9 research outputs found
Advances in optimisation algorithms and techniques for deep learning
In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by some optimisation techniques which have been studied extensively. These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures. However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature. This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models. The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers. From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts
Reservoir based spiking models for univariate Time Series Classification
A variety of advanced machine learning and deep learning algorithms achieve state-of-the-art performance on various temporal processing tasks. However, these methods are heavily energy inefficientβthey run mainly on the power hungry CPUs and GPUs. Computing with Spiking Networks, on the other hand, has shown to be energy efficient on specialized neuromorphic hardware, e.g., Loihi, TrueNorth, SpiNNaker, etc. In this work, we present two architectures of spiking models, inspired from the theory of Reservoir Computing and Legendre Memory Units, for the Time Series Classification (TSC) task. Our first spiking architecture is closer to the general Reservoir Computing architecture and we successfully deploy it on Loihi; the second spiking architecture differs from the first by the inclusion of non-linearity in the readout layer. Our second model (trained with Surrogate Gradient Descent method) shows that non-linear decoding of the linearly extracted temporal features through spiking neurons not only achieves promising results, but also offers low computation-overhead by significantly reducing the number of neurons compared to the popular LSM based modelsβmore than 40x reduction with respect to the recent spiking model we compare with. We experiment on five TSC datasets and achieve new SoTA spiking results (βas much as 28.607% accuracy improvement on one of the datasets), thereby showing the potential of our models to address the TSC tasks in a green energy-efficient manner. In addition, we also do energy profiling and comparison on Loihi and CPU to support our claims
Multimodal sentiment analysis in real-life videos
This thesis extends the emerging field of multimodal sentiment analysis of real-life videos, taking two components into consideration: the emotion and the emotion's target.
The emotion component of media is traditionally represented as a segment-based intensity model of emotion classes. This representation is replaced here by a value- and time-continuous view. Adjacent research fields, such as affective computing, have largely neglected the linguistic information available from automatic transcripts of audio-video material. As is demonstrated here, this text modality is well-suited for time- and value-continuous prediction. Moreover, source-specific problems, such as trustworthiness, have been largely unexplored so far.
This work examines perceived trustworthiness of the source, and its quantification, in user-generated video data and presents a possible modelling path. Furthermore, the transfer between the continuous and discrete emotion representations is explored in order to summarise the emotional context at a segment level.
The other component deals with the target of the emotion, for example, the topic the speaker is addressing. Emotion targets in a video dataset can, as is shown here, be coherently extracted based on automatic transcripts without limiting a priori parameters, such as the expected number of targets. Furthermore, alternatives to purely linguistic investigation in predicting targets, such as knowledge-bases and multimodal systems, are investigated.
A new dataset is designed for this investigation, and, in conjunction with proposed novel deep neural networks, extensive experiments are conducted to explore the components described above.
The developed systems show robust prediction results and demonstrate strengths of the respective modalities, feature sets, and modelling techniques. Finally, foundations are laid for cross-modal information prediction systems with applications to the correction of corrupted in-the-wild signals from real-life videos
The 1991 3rd NASA Symposium on VLSI Design
Papers from the symposium are presented from the following sessions: (1) featured presentations 1; (2) very large scale integration (VLSI) circuit design; (3) VLSI architecture 1; (4) featured presentations 2; (5) neural networks; (6) VLSI architectures 2; (7) featured presentations 3; (8) verification 1; (9) analog design; (10) verification 2; (11) design innovations 1; (12) asynchronous design; and (13) design innovations 2
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The translational potential of sleep and circadian rhythm disturbances as a biomarker of Alzheimer's disease
ΠΠΎΠ»ΠΎΠ΄Π΅ΠΆΡ ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ: ΡΠ±ΠΎΡΠ½ΠΈΠΊ ΡΡΡΠ΄ΠΎΠ² XVI ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΉ Π½Π°ΡΡΠ½ΠΎ-ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ², Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² ΠΈ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ ΡΡΡΠ½ΡΡ , 3-7 Π΄Π΅ΠΊΠ°Π±ΡΡ 2018 Π³., Π³. Π’ΠΎΠΌΡΠΊ
Π‘Π±ΠΎΡΠ½ΠΈΠΊ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ Π΄ΠΎΠΊΠ»Π°Π΄Ρ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΠ΅ Π½Π° XVI ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΉ Π½Π°ΡΡΠ½ΠΎ-ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ², Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² ΠΈ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ
ΡΡΠ΅Π½ΡΡ
Β«ΠΠΎΠ»ΠΎΠ΄Π΅ΠΆΡ ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈΒ», ΠΏΡΠΎΡΠ΅Π΄ΡΠ΅ΠΉ Π² Π’ΠΎΠΌΡΠΊΠΎΠΌ ΠΏΠΎΠ»ΠΈΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ΅ Π½Π° Π±Π°Π·Π΅ ΠΠ½ΠΆΠ΅Π½Π΅ΡΠ½ΠΎΠΉ ΡΠΊΠΎΠ»Ρ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΈ ΡΠΎΠ±ΠΎΡΠΎΡΠ΅Ρ
Π½ΠΈΠΊΠΈ. ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΡΠ±ΠΎΡΠ½ΠΈΠΊΠ° ΠΎΡΡΠ°ΠΆΠ°ΡΡ Π΄ΠΎΠΊΠ»Π°Π΄Ρ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ², Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² ΠΈ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ
ΡΡΠ΅Π½ΡΡ
, ΠΏΡΠΈΠ½ΡΡΡΠ΅ ΠΊ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΡ Π½Π° ΡΠ΅ΠΊΡΠΈΡΡ
: Β«ΠΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π΄Π°Π½Π½ΡΡ
Β», Β«ΠΠ²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΡ ΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ Π² ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Β», Β«Π ΠΎΠ±ΠΎΡΠΎΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ ΠΌΠ΅Ρ
Π°ΡΡΠΎΠ½Π½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», Β«Π¦ΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΡ, IT ΠΈ ΡΠΈΡΡΠΎΠ²Π°Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ°Β», Β«ΠΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½Π°Ρ Π³ΡΠ°ΡΠΈΠΊΠ° ΠΈ Π΄ΠΈΠ·Π°ΠΉΠ½Β». Π‘Π±ΠΎΡΠ½ΠΈΠΊ ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½ Π΄Π»Ρ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠΎΠ² Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² ΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΡ
ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΡΡΠ΅ΠΉ
ΠΠΎΠ»ΠΎΠ΄Π΅ΠΆΡ ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ: ΡΠ±ΠΎΡΠ½ΠΈΠΊ ΡΡΡΠ΄ΠΎΠ² XVI ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΉ Π½Π°ΡΡΠ½ΠΎ-ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ², Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² ΠΈ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ ΡΡΡΠ½ΡΡ , 3-7 Π΄Π΅ΠΊΠ°Π±ΡΡ 2018 Π³., Π³. Π’ΠΎΠΌΡΠΊ
Π‘Π±ΠΎΡΠ½ΠΈΠΊ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ Π΄ΠΎΠΊΠ»Π°Π΄Ρ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π½ΡΠ΅ Π½Π° XVI ΠΠ΅ΠΆΠ΄ΡΠ½Π°ΡΠΎΠ΄Π½ΠΎΠΉ Π½Π°ΡΡΠ½ΠΎ-ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠΎΠ½ΡΠ΅ΡΠ΅Π½ΡΠΈΠΈ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ², Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² ΠΈ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ
ΡΡΠ΅Π½ΡΡ
Β«ΠΠΎΠ»ΠΎΠ΄Π΅ΠΆΡ ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈΒ», ΠΏΡΠΎΡΠ΅Π΄ΡΠ΅ΠΉ Π² Π’ΠΎΠΌΡΠΊΠΎΠΌ ΠΏΠΎΠ»ΠΈΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ΅ Π½Π° Π±Π°Π·Π΅ ΠΠ½ΠΆΠ΅Π½Π΅ΡΠ½ΠΎΠΉ ΡΠΊΠΎΠ»Ρ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΈ ΡΠΎΠ±ΠΎΡΠΎΡΠ΅Ρ
Π½ΠΈΠΊΠΈ. ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΡΠ±ΠΎΡΠ½ΠΈΠΊΠ° ΠΎΡΡΠ°ΠΆΠ°ΡΡ Π΄ΠΎΠΊΠ»Π°Π΄Ρ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ², Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² ΠΈ ΠΌΠΎΠ»ΠΎΠ΄ΡΡ
ΡΡΠ΅Π½ΡΡ
, ΠΏΡΠΈΠ½ΡΡΡΠ΅ ΠΊ ΠΎΠ±ΡΡΠΆΠ΄Π΅Π½ΠΈΡ Π½Π° ΡΠ΅ΠΊΡΠΈΡΡ
: Β«ΠΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· Π΄Π°Π½Π½ΡΡ
Β», Β«ΠΠ²ΡΠΎΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΡ ΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠ΅ Π² ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ°Ρ
Β», Β«Π ΠΎΠ±ΠΎΡΠΎΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ ΠΌΠ΅Ρ
Π°ΡΡΠΎΠ½Π½ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΡΒ», Β«Π¦ΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΡ, IT ΠΈ ΡΠΈΡΡΠΎΠ²Π°Ρ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠ°Β», Β«ΠΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½Π°Ρ Π³ΡΠ°ΡΠΈΠΊΠ° ΠΈ Π΄ΠΈΠ·Π°ΠΉΠ½Β». Π‘Π±ΠΎΡΠ½ΠΈΠΊ ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½ Π΄Π»Ρ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠΎΠ² Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² ΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΡ
ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΡΡΠ΅ΠΉ
Smoking and Second Hand Smoking in Adolescents with Chronic Kidney Disease: A Report from the Chronic Kidney Disease in Children (CKiD) Cohort Study
The goal of this study was to determine the prevalence of smoking and second hand smoking [SHS] in adolescents with CKD and their relationship to baseline parameters at enrollment in the CKiD, observational cohort study of 600 children (aged 1-16 yrs) with Schwartz estimated GFR of 30-90 ml/min/1.73m2. 239 adolescents had self-report survey data on smoking and SHS exposure: 21 [9%] subjects had βeverβ smoked a cigarette. Among them, 4 were current and 17 were former smokers. Hypertension was more prevalent in those that had βeverβ smoked a cigarette (42%) compared to non-smokers (9%), p\u3c0.01. Among 218 non-smokers, 130 (59%) were male, 142 (65%) were Caucasian; 60 (28%) reported SHS exposure compared to 158 (72%) with no exposure. Non-smoker adolescents with SHS exposure were compared to those without SHS exposure. There was no racial, age, or gender differences between both groups. Baseline creatinine, diastolic hypertension, C reactive protein, lipid profile, GFR and hemoglobin were not statistically different. Significantly higher protein to creatinine ratio (0.90 vs. 0.53, p\u3c0.01) was observed in those exposed to SHS compared to those not exposed. Exposed adolescents were heavier than non-exposed adolescents (85th percentile vs. 55th percentile for BMI, p\u3c 0.01). Uncontrolled casual systolic hypertension was twice as prevalent among those exposed to SHS (16%) compared to those not exposed to SHS (7%), though the difference was not statistically significant (p= 0.07). Adjusted multivariate regression analysis [OR (95% CI)] showed that increased protein to creatinine ratio [1.34 (1.03, 1.75)] and higher BMI [1.14 (1.02, 1.29)] were independently associated with exposure to SHS among non-smoker adolescents. These results reveal that among adolescents with CKD, cigarette use is low and SHS is highly prevalent. The association of smoking with hypertension and SHS with increased proteinuria suggests a possible role of these factors in CKD progression and cardiovascular outcomes