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"abstract": "Deep learning methods have been very successful at radio frequency fingerprinting tasks, predicting the identity of transmitting devices with high accuracy. We study radio frequency fingerprinting deployments at resource-constrained edge devices. We use structured pruning to jointly train and sparsify neural networks tailored to edge hardware implementations. We compress convolutional layers by a <inline-formula><tex-math notation=\"LaTeX\">Z_$27.2\\times$_Z</tex-math></inline-formula> factor while incurring a negligible prediction accuracy decrease (less than 1 percent). We demonstrate the efficacy of our approach over multiple edge hardware platforms, including a Samsung Gallaxy S10 phone and a Xilinx-ZCU104 FPGA. Our method yields significant inference speedups, <inline-formula><tex-math notation=\"LaTeX\">Z_$11.5\\times$_Z</tex-math></inline-formula> on the FPGA and <inline-formula><tex-math notation=\"LaTeX\">Z_$3\\times$_Z</tex-math></inline-formula> on the smartphone, as well as high efficiency: the FPGA processing time is <inline-formula><tex-math notation=\"LaTeX\">Z_$17\\times$_Z</tex-math></inline-formula> smaller than in a V100 GPU. To the best of our knowledge, we are the first to explore the possibility of compressing networks for radio frequency fingerprinting; as such, our experiments can be seen as a means of characterizing the informational capacity associated with this specific learning task.",
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"abstract": "In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turn-around time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from <italic>8,380</italic> <italic>clinically validated samples with laboratory molecular-test</italic> (<italic>2,339</italic> Covid-19 positive and <italic>6,041</italic> Covid-19 negative) under quantitative RT-PCR (qRT-PCR) from certified laboratories. All collected samples were clinically labelled, i.e., Covid-19 positive or negative, according to the results in addition to the disease severity based on the qRT-PCR threshold cycle (Ct) and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called <italic>‘DeepCough’</italic>. Two different versions of DeepCough based on the number of tensor dimensions, i.e., DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform prototype web-app <italic>‘CoughDetect’</italic>. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of <inline-formula><tex-math notation=\"LaTeX\">Z_$98.80\\% \\pm 0.83\\%$_Z</tex-math></inline-formula>, sensitivity of <inline-formula><tex-math notation=\"LaTeX\">Z_$96.43\\% \\pm 1.85\\%$_Z</tex-math></inline-formula>, and specificity of <inline-formula><tex-math notation=\"LaTeX\">Z_$96.20\\% \\pm 1.74\\%$_Z</tex-math></inline-formula> and average AUC of <inline-formula><tex-math notation=\"LaTeX\">Z_$81.08\\% \\pm 5.05\\%$_Z</tex-math></inline-formula> for the recognition of three severity levels. Our proposed web tool as a point-of-need primary diagnostic test for Covid-19 facilitates the rapid detection of the infection. We believe it has the potential to significantly hamper the Covid-19 pandemic across the world.",
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"content": "In an attempt to reduce the infection rate of the COrona VIrus Disease-19 (Covid-19) countries around the world have echoed the exigency for an economical, accessible, point-of-need diagnostic test to identify Covid-19 carriers so that they (individuals who test positive) can be advised to self isolate rather than the entire community. Availability of a quick turn-around time diagnostic test would essentially mean that life, in general, can return to normality-at-large. In this regards, studies concurrent in time with ours have investigated different respiratory sounds, including cough, to recognise potential Covid-19 carriers. However, these studies lack clinical control and rely on Internet users confirming their test results in a web questionnaire (crowdsourcing) thus rendering their analysis inadequate. We seek to evaluate the detection performance of a primary screening tool of Covid-19 solely based on the cough sound from <italic>8,380</italic> <italic>clinically validated samples with laboratory molecular-test</italic> (<italic>2,339</italic> Covid-19 positive and <italic>6,041</italic> Covid-19 negative) under quantitative RT-PCR (qRT-PCR) from certified laboratories. All collected samples were clinically labelled, i.e., Covid-19 positive or negative, according to the results in addition to the disease severity based on the qRT-PCR threshold cycle (Ct) and lymphocytes count from the patients. Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) for cough sound detection with subsequent classification based on a tensor of audio sonographs and deep artificial neural network classifier with convolutional layers called <italic>‘DeepCough’</italic>. Two different versions of DeepCough based on the number of tensor dimensions, i.e., DeepCough2D and DeepCough3D, have been investigated. These methods have been deployed in a multi-platform prototype web-app <italic>‘CoughDetect’</italic>. Covid-19 recognition results rates achieved a promising AUC (Area Under Curve) of <inline-formula><tex-math notation=\"LaTeX\">$98.80\\% \\pm 0.83\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>98</mml:mn><mml:mo>.</mml:mo><mml:mn>80</mml:mn><mml:mo>%</mml:mo><mml:mo>±</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>83</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"andreuperez-ieq1-3061402.gif\"/></alternatives></inline-formula>, sensitivity of <inline-formula><tex-math notation=\"LaTeX\">$96.43\\% \\pm 1.85\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>96</mml:mn><mml:mo>.</mml:mo><mml:mn>43</mml:mn><mml:mo>%</mml:mo><mml:mo>±</mml:mo><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>85</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"andreuperez-ieq2-3061402.gif\"/></alternatives></inline-formula>, and specificity of <inline-formula><tex-math notation=\"LaTeX\">$96.20\\% \\pm 1.74\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>96</mml:mn><mml:mo>.</mml:mo><mml:mn>20</mml:mn><mml:mo>%</mml:mo><mml:mo>±</mml:mo><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>74</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"andreuperez-ieq3-3061402.gif\"/></alternatives></inline-formula> and average AUC of <inline-formula><tex-math notation=\"LaTeX\">$81.08\\% \\pm 5.05\\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>81</mml:mn><mml:mo>.</mml:mo><mml:mn>08</mml:mn><mml:mo>%</mml:mo><mml:mo>±</mml:mo><mml:mn>5</mml:mn><mml:mo>.</mml:mo><mml:mn>05</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"andreuperez-ieq4-3061402.gif\"/></alternatives></inline-formula> for the recognition of three severity levels. Our proposed web tool as a point-of-need primary diagnostic test for Covid-19 facilitates the rapid detection of the infection. We believe it has the potential to significantly hamper the Covid-19 pandemic across the world.",
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"title": "A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels",
"normalizedTitle": "A Generic Deep Learning Based Cough Analysis System From Clinically Validated Samples for Point-of-Need Covid-19 Test and Severity Levels",
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"abstract": "A lack of floor plans is a fundamental obstacle to ubiquitous indoor location-based services. Recent work have made significant progress to accuracy, but they largely rely on slow crowdsensing that may take weeks or even months to collect enough data. In this paper, we propose Knitter that can generate accurate floor maps by a single random user’s one hour data collection efforts, and demonstrate how such maps can be used for indoor navigation. Knitter extracts high quality floor layout information from single images, calibrates user trajectories, and filters outliers. It uses a multi-hypothesis map fusion framework that updates landmark positions/orientations and accessible areas incrementally according to evidences from each measurement. Our experiments on three different large buildings (up to <inline-formula><tex-math notation=\"LaTeX\">Z_$140\\times 50\\;\\mathrm{m}^2$_Z</tex-math></inline-formula>) with 30+ users show that Knitter produces correct map topology, with landmark location errors of <inline-formula><tex-math notation=\"LaTeX\">Z_$3\\sim 5\\;\\mathrm{m}$_Z</tex-math></inline-formula> and orientation errors of <inline-formula><tex-math notation=\"LaTeX\">Z_$4\\sim 6^\\circ$_Z</tex-math></inline-formula>, both at 90-percentile. Our results are comparable to the state-of-the-art at more than <inline-formula><tex-math notation=\"LaTeX\">Z_$20\\times$_Z</tex-math></inline-formula> speed up: data collection in each of the three buildings can finish in about one hour even by a novice user trained just a few minutes.",
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"abstract": "The proposed herein is a scalable high-radix (i.e., <inline-formula><tex-math notation=\"LaTeX\">Z_$2^m$_Z</tex-math></inline-formula>) Montgomery Modular (MM) Multiplication circuit replacing the integer multiplications in each iteration of the Montgomery MM algorithm (related to the product of <inline-formula><tex-math notation=\"LaTeX\">Z_$m$_Z</tex-math></inline-formula> bits of the multiplier and the multiplicand) with carry-save compressions and completely eliminating costly multiplications. Furthermore, the proposed Montgomery MM decomposes the multiplicand itself using a radix of <inline-formula><tex-math notation=\"LaTeX\">Z_$2^w$_Z</tex-math></inline-formula> with <inline-formula><tex-math notation=\"LaTeX\">Z_$w\\geq 2m$_Z</tex-math></inline-formula>, thereby achieving a scalable design, which can deliver an issue latency of one cycle and a cycle (count) latency of <inline-formula><tex-math notation=\"LaTeX\">Z_$O(N^2/(wmp))$_Z</tex-math></inline-formula> where <inline-formula><tex-math notation=\"LaTeX\">Z_$p$_Z</tex-math></inline-formula> denotes the number of available processing elements, each of which is designed to complete the above iteration by computing in part the product of <inline-formula><tex-math notation=\"LaTeX\">Z_$w$_Z</tex-math></inline-formula> bits of the multiplicand and <inline-formula><tex-math notation=\"LaTeX\">Z_$m$_Z</tex-math></inline-formula> bits of the multiplier. The area complexity of the proposed Montgomery MM is <inline-formula><tex-math notation=\"LaTeX\">Z_$O(wmp)$_Z</tex-math></inline-formula>, and thus, the Area-Latency-Product complexity is <inline-formula><tex-math notation=\"LaTeX\">Z_$O(N^{2})$_Z</tex-math></inline-formula>.",
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"content": "The proposed herein is a scalable high-radix (i.e., <inline-formula><tex-math notation=\"LaTeX\">$2^m$</tex-math><alternatives><mml:math><mml:msup><mml:mn>2</mml:mn><mml:mi>m</mml:mi></mml:msup></mml:math><inline-graphic xlink:href=\"zhang-ieq1-3052999.gif\"/></alternatives></inline-formula>) Montgomery Modular (MM) Multiplication circuit replacing the integer multiplications in each iteration of the Montgomery MM algorithm (related to the product of <inline-formula><tex-math notation=\"LaTeX\">$m$</tex-math><alternatives><mml:math><mml:mi>m</mml:mi></mml:math><inline-graphic xlink:href=\"zhang-ieq2-3052999.gif\"/></alternatives></inline-formula> bits of the multiplier and the multiplicand) with carry-save compressions and completely eliminating costly multiplications. Furthermore, the proposed Montgomery MM decomposes the multiplicand itself using a radix of <inline-formula><tex-math notation=\"LaTeX\">$2^w$</tex-math><alternatives><mml:math><mml:msup><mml:mn>2</mml:mn><mml:mi>w</mml:mi></mml:msup></mml:math><inline-graphic xlink:href=\"zhang-ieq3-3052999.gif\"/></alternatives></inline-formula> with <inline-formula><tex-math notation=\"LaTeX\">$w\\geq 2m$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>w</mml:mi><mml:mo>≥</mml:mo><mml:mn>2</mml:mn><mml:mi>m</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"zhang-ieq4-3052999.gif\"/></alternatives></inline-formula>, thereby achieving a scalable design, which can deliver an issue latency of one cycle and a cycle (count) latency of <inline-formula><tex-math notation=\"LaTeX\">$O(N^2/(wmp))$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>/</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mi>w</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"zhang-ieq5-3052999.gif\"/></alternatives></inline-formula> where <inline-formula><tex-math notation=\"LaTeX\">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href=\"zhang-ieq6-3052999.gif\"/></alternatives></inline-formula> denotes the number of available processing elements, each of which is designed to complete the above iteration by computing in part the product of <inline-formula><tex-math notation=\"LaTeX\">$w$</tex-math><alternatives><mml:math><mml:mi>w</mml:mi></mml:math><inline-graphic xlink:href=\"zhang-ieq7-3052999.gif\"/></alternatives></inline-formula> bits of the multiplicand and <inline-formula><tex-math notation=\"LaTeX\">$m$</tex-math><alternatives><mml:math><mml:mi>m</mml:mi></mml:math><inline-graphic xlink:href=\"zhang-ieq8-3052999.gif\"/></alternatives></inline-formula> bits of the multiplier. The area complexity of the proposed Montgomery MM is <inline-formula><tex-math notation=\"LaTeX\">$O(wmp)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mi>w</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"zhang-ieq9-3052999.gif\"/></alternatives></inline-formula>, and thus, the Area-Latency-Product complexity is <inline-formula><tex-math notation=\"LaTeX\">$O(N^{2})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"zhang-ieq10-3052999.gif\"/></alternatives></inline-formula>.",
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"abstract": "In this paper, we focus upon the important problem of indexing and searching highly repetitive DNA sequence collections. Given a collection <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {G}$_Z</tex-math></inline-formula> of <inline-formula><tex-math notation=\"LaTeX\">Z_$t$_Z</tex-math></inline-formula> sequences <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {S}_{i}$_Z</tex-math></inline-formula> of length <inline-formula><tex-math notation=\"LaTeX\">Z_$n$_Z</tex-math></inline-formula> each, we can represent <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {G}$_Z</tex-math></inline-formula> succinctly in <inline-formula><tex-math notation=\"LaTeX\">Z_$2n\\mathcal {H}_{k}(\\mathcal {T}) + \\mathcal {O}(n^{\\prime }\\ {\\log \\log n}) + o(q n^{\\prime }) + o(tn)$_Z</tex-math></inline-formula> bits using <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {O}(t n^{2} + q n^{\\prime })$_Z</tex-math></inline-formula> time, where <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {H}_{k}(\\mathcal {T})$_Z</tex-math></inline-formula> is the <inline-formula><tex-math notation=\"LaTeX\">Z_$k$_Z</tex-math></inline-formula>th-order empirical entropy of the sequence <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {T} \\in \\mathcal {G}$_Z</tex-math></inline-formula> that is used as the reference sequence, <inline-formula><tex-math notation=\"LaTeX\">Z_$n^{\\prime }$_Z</tex-math></inline-formula> is the total number of variations between <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {T}$_Z</tex-math></inline-formula> and the sequences in <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {G}$_Z</tex-math></inline-formula>, and <inline-formula><tex-math notation=\"LaTeX\">Z_$q$_Z</tex-math></inline-formula> is a small fixed constant. We can restore any length <inline-formula><tex-math notation=\"LaTeX\">Z_$ {len}$_Z</tex-math></inline-formula> substring <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {S}[ {sp}, \\dots, {sp} + {len}-1]$_Z</tex-math></inline-formula> of <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {S} \\in \\mathcal {G}$_Z</tex-math></inline-formula> in <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {O}\\bigl (n_{s}^{\\prime } + {len}(\\log n)^{2} / {\\log \\log n}\\bigr)$_Z</tex-math></inline-formula> time and report all positions where <inline-formula><tex-math notation=\"LaTeX\">Z_$P$_Z</tex-math></inline-formula> occurs in <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {G}$_Z</tex-math></inline-formula> in <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {O}\\bigl (m \\cdot t + {occ} \\cdot t \\cdot (\\log n)^{2}/\\log \\log n \\bigr)$_Z</tex-math></inline-formula> time. In addition, we propose a dynamic programming method to find the variations between <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {T}$_Z</tex-math></inline-formula> and the sequences in <inline-formula><tex-math notation=\"LaTeX\">Z_$\\mathcal {G}$_Z</tex-math></inline-formula> in a space-efficient way, with which we can build succinct structures to enable efficient search. For highly repetitive sequences, experimental results on the tested data demonstrate that the proposed method has significant advantages in space usage and retrieval time over the current state-of-the-art methods. The source code is available online.",
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"content": "In this paper, we focus upon the important problem of indexing and searching highly repetitive DNA sequence collections. Given a collection <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {G}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">G</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq1-2968323.gif\"/></alternatives></inline-formula> of <inline-formula><tex-math notation=\"LaTeX\">$t$</tex-math><alternatives><mml:math><mml:mi>t</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq2-2968323.gif\"/></alternatives></inline-formula> sequences <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {S}_{i}$</tex-math><alternatives><mml:math><mml:msub><mml:mi mathvariant=\"script\">S</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math><inline-graphic xlink:href=\"huo-ieq3-2968323.gif\"/></alternatives></inline-formula> of length <inline-formula><tex-math notation=\"LaTeX\">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq4-2968323.gif\"/></alternatives></inline-formula> each, we can represent <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {G}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">G</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq5-2968323.gif\"/></alternatives></inline-formula> succinctly in <inline-formula><tex-math notation=\"LaTeX\">$2n\\mathcal {H}_{k}(\\mathcal {T}) + \\mathcal {O}(n^{\\prime }\\ {\\log \\log n}) + o(q n^{\\prime }) + o(tn)$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mi>n</mml:mi><mml:msub><mml:mi mathvariant=\"script\">H</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi mathvariant=\"script\">T</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi mathvariant=\"script\">O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:msup><mml:mi>n</mml:mi><mml:mo>'</mml:mo></mml:msup><mml:mspace width=\"4pt\"/><mml:mrow><mml:mo form=\"prefix\">log</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>o</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>q</mml:mi><mml:msup><mml:mi>n</mml:mi><mml:mo>'</mml:mo></mml:msup><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>o</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"huo-ieq6-2968323.gif\"/></alternatives></inline-formula> bits using <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {O}(t n^{2} + q n^{\\prime })$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"script\">O</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:msup><mml:mi>n</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:mi>q</mml:mi><mml:msup><mml:mi>n</mml:mi><mml:mo>'</mml:mo></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"huo-ieq7-2968323.gif\"/></alternatives></inline-formula> time, where <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {H}_{k}(\\mathcal {T})$</tex-math><alternatives><mml:math><mml:mrow><mml:msub><mml:mi mathvariant=\"script\">H</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi mathvariant=\"script\">T</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math><inline-graphic xlink:href=\"huo-ieq8-2968323.gif\"/></alternatives></inline-formula> is the <inline-formula><tex-math notation=\"LaTeX\">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq9-2968323.gif\"/></alternatives></inline-formula>th-order empirical entropy of the sequence <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {T} \\in \\mathcal {G}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"script\">T</mml:mi><mml:mo>∈</mml:mo><mml:mi mathvariant=\"script\">G</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"huo-ieq10-2968323.gif\"/></alternatives></inline-formula> that is used as the reference sequence, <inline-formula><tex-math notation=\"LaTeX\">$n^{\\prime }$</tex-math><alternatives><mml:math><mml:msup><mml:mi>n</mml:mi><mml:mo>'</mml:mo></mml:msup></mml:math><inline-graphic xlink:href=\"huo-ieq11-2968323.gif\"/></alternatives></inline-formula> is the total number of variations between <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {T}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">T</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq12-2968323.gif\"/></alternatives></inline-formula> and the sequences in <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {G}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">G</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq13-2968323.gif\"/></alternatives></inline-formula>, and <inline-formula><tex-math notation=\"LaTeX\">$q$</tex-math><alternatives><mml:math><mml:mi>q</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq14-2968323.gif\"/></alternatives></inline-formula> is a small fixed constant. We can restore any length <inline-formula><tex-math notation=\"LaTeX\">$ {len}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"huo-ieq15-2968323.gif\"/></alternatives></inline-formula> substring <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {S}[ {sp}, \\dots, {sp} + {len}-1]$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"script\">S</mml:mi><mml:mo>[</mml:mo><mml:mrow><mml:mi>s</mml:mi><mml:mi>p</mml:mi></mml:mrow><mml:mo>,</mml:mo><mml:mo>⋯</mml:mo><mml:mo>,</mml:mo><mml:mrow><mml:mi>s</mml:mi><mml:mi>p</mml:mi></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi></mml:mrow><mml:mo>-</mml:mo><mml:mn>1</mml:mn><mml:mo>]</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href=\"huo-ieq16-2968323.gif\"/></alternatives></inline-formula> of <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {S} \\in \\mathcal {G}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"script\">S</mml:mi><mml:mo>∈</mml:mo><mml:mi mathvariant=\"script\">G</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href=\"huo-ieq17-2968323.gif\"/></alternatives></inline-formula> in <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {O}\\bigl (n_{s}^{\\prime } + {len}(\\log n)^{2} / {\\log \\log n}\\bigr)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"script\">O</mml:mi><mml:mfenced separators=\"\" open=\"(\" close=\")\"><mml:msubsup><mml:mi>n</mml:mi><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mo>'</mml:mo></mml:msubsup><mml:mo>+</mml:mo><mml:mrow><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi></mml:mrow><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>/</mml:mo><mml:mrow><mml:mo form=\"prefix\">log</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:mfenced></mml:mrow></mml:math><inline-graphic xlink:href=\"huo-ieq18-2968323.gif\"/></alternatives></inline-formula> time and report all positions where <inline-formula><tex-math notation=\"LaTeX\">$P$</tex-math><alternatives><mml:math><mml:mi>P</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq19-2968323.gif\"/></alternatives></inline-formula> occurs in <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {G}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">G</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq20-2968323.gif\"/></alternatives></inline-formula> in <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {O}\\bigl (m \\cdot t + {occ} \\cdot t \\cdot (\\log n)^{2}/\\log \\log n \\bigr)$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant=\"script\">O</mml:mi><mml:mfenced separators=\"\" open=\"(\" close=\")\"><mml:mi>m</mml:mi><mml:mo>·</mml:mo><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mrow><mml:mi>o</mml:mi><mml:mi>c</mml:mi><mml:mi>c</mml:mi></mml:mrow><mml:mo>·</mml:mo><mml:mi>t</mml:mi><mml:mo>·</mml:mo><mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup><mml:mo>/</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mo form=\"prefix\">log</mml:mo><mml:mi>n</mml:mi></mml:mfenced></mml:mrow></mml:math><inline-graphic xlink:href=\"huo-ieq21-2968323.gif\"/></alternatives></inline-formula> time. In addition, we propose a dynamic programming method to find the variations between <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {T}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">T</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq22-2968323.gif\"/></alternatives></inline-formula> and the sequences in <inline-formula><tex-math notation=\"LaTeX\">$\\mathcal {G}$</tex-math><alternatives><mml:math><mml:mi mathvariant=\"script\">G</mml:mi></mml:math><inline-graphic xlink:href=\"huo-ieq23-2968323.gif\"/></alternatives></inline-formula> in a space-efficient way, with which we can build succinct structures to enable efficient search. For highly repetitive sequences, experimental results on the tested data demonstrate that the proposed method has significant advantages in space usage and retrieval time over the current state-of-the-art methods. The source code is available online.",
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"normalizedAbstract": "In this paper, we focus upon the important problem of indexing and searching highly repetitive DNA sequence collections. Given a collection - of - sequences - of length - each, we can represent - succinctly in - bits using - time, where - is the -th-order empirical entropy of the sequence - that is used as the reference sequence, - is the total number of variations between - and the sequences in -, and - is a small fixed constant. We can restore any length - substring - of - in - time and report all positions where - occurs in - in - time. In addition, we propose a dynamic programming method to find the variations between - and the sequences in - in a space-efficient way, with which we can build succinct structures to enable efficient search. For highly repetitive sequences, experimental results on the tested data demonstrate that the proposed method has significant advantages in space usage and retrieval time over the current state-of-the-art methods. The source code is available online.",
"title": "Efficient Compression and Indexing for Highly Repetitive DNA Sequence Collections",
"normalizedTitle": "Efficient Compression and Indexing for Highly Repetitive DNA Sequence Collections",
"fno": "08964488",
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"keywords": [
"Bioinformatics",
"Computational Complexity",
"Data Analysis",
"Data Compression",
"DNA",
"Dynamic Programming",
"Entropy",
"Indexing",
"String Matching",
"Kkth Order Empirical Entropy",
"Reference Sequence",
"Repetitive Sequences",
"DNA Sequence Collections",
"Dynamic Programming",
"Indexing",
"Data Compression",
"DNA",
"Indexing",
"Sequential Analysis",
"Dynamic Programming",
"Data Structures",
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],
"authors": [
{
"givenName": "Hongwei",
"surname": "Huo",
"fullName": "Hongwei Huo",
"affiliation": "School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China",
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{
"givenName": "Xiaoyang",
"surname": "Chen",
"fullName": "Xiaoyang Chen",
"affiliation": "School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China",
"__typename": "ArticleAuthorType"
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{
"givenName": "Xu",
"surname": "Guo",
"fullName": "Xu Guo",
"affiliation": "School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, China",
"__typename": "ArticleAuthorType"
},
{
"givenName": "Jeffrey Scott",
"surname": "Vitter",
"fullName": "Jeffrey Scott Vitter",
"affiliation": "Department of Computer & Information Science, The University of Mississippi, University, MS, USA",
"__typename": "ArticleAuthorType"
}
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"abstract": "Tumor clustering is a powerful approach for cancer class discovery which is crucial to the effective treatment of cancer. Many traditional clustering methods such as NMF-based models, have been widely used to identify tumors. However, they cannot achieve satisfactory results. Recently, subspace clustering approaches have been proposed to improve the performance by dividing the original space into multiple low-dimensional subspaces. Among them, low rank representation is becoming a popular approach to attain subspace clustering. In this paper, we propose a novel Low Rank Subspace Clustering model via Discrete Constraint and Hypergraph Regularization (DHLRS). The proposed method learns the cluster indicators directly by using discrete constraint, which makes the clustering task simple. For each subspace, we adopt Schatten Z_$p$_Z -norm to better approximate the low rank constraint. Moreover, Hypergraph Regularization is adopted to infer the complex relationship between genes and intrinsic geometrical structure of gene expression data in each subspace. Finally, the molecular pattern of tumor gene expression data sets is discovered according to the optimized cluster indicators. Experiments on both synthetic data and real tumor gene expression data sets prove the effectiveness of proposed DHLRS.",
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"content": "Tumor clustering is a powerful approach for cancer class discovery which is crucial to the effective treatment of cancer. Many traditional clustering methods such as NMF-based models, have been widely used to identify tumors. However, they cannot achieve satisfactory results. Recently, subspace clustering approaches have been proposed to improve the performance by dividing the original space into multiple low-dimensional subspaces. Among them, low rank representation is becoming a popular approach to attain subspace clustering. In this paper, we propose a novel Low Rank Subspace Clustering model via Discrete Constraint and Hypergraph Regularization (DHLRS). The proposed method learns the cluster indicators directly by using discrete constraint, which makes the clustering task simple. For each subspace, we adopt Schatten $p$ -norm to better approximate the low rank constraint. Moreover, Hypergraph Regularization is adopted to infer the complex relationship between genes and intrinsic geometrical structure of gene expression data in each subspace. Finally, the molecular pattern of tumor gene expression data sets is discovered according to the optimized cluster indicators. Experiments on both synthetic data and real tumor gene expression data sets prove the effectiveness of proposed DHLRS.",
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"abstract": "Multimodal data integration is an important framework for cancer subtype discovery as it can blend the inherent properties of individual modalities with their cross-platform correlations to infer clinically relevant subtypes. The main problem here is the appropriate selection of relevant and complementary modalities. Another problem is the `high dimension-low sample size' nature of each modality. The current research work proposes a novel algorithm to construct a low-rank joint subspace from the low-rank subspaces of individual high-dimensional modalities. Statistical hypothesis testing is introduced to effectively estimate the rank of each modality by separating the signal component from its noise counterpart. Two quantitative indices are proposed to evaluate the quality of different modalities, the first one assesses the degree of relevance of the cluster structure embedded within each modality, while the second measure evaluates the amount of cluster information shared between two modalities. To construct the joint subspace, the algorithm selects the most relevant modalities with maximum shared information. During data integration, the intersection between two subspaces is also considered to select cluster information and filter out the noise from different subspaces. The efficacy of clustering on the joint subspace, extracted by the proposed algorithm, is compared with that of several existing integrative clustering approaches on real-life multimodal data sets. Experimental results show that the identified subtypes have closer resemblance with the clinically established subtypes as compared to the subtypes identified by the existing approaches. Survival analysis has revealed the significant differences between survival profiles of the identified subtypes, while robustness analysis shows that the identified subtypes are not sensitive towards perturbation of the data sets.",
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