Salient Object Detection via Integrity Learning

Albeit current salient object detection (SOD) works have achieved fantastic progress, they are cast into the shade when it comes to the integrity of the predicted salient regions. We define the concept of integrity at both the micro and macro level. Specifically, at the micro level, the model should highlight all parts that belong to a certain salient object, while at the macro level, the model needs to discover all salient objects from the given image scene. To facilitate integrity learning for salient object detection, we design a novel Integrity Cognition Network (ICON), which explores three important components to learn strong integrity features. 1) Unlike the existing models that focus more on feature discriminability, we introduce a diverse feature aggregation (DFA) component to aggregate features with various receptive fields (i.e.,, kernel shape and context) and increase the feature diversity. Such diversity is the foundation for mining the integral salient objects. 2) Based on the DFA features, we introduce the integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects at the macro level, while suppressing the other distracting ones. 3) After extracting the enhanced features, the part-whole verification (PWV) method is employed to determine whether the part and whole object features have strong agreement. Such part-whole agreements can further improve the micro-level integrity for each salient object. To demonstrate the effectiveness of ICON, comprehensive experiments are conducted on seven challenging benchmarks, where promising results are achieved

Learning to Identify Critical States for Reinforcement Learning from Videos

Recent work on deep reinforcement learning (DRL) has pointed out that algorithmic information about good policies can be extracted from offline data which lack explicit information about executed actions. For example, videos of humans or robots may convey a lot of implicit information about rewarding action sequences, but a DRL machine that wants to profit from watching such videos must first learn by itself to identify and recognize relevant states/actions/rewards. Without relying on ground-truth annotations, our new method called Deep State Identifier learns to predict returns from episodes encoded as videos. Then it uses a kind of mask-based sensitivity analysis to extract/identify important critical states. Extensive experiments showcase our method's potential for understanding and improving agent behavior. The source code and the generated datasets are available at https://github.com/AI-Initiative-KAUST/VideoRLCS.Comment: This paper was accepted to ICCV2

A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering. This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare. The review begins by visualizing a roadmap of machine learning and its applications in healthcare. And then it explores the integration of RL with NLP tasks. We examined dialogue systems where RL enables the learning of conversational strategies, RL-based machine translation models, question-answering systems, text summarization, and information extraction. Additionally, ethical considerations and biases in RL-NLP systems are addressed

Quadratic Soliton Frequency Comb at 4 µm from an OP-GaP-based Optical Parametric Oscillator

We report generation of quadratic solitons, i.e. temporal simultons, in an OP-GaP based halfharmonic optical parametric oscillator. We achieve 4-µm pulses with sech² spectrum of 790nm FWHM bandwidth, 197% slope efficiency, and 38% conversion efficiency

Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data

Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-the-art data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process

Multi-wavelength 128 Gbit s−1 λ−1 PAM4 optical transmission enabled by a 100 GHz quantum dot mode-locked optical frequency comb

Semiconductor mode-locked lasers (MLLs) with extremely high repetition rates are promising optical frequency comb (OFC) sources for their usage as compact, high-efficiency, and low-cost light sources in high-speed dense wavelength-division multiplexing transmissions. The fully exploited conventional C- and L- bands require the research on O-band to fulfil the transmission capacity of the current photonic networks. In this work, we present a passive two-section InAs/InGaAs quantum-dot (QD) MLL-based OFC with a fundamental repetition rate of ∼100 GHz operating at O-band wavelength range. The specially designed device favours the generation of nearly Fourier-transform-limited pulses in the entire test range by only pumping the gain section while with the absorber unbiased. The typical integrated relative intensity noise of the whole spectrum and a single tone are −152 and −137 dB Hz−1 in the range of 100 MHz–10 GHz, respectively. Back-to-back data transmissions for seven selected tones have been realised by employing a 64 Gbaud four-level pulse amplitude modulation format. The demonstrated performance shows the feasibility of the InAs QD MLLs as a simple structure, easy operation, and low power consumption OFC sources for high-speed fibre-optic communications

Nonlinear Enhancement of Optical Spectroscopy in the Mid-infrared

Optical spectroscopy has long been a cornerstone in studying material properties, playing a pivotal role in the advancement of science and technology. It remains crucial in both research and industry, particularly in the mid-infrared (MIR) region, known for its unique molecular fingerprint capabilities. The emergence of optical frequency comb technology has set the stage for dual-comb spectroscopy (DCS) to revolutionize optical spectroscopy with its potential superiority in speed, resolution, sensitivity, precision, and compactness. However, practical implementation of DCS in the MIR region faces challenges due to its demanding requirements for sources, inefficient photodetection, and dynamic range limitations, despite an exciting prospect. This dissertation explores the use of quadratic optical nonlinearity to tackle these challenges. By manipulating energy and information flows between photons of different frequencies through nonlinear optics, we leverage well-developed near-infrared (NIR) sources, detectors, and optics to address difficulties in the MIR region. We first demonstrate optical parametric oscillators in the regime of simulton (quadratic soliton pair), achieving a high-power broadband MIR frequency comb with a remarkably high NIR-to-MIR power conversion efficiency. We also introduce cross-comb spectroscopy (CCS), which upconverts the MIR frequency comb to the NIR region and allows MIR spectral analysis with NIR photodetection. This novel approach can offer superior signal-to-noise ratio (SNR), dynamic range, and detection efficiency compared to conventional DCS, while providing wavelength flexibility. Additionally, we present a new method to facilitate the detection of trace samples with short-pulse optical parametric amplifiers, which can significantly enhance SNR and limit of detection of existing methods. Overall, this research demonstrates the capabilities of quadratic nonlinearity in enabling high-performance optical sensing in spectral regions where sources, detectors, and optics are less developed

Topical development, relevance and organisation in expository writting

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