logical Embeddings for Minimum Congestion Routing in Lightwave Networks

The problem considered in this paper is motivated by the independence between logical and physical topology in Wavelength Division Multiplexing WDM based local and metropolitan lightwave networks. This paper suggests logical embeddings of digraphs into multihop lightwave networks to maximize the throughput under nonuniform traffic conditions. Defining congestion as the maximum flow carried on any link, two perturbation heuristics are presented to find a good logical embedding on which the routing problem is solved with minimum congestion. A constructive proof for a lower bound of the problem is given, and obtaining an optimal solution for integral routing is shown to be NP-Complete. The performance of the heuristics is empirically analyzed on various traffic models. Simulation results show that our heuristics perform on the average from a computed lower bound Since this lower bound is not quite tight we suspect that the actual performance is better In addition we show that 5%-20% performance improvements can be obtained over the previous work

Word Sense Induction with Knowledge Distillation from BERT

Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embeddings are an efficient alternative in these settings. Such methods typically use one vector to encode multiple different meanings of a word, and incur errors due to polysemy. This paper proposes a two-stage method to distill multiple word senses from a pre-trained language model (BERT) by using attention over the senses of a word in a context and transferring this sense information to fit multi-sense embeddings in a skip-gram-like framework. We demonstrate an effective approach to training the sense disambiguation mechanism in our model with a distribution over word senses extracted from the output layer embeddings of BERT. Experiments on the contextual word similarity and sense induction tasks show that this method is superior to or competitive with state-of-the-art multi-sense embeddings on multiple benchmark data sets, and experiments with an embedding-based topic model (ETM) demonstrates the benefits of using this multi-sense embedding in a downstream application

Antikernel: A Decentralized Secure Hardware-Software Operating System Architecture

The ``kernel model has been part of operating system architecture for decades, but upon closer inspection it clearly violates the principle of least required privilege. The kernel is a single entity which provides many services (memory management, interfacing to drivers, context switching, IPC) which have no real relation to each other, and has the ability to observe or tamper with all state of the system. This work presents Antikernel, a novel operating system architecture consisting of both hardware and software components and designed to be fundamentally more secure than the state of the art. To make formal verification easier, and improve parallelism, the Antikernel system is highly modular and consists of many independent hardware state machines (one or more of which may be a general-purpose CPU running application or systems software) connected by a packet-switched network-on-chip (NoC). We create and verify an FPGA-based prototype of the system

A Cross-Domain Evaluation of Approaches for Causal Knowledge Extraction

Causal knowledge extraction is the task of extracting relevant causes and effects from text by detecting the causal relation. Although this task is important for language understanding and knowledge discovery, recent works in this domain have largely focused on binary classification of a text segment as causal or non-causal. In this regard, we perform a thorough analysis of three sequence tagging models for causal knowledge extraction and compare it with a span based approach to causality extraction. Our experiments show that embeddings from pre-trained language models (e.g. BERT) provide a significant performance boost on this task compared to previous state-of-the-art models with complex architectures. We observe that span based models perform better than simple sequence tagging models based on BERT across all 4 data sets from diverse domains with different types of cause-effect phrases

Improving Neural Ranking Models with Traditional IR Methods

Neural ranking methods based on large transformer models have recently gained significant attention in the information retrieval community, and have been adopted by major commercial solutions. Nevertheless, they are computationally expensive to create, and require a great deal of labeled data for specialized corpora. In this paper, we explore a low resource alternative which is a bag-of-embedding model for document retrieval and find that it is competitive with large transformer models fine tuned on information retrieval tasks. Our results show that a simple combination of TF-IDF, a traditional keyword matching method, with a shallow embedding model provides a low cost path to compete well with the performance of complex neural ranking models on 3 datasets. Furthermore, adding TF-IDF measures improves the performance of large-scale fine tuned models on these tasks.Comment: Short paper, 4 page

Environmental Authentication in Malware

Malware needs to execute on a target machine while simultaneously keeping its payload confidential from a malware analyst. Standard encryption can be used to ensure the confidentiality, but it does not address the problem of hiding the key. Any analyst can find the decryption key if it is stored in the malware or derived in plain view. One approach is to derive the key from a part of the environment which changes when the analyst is present. Such malware derives a key from the environment and encrypts its true functionality under this key. In this paper, we present a formal framework for environmental authentication. We formalize the interaction between malware and analyst in three settings: 1) blind: in which the analyst does not have access to the target environment, 2) basic: where the analyst can load a single analysis toolkit on an effected target, and 3) resettable: where the analyst can create multiple copies of an infected environment. We show necessary and sufficient conditions for malware security in the blind and basic games and show that even under mild conditions, the analyst can always win in the resettable scenario

Histopathological image analysis: a review,”

Abstract-Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe