Publications
Four-tap RF canceller evaluation for indoor in-band full-duplex wireless operation
January 15, 2017
Conference Paper
Published in:
IEEE Radio and Wireless Symp., RWS 2017, 15-18 January 2017.
Summary
Analog self-interference mitigation techniques are currently being investigated in a variety of operational settings for In-Band Full-Duplex (IBFD) systems. The significant multipath effects of realistic environments, such as inside buildings, can severely limit performance. The influence of different transceiver parameters on the effectiveness of a four-tap RF canceller using a tapped delay line architecture was characterized with a set of indoor measurements. The prototype canceller yielded up to 30 dB of cancellation over bandwidths ranging from 10 to 120 MHz centered at 2.45 GHz, and produced a combined analog system isolation that reached above 85 dB.
Summary
Analog self-interference mitigation techniques are currently being investigated in a variety of operational settings for In-Band Full-Duplex (IBFD) systems. The significant multipath effects of realistic environments, such as inside buildings, can severely limit performance. The influence of different transceiver parameters on the effectiveness of a four-tap RF canceller using a...
READ MORE
Spatially-resolved individual particle spectroscopy using photothermal modulation of Mie scattering
January 15, 2017
Journal Article
Published in:
Opt. Lett., Vol. 42, No. 2, 15 January 2017, pp. 203-6.
Summary
We report a photothermal modulation of Mie scattering (PMMS) method that enables concurrent spatial and spectral discrimination of individual micron-sized particles. This approach provides a direct measurement of the "fingerprint" infrared absorption spectrum with the spatial resolution of visible light. Trace quantities (tens of picograms) of material were deposited onto an infrared transparent substrate and simultaneously illuminated by a wavelength-tunable intensity-modulated quantum cascade pump laser and a continuous-wave 532 nm probe laser. Absorption of the pump laser by the particles results in direct modulation of the scatter field of the probe laser. The probe light scattered from the interrogated region is imaged onto a visible camera, enabling simultaneous probing of spatially-separated individual particles. By tuning the wavelength of the pump laser, the IR absorption spectrum is obtained. Using this approach, we measured the infrared absorption spectra of individual 3 um PMMA and silica spheres. Experimental PMMS signal amplitudes agree with modeling using an extended version of the Mie scattering theory for particles on substrates, enabling the prediction of the PMMS signal magnitude based on the material and substrate properties.
Summary
We report a photothermal modulation of Mie scattering (PMMS) method that enables concurrent spatial and spectral discrimination of individual micron-sized particles. This approach provides a direct measurement of the "fingerprint" infrared absorption spectrum with the spatial resolution of visible light. Trace quantities (tens of picograms) of material were deposited onto...
READ MORE
Interactive synthesis of code-level security rules
January 1, 2017
Thesis
Published in:
Thesis (M.S.)--Northeastern University, 2017.
Topic:
R&D group:
Summary
Software engineers inadvertently introduce bugs into software during the development process and these bugs can potentially be exploited once the software is deployed. As the size and complexity of software systems increase, it is important that we are able to verify and validate not only that the software behaves as it is expected to, but also that it does not violate any security policies or properties. One of the approaches to reduce software vulnerabilities is to use a bug detection tool during the development process. Many bug detection techniques are limited by the burdensome and error prone process of manually writing a bug specification. Other techniques are able to learn specifications from examples, but are limited in the types of bugs that they are able to discover. This work presents a novel, general approach for synthesizing security rules for C code. The approach combines human knowledge with an interactive logic programming synthesis system to learn Datalog rules for various security properties. The approach has been successfully used to synthesize rules for three intraprocedural security properties: (1) out of bounds array accesses, (2) return value validation, and (3) double freed pointers. These rules have been evaluated on randomly generated C code and yield a 0% false positive rate and a 0%, 20%, and 0% false negative rate, respectively for each rule.
Summary
Software engineers inadvertently introduce bugs into software during the development process and these bugs can potentially be exploited once the software is deployed. As the size and complexity of software systems increase, it is important that we are able to verify and validate not only that the software behaves as...
READ MORE
Causal inference under network interference: a framework for experiments on social networks
January 1, 2017
Thesis
Published in:
Thesis (Ph.D.)--Harvard University, 2017.
Summary
No man is an island, as individuals interact and influence one another daily in our society. When social influence takes place in experiments on a population of interconnected individuals, the treatment on a unit may affect the outcomes of other units, a phenomenon known as interference. This thesis develops a causal framework and inference methodology for experiments where interference takes place on a network of influence (i.e. network interference). In this framework, the network potential outcomes serve as the key quantity and flexible building blocks for causal estimands that represent a variety of primary, peer, and total treatment effects. These causal estimands are estimated via principled Bayesian imputation of missing outcomes. The theory on the unconfoundedness assumptions leading to simplified imputation highlights the importance of including relevant network covariates in the potential outcome model. Additionally, experimental designs that result in balanced covariates and sizes across treatment exposure groups further improve the causal estimate, especially by mitigating potential outcome model mis-specification. The true potential outcome model is not typically known in real-world experiments, so the best practice is to account for interference and confounding network covariates through both balanced designs and model-based imputation. A full factorial simulated experiment is formulated to demonstrate this principle by comparing performance across different randomization schemes during the design phase and estimators during the analysis phase, under varying network topology and true potential outcome models. Overall, this thesis asserts that interference is not just a nuisance for analysis but rather an opportunity for quantifying and leveraging peer effects in real-world experiments.
Summary
No man is an island, as individuals interact and influence one another daily in our society. When social influence takes place in experiments on a population of interconnected individuals, the treatment on a unit may affect the outcomes of other units, a phenomenon known as interference. This thesis develops a...
READ MORE
Suppressing relaxation in superconducting qubits by quasiparticle pumping
December 23, 2016
Journal Article
Published in:
Sci., Vol. 354, No. 6319, 23 December 2016, pp. 1573-77.
Summary
Dynamical error suppression techniques are commonly used to improve coherence in quantum systems. They reduce dephasing errors by applying control pulses designed to reverse erroneous coherent evolution driven by environmental noise. However, such methods cannot correct for irreversible processes such as energy relaxation. We investigate a complementary, stochastic approach to reducting errors: instead of deterministically reversing the unwanted qubit evolution, we use control pulses to shape the noise environment dynamically. in the context of superconducting qubits, we implement a pumping sequence to reduce the number of unpaired electrons (quasiparticles) in close proximity to the device. A 70% reduction in the quasiparticle density reesults in a threefold enhancement in qubit relaxation times and a comparable reduction in coherence variablity.
Summary
Dynamical error suppression techniques are commonly used to improve coherence in quantum systems. They reduce dephasing errors by applying control pulses designed to reverse erroneous coherent evolution driven by environmental noise. However, such methods cannot correct for irreversible processes such as energy relaxation. We investigate a complementary, stochastic approach to...
READ MORE
Approaches for language identification in mismatched environments
December 13, 2016
Conference Paper
Published in:
SLT 2016, IEEE Spoken Language Technology Workshop, 13-16 December 2016.
Summary
In this paper, we consider the task of language identification in the context of mismatch conditions. Specifically, we address the issue of using unlabeled data in the domain of interest to improve the performance of a state-of-the-art system. The evaluation is performed on a 9-language set that includes data in both conversational telephone speech and narrowband broadcast speech. Multiple experiments are conducted to assess the performance of the system in this condition and a number of alternatives to ameliorate the drop in performance. The best system evaluated is based on deep neural network (DNN) bottleneck features using i-vectors utilizing a combination of all the approaches proposed in this work. The resulting system improved baseline DNN system performance by 30%.
Summary
In this paper, we consider the task of language identification in the context of mismatch conditions. Specifically, we address the issue of using unlabeled data in the domain of interest to improve the performance of a state-of-the-art system. The evaluation is performed on a 9-language set that includes data in...
READ MORE
Multi-lingual deep neural networks for language recognition
December 13, 2016
Conference Paper
Published in:
SLT 2016, IEEE Spoken Language Technology Workshop, 13-16 December 2016.
Topic:
R&D area:
Summary
Multi-lingual feature extraction using bottleneck layers in deep neural networks (BN-DNNs) has been proven to be an effective technique for low resource speech recognition and more recently for language recognition. In this work we investigate the impact on language recognition performance of the multi-lingual BN-DNN architecture and training configurations for the NIST 2011 and 2015 language recognition evaluations (LRE11 and LRE15). The best performing multi-lingual BN-DNN configuration yields relative performance gains of 50% on LRE11 and 40% on LRE15 compared to a standard MFCC/SDC baseline system and 17% on LRE11 and 7% on LRE15 relative to a single language BN-DNN system. Detailed performance analysis using data from all 24 Babel languages, Fisher Spanish and Switchboard English shows the impact of language selection and the amount of training data on overall BN-DNN performance.
Summary
Multi-lingual feature extraction using bottleneck layers in deep neural networks (BN-DNNs) has been proven to be an effective technique for low resource speech recognition and more recently for language recognition. In this work we investigate the impact on language recognition performance of the multi-lingual BN-DNN architecture and training configurations for...
READ MORE
Resilience of cyber systems with over- and underregulation
December 9, 2016
Journal Article
Published in:
Risk Analysis, Vol. 37, No. 9, 2017, pp. 1644-51, DOI:10.1111/risa.12729.
Topic:
R&D area:
R&D group:
Summary
Recent cyber attacks provide evidence of increased threats to our critical systems and infrastructure. A common reaction to a new threat is to harden the system by adding new rules and regulations. As federal and state governments request new procedures to follow, each of their organizations implements their own cyber defense strategies. This unintentionally increases time and effort that employees spend on training and policy implementation and decreases the time and latitude to perform critical job functions, thus raising overall levels of stress. People's performance under stress, coupled with an overabundance of information, results in even more vulnerabilities for adversaries to exploit. In this article, we embed a simple regulatory model that accounts for cybersecurity human factors and an organization's regulatory environment in a model of a corporate cyber network under attack. The resulting model demonstrates the effect of under- and overregulation on an organization's resilience with respect to insider threats. Currently, there is a tendency to use ad-hoc approaches to account for human factors rather than to incorporate them into cyber resilience modeling. It is clear that using a systematic approach utilizing behavioral science, which already exists in cyber resilience assessment, would provide a more holistic view for decisionmakers.
Summary
Recent cyber attacks provide evidence of increased threats to our critical systems and infrastructure. A common reaction to a new threat is to harden the system by adding new rules and regulations. As federal and state governments request new procedures to follow, each of their organizations implements their own cyber...
READ MORE
Intersection and convex combination in multi-source spectral planted cluster detection
December 7, 2016
Conference Paper
Published in:
IEEE Global Conf. on Signal and Information Processing, GlobalSIP, 7-9 December 2016.
Topic:
R&D group:
Summary
Planted cluster detection is an important form of signal detection when the data are in the form of a graph. When there are multiple graphs representing multiple connection types, the method of aggregation can have significant impact on the results of a detection algorithm. This paper addresses the tradeoff between two possible aggregation methods: convex combination and intersection. For a spectral detection method, convex combination dominates when the cluster is relatively sparse in at least one graph, while the intersection method dominates in cases where it is dense across graphs. Experimental results confirm the theory. We consider the context of adversarial cluster placement, and determine how an adversary would distribute connections among the graphs to best avoid detection.
Summary
Planted cluster detection is an important form of signal detection when the data are in the form of a graph. When there are multiple graphs representing multiple connection types, the method of aggregation can have significant impact on the results of a detection algorithm. This paper addresses the tradeoff between...
READ MORE
Making #sense of #unstructured text data
December 5, 2016
Conference Paper
Published in:
30th Conf. on Neural Info. Processing Syst., NIPS 2016, 5-10 December 2016.
Topic:
R&D area:
Summary
Automatic extraction of intelligent and useful information from data is one of the main goals in data science. Traditional approaches have focused on learning from structured features, i.e., information in a relational database. However, most of the data encountered in practice are unstructured (i.e., social media posts, forums, emails and web logs); they do not have a predefined schema or format. In this work, we examine unsupervised methods for processing unstructured text data, extracting relevant information, and transforming it into structured information that can then be leveraged in various applications such as graph analysis and matching entities across different platforms. Various efforts have been proposed to develop algorithms for processing unstructured text data. At a top level, text can be either summarized by document level features (i.e., language, topic, genre, etc.) or analyzed at a word or sub-word level. Text analytics can be unsupervised, semi-supervised, or supervised. In this work, we focus on word analysis and unsupervised methods. Unsupervised (or semi-supervised) methods require less human annotation and can easily fulfill the role of automatic analysis. For text analysis, we focus on methods for finding relevant words in the text. Specifically, we look at social media data and attempt to predict hashtags for users' posts. The resulting hashtags can be used for downstream processing such as graph analysis. Automatic hashtag annotation is closely related to automatic tag extraction and keyword extraction. Techniques for hashtags extraction include topic analysis, supervised classifiers, machine translation methods, and collaborative filtering. Methods for keyword extraction include graph-based and topical analysis of text.
Summary
Automatic extraction of intelligent and useful information from data is one of the main goals in data science. Traditional approaches have focused on learning from structured features, i.e., information in a relational database. However, most of the data encountered in practice are unstructured (i.e., social media posts, forums, emails and...
READ MORE