Optical cavity-referenced terahertz synthesizer with 15-digit short-term stability

Abstract Stable terahertz sources are required to advance high precision terahertz applications such as molecular spectroscopy, terahertz radars, and wireless communications. Here, we demonstrate a photonic scheme of terahertz synthesis using an optical comb in stabilization to an ultra-low expansion optical cavity offering a 15-digit accuracy. By heterodyne photomixing of comb lines, terahertz frequencies of 0.10 – 1.10 THz are synthesized with a 2-mHz linewidth and a fractional instability of 3.26×10-15 at 1.3-s integration. Compared to other state-of-the-art counterparts, our terahertz synthesizer offers a fine frequency tuning capability in steps of 100 MHz and an extremely low level of phase noise below -70 dBc/Hz even at 1 Hz offset. Such unprecedented performance is expected to drastically improve the signal-to-noise ratio of terahertz radars, the resolving power of terahertz molecular spectroscopy, and the transmission capacity of wireless communications.

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A comparative study on the cold type and thermal type field emission guns used in the same electron microscope

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100107435 ◽

1978 ◽

Vol 36 (1) ◽

pp. 58-59

Author(s):

M. Iwatsuki ◽

Y. Kokubo ◽

Y. Harada

Keyword(s):

Electron Microscope ◽

Field Emission ◽

Signal To Noise Ratio ◽

Electron Source ◽

Resolving Power ◽

Optical Source ◽

Experimental Conditions ◽

High Brightness ◽

Illumination System ◽

Transmission Electron

On accout of its high brightness, small optical source size, and minimal energy spread, the field emission gun (FEG) has the advantage that it provides the conventional transmission electron microscope (TEM) with a highly coherent illumination system and directly improves the resolving power and signal-to-noise ratio of the scanning electron microscope (SEM). The FEG is generally classified into two types; the cold field emission (C-FEG) and thermal field emission gun (T-FEG). The former, in which a field emitter is used at the room temperature, was successfully developed as an electron source for the SEM. The latter, in which the emitter is heated to the temperature range of 1000-1800°K, was also proved to be very suited as an electron source for the TEM, as well as for the SEM. Some characteristics of the two types of the FEG have been studied and reported by many authors. However, the results of the respective types have been obtained separately under different experimental conditions.

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A Two-Layer Long Short-Term Memory Network for Bottleneck Prediction in Multi-Job Manufacturing Systems

Volume 3: Manufacturing Equipment and Systems ◽

10.1115/msec2018-6678 ◽

2018 ◽

Cited By ~ 1

Author(s):

Xingjian Lai ◽

Huanyi Shui ◽

Jun Ni

Keyword(s):

Manufacturing Systems ◽

Short Term Memory ◽

Complex Dynamics ◽

State Of The Art ◽

Short Term ◽

Term Memory ◽

Future Production ◽

Effective Manner ◽

Long Short Term Memory ◽

Factory Floor

Throughput bottlenecks define and constrain the productivity of a production line. Prediction of future bottlenecks provides a great support for decision-making on the factory floor, which can help to foresee and formulate appropriate actions before production to improve the system throughput in a cost-effective manner. Bottleneck prediction remains a challenging task in literature. The difficulty lies in the complex dynamics of manufacturing systems. There are multiple factors collaboratively affecting bottleneck conditions, such as machine performance, machine degradation, line structure, operator skill level, and product release schedules. These factors impact on one another in a nonlinear manner and exhibit long-term temporal dependencies. State-of-the-art research utilizes various assumptions to simplify the modeling by reducing the input dimensionality. As a result, those models cannot accurately reflect complex dynamics of the bottleneck in a manufacturing system. To tackle this problem, this paper will propose a systematic framework to design a two-layer Long Short-Term Memory (LSTM) network tailored to the dynamic bottleneck prediction problem in multi-job manufacturing systems. This neural network based approach takes advantage of historical high dimensional factory floor data to predict system bottlenecks dynamically considering the future production planning inputs. The model is demonstrated with data from an automotive underbody assembly line. The result shows that the proposed method can achieve higher prediction accuracy compared with current state-of-the-art approaches.

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Arabic dialect sentiment analysis with ZERO effort. \\ Case study: Algerian dialect

INTELIGENCIA ARTIFICIAL ◽

10.4114/intartif.vol23iss65pp124-135 ◽

2020 ◽

Vol 23 (65) ◽

pp. 124-135

Author(s):

Imane Guellil ◽

Marcelo Mendoza ◽

Faical Azouaou

Keyword(s):

Sentiment Analysis ◽

Short Term Memory ◽

State Of The Art ◽

Short Term ◽

Term Memory ◽

Ongoing Work ◽

Long Short Term Memory ◽

Large Corpus ◽

Unique Condition

This paper presents an analytic study showing that it is entirely possible to analyze the sentiment of an Arabic dialect without constructing any resources. The idea of this work is to use the resources dedicated to a given dialect \textit{X} for analyzing the sentiment of another dialect \textit{Y}. The unique condition is to have \textit{X} and \textit{Y} in the same category of dialects. We apply this idea on Algerian dialect, which is a Maghrebi Arabic dialect that suffers from limited available tools and other handling resources required for automatic sentiment analysis. To do this analysis, we rely on Maghrebi dialect resources and two manually annotated sentiment corpus for respectively Tunisian and Moroccan dialect. We also use a large corpus for Maghrebi dialect. We use a state-of-the-art system and propose a new deep learning architecture for automatically classify the sentiment of Arabic dialect (Algerian dialect). Experimental results show that F1-score is up to 83% and it is achieved by Multilayer Perceptron (MLP) with Tunisian corpus and with Long short-term memory (LSTM) with the combination of Tunisian and Moroccan. An improvement of 15% compared to its closest competitor was observed through this study. Ongoing work is aimed at manually constructing an annotated sentiment corpus for Algerian dialect and comparing the results

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Selective Corticofugal Modulation on Sound Processing in Auditory Thalamus of Awake Marmosets

10.1101/2021.03.13.435231 ◽

2021 ◽

Author(s):

Yuanqing Zhang ◽

Xiaohui Wang ◽

Lin Zhu ◽

Siyi Bai ◽

Rui Li ◽

...

Keyword(s):

Sensory Processing ◽

Frequency Tuning ◽

Signal To Noise Ratio ◽

Medial Geniculate Body ◽

Primary Auditory Cortex ◽

Medial Geniculate ◽

Auditory Response ◽

Corticofugal Feedback ◽

Corticothalamic Feedback

Cortical feedback has long been considered crucial for modulation of sensory processing. In the mammalian auditory system, studies have suggested that corticofugal feedback can have excitatory, inhibitory, or both effects on the response of subcortical neurons, leading to controversies regarding the role of corticothalamic influence. This has been further complicated by studies conducted under different brain states. In the current study, we used cryo-inactivation in the primary auditory cortex (A1) to examine the role of corticothalamic feedback on medial geniculate body (MGB) neurons in awake marmosets. The primary effects of A1 inactivation were a frequency-specific decrease in the auditory response of MGB neurons coupled with an increased spontaneous firing rate, which together resulted in a decrease in the signal-to-noise ratio. In addition, we report for the first-time that A1 robustly modulated the long-lasting sustained response of MGB neurons which changed the frequency tuning after A1 inactivation, e.g., neurons with sharp tuning increased tuning bandwidth whereas those with broad tuning decreased tuning bandwidth. Taken together, our results demonstrate that corticothalamic modulation in awake marmosets serves to enhance sensory processing in a way similar to center-surround models proposed in visual and somatosensory systems, a finding which supports common principles of corticothalamic processing across sensory systems.

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Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6503 ◽

2020 ◽

Vol 34 (05) ◽

pp. 9571-9578 ◽

Cited By ~ 1

Author(s):

Wei Zhang ◽

Yue Ying ◽

Pan Lu ◽

Hongyuan Zha

Keyword(s):

State Of The Art ◽

Natural Extension ◽

Target Image ◽

Short Term ◽

Image Representations ◽

High Level ◽

Image Descriptions ◽

Shed Light ◽

Image Caption

Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.

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Learning from Interventions Using Hierarchical Policies for Safe Learning

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i06.6602 ◽

2020 ◽

Vol 34 (06) ◽

pp. 10352-10360

Author(s):

Jing Bi ◽

Vikas Dhiman ◽

Tianyou Xiao ◽

Chenliang Xu

Keyword(s):

Reaction Time ◽

State Of The Art ◽

The State ◽

Policy Framework ◽

Asymptotic Performance ◽

Short Term ◽

Learning From Demonstrations ◽

Hierarchical Levels ◽

Long Term Behavior

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.

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Long-term target tracking combined with re-detection

10.21203/rs.3.rs-51036/v3 ◽

2020 ◽

Author(s):

Juanjuan Wang ◽

HaoRan Yang ◽

Ning Xu ◽

Chengqin Wu ◽

ZengShun Zhao ◽

...

Keyword(s):

Correlation Energy ◽

State Of The Art ◽

Tracking Algorithm ◽

Correlation Filters ◽

Short Term ◽

Tracking Method ◽

Tracking Tasks ◽

Svm Model ◽

Confidence Degree

Abstract The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

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Discovery of allenyl acetylene, H2CCCHCCH, in TMC-1

Astronomy and Astrophysics ◽

10.1051/0004-6361/202140482 ◽

2021 ◽

Vol 647 ◽

pp. L3 ◽

Cited By ~ 1

Author(s):

J. Cernicharo ◽

C. Cabezas ◽

M. Agúndez ◽

B. Tercero ◽

N. Marcelino ◽

...

Keyword(s):

Column Density ◽

State Of The Art ◽

Rotational Temperature ◽

Signal To Noise Ratio ◽

Laboratory Data ◽

The Other ◽

Signal To Noise ◽

Chemical Models ◽

Upper Limit

We present the discovery in TMC-1 of allenyl acetylene, H2CCCHCCH, through the observation of nineteen lines with a signal-to-noise ratio ∼4–15. For this species, we derived a rotational temperature of 7 ± 1 K and a column density of 1.2 ± 0.2 × 1013 cm−2. The other well known isomer of this molecule, methyl diacetylene (CH3C4H), has also been observed and we derived a similar rotational temperature, Tr = 7.0 ± 0.3 K, and a column density for its two states (A and E) of 6.5 ± 0.3 × 1012 cm−2. Hence, allenyl acetylene and methyl diacetylene have a similar abundance. Remarkably, their abundances are close to that of vinyl acetylene (CH2CHCCH). We also searched for the other isomer of C5H4, HCCCH2CCH (1.4-Pentadiyne), but only a 3σ upper limit of 2.5 × 1012 cm−2 to the column density can be established. These results have been compared to state-of-the-art chemical models for TMC-1, indicating the important role of these hydrocarbons in its chemistry. The rotational parameters of allenyl acetylene have been improved by fitting the existing laboratory data together with the frequencies of the transitions observed in TMC-1.

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The Order of Information Transfer into Short- Term Memory from Visual Pathways with Different Spatial-Frequency Tunings

Experimental Psychology (Russia) ◽

10.17759/exppsy.2020130206 ◽

2020 ◽

Vol 13 (2) ◽

pp. 72-89

Author(s):

D.S. Alekseeva ◽

V.V. Babenko ◽

D.V. Yavna

Keyword(s):

Spatial Frequency ◽

Information Transfer ◽

Short Term Memory ◽

Frequency Tuning ◽

Visual Pathways ◽

Input Image ◽

Target Face ◽

Short Term ◽

Term Memory ◽

Spatial Frequencies

Visual perceptual representations are formed from the results of processing the input image in parallel pathways with different spatial-frequency tunings. It is known that these representations are created gradually, starting from low spatial frequencies. However, the order of information transfer from the perceptual representation to short-term memory has not yet been determined. The purpose of our study is to determine the principle of entering information of different spatial frequencies in the short-term memory. We used the task of unfamiliar faces matching. Digitized photographs of faces were filtered by six filters with a frequency tuning step of 1 octave. These filters reproduced the spatial-frequency characteristics of the human visual pathways. In the experiment, the target face was shown first. Its duration was variable and limited by a mask. Then four test faces were presented. Their presentation was not limited in time. The observer had to determine the face that corresponds to the target one. The dependence of the accuracy of the solution of the task on the target face duration for different ranges of spatial frequencies was determined. When the target stimuli were unfiltered (broadband) faces, the filtered faces were the test ones, and vice versa. It was found that the short-term memory gets information about an unfamiliar face in a certain order, starting from the medium spatial frequencies, and this sequence does not depend on the processing method (holistic or featural).

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