Reviewerly/exHarmony huggingface dataset:
Solving integral equations in free space with inverse-designed ultrathin optical metagratings

As standard microelectronic technology approaches fundamental limitations in speed and power consumption, novel computing strategies are strongly needed. Analogue optical computing enables the processing of large amounts of data at a negligible energy cost and high speeds. Based on these principles, ultrathin optical metasurfaces have been recently explored to process large images in real time, in particular for edge detection. By incorporating feedback, it has also recently been shown that metamaterials can be tailored to solve complex mathematical problems in the analogue domain, although these efforts have so far been limited to guided-wave systems and bulky set-ups. Here, we present an ultrathin Si metasurface-based platform for analogue computing that is able to solve Fredholm integral equations of the second kind using free-space visible radiation. A Si-based metagrating was inverse-designed to implement the scattering matrix synthesizing a prescribed kernel corresponding to the mathematical problem of interest. Next, a semitransparent mirror was incorporated into the sample to provide adequate feedback and thus perform the required Neumann series, solving the corresponding equation in the analogue domain at the speed of light. Visible wavelength operation enables a highly compact, ultrathin device that can be interrogated from free space, implying high processing speeds and the possibility of on-chip integration.
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers

Most polyp segmentation methods use convolutional neural networks (CNNs) as their backbone, leading to two key issues when exchanging information between the encoder and decoder: (1) taking into account the differences in contribution between differentlevel features, and (2) designing an effective mechanism for fusing these features.Unlike existing CNN-based methods, we adopt a transformer encoder, which learns more powerful and robust representations.In addition, considering the image acquisition influence and elusive properties of polyps, we introduce three standard modules, including a cascaded fusion module (CFM), a camouflage identification module (CIM), and a similarity aggregation module (SAM).Among these, the CFM is used to collect the semantic and location information of polyps from high-level features; the CIM is applied to capture polyp information disguised in low-level features, and the SAM extends the pixel features of the polyp area with high-level semantic position information to the entire polyp area, thereby effectively fusing cross-level features.The proposed model, named Polyp-PVT, effectively suppresses noises in the features and significantly improves their expressive capabilities.Extensive experiments on five widely adopted datasets show that the proposed model is more robust to various challenging situations (e.g., appearance changes, small objects, and rotation) than existing representative methods.The proposed model is available at https://github.com/
An Exploratory Framework for Cyclone Identification and Tracking

Analyzing depressions plays an important role in meteorology, especially in the study of cyclones. In particular, the study of the temporal evolution of cyclones requires a robust depression tracking framework. To cope with this demand we propose a pipeline for the exploration of cyclones and their temporal evolution. This entails a generic framework for their identification and tracking. The fact that depressions and cyclones are not well-defined objects and their shape and size characteristics change over time makes this task especially challenging. Our method combines the robustness of topological approaches and the detailed tracking information from optical flow analysis. At first cyclones are identified within each time step based on well-established topological concepts. Then candidate tracks are computed from an optical flow field. These tracks are clustered within a moving time window to distill dominant coherent cyclone movements, which are then forwarded to a final tracking step. In contrast to previous methods our method requires only a few intuitive parameters. An integration into an exploratory framework helps in the study of cyclone movement by identifying smooth, representative tracks. Multiple case studies demonstrate the effectiveness of the method in tracking cyclones, both in the northern and southern hemisphere.
Abstracting algebraic effects

Proposed originally by Plotkin and Pretnar, algebraic effects and their handlers are a leading-edge approach to computational effects: exceptions, mutable state, nondeterminism, and such. Appreciated for their elegance and expressiveness, they are now progressing into mainstream functional programming languages. In this paper, we introduce and examine programming language constructs that back adoption of programming with algebraic effects on a larger scale in a modular fashion by providing mechanisms for abstraction. We propose two such mechanisms: existential effects (which hide the details of a particular effect from the user) and local effects (which guarantee that no code coming from the outside can interfere with a given effect). The main technical difficulty arises from the dynamic nature of coupling an effectful operation with the right handler during execution, but, as we show in this paper, a carefully designed type system can ensure that this will not break the abstraction. Our main contribution is a novel calculus for algebraic effects and handlers, called λ HEL , equipped with local and existential algebraic effects, in which the dynamic nature of handlers is kept in check by typed runtime coercions. As a proof of concept, we present an experimental programming language based on our calculus, which provides strong abstraction mechanisms via an ML-style module system.
Toward Resolution-Invariant Person Reidentification via Projective Dictionary Learning

Person reidentification (ReID) has recently been widely investigated for its vital role in surveillance and forensics applications. This paper addresses the low-resolution (LR) person ReID problem, which is of great practical meaning because pedestrians are often captured in LRs by surveillance cameras. Existing methods cope with this problem via some complicated and time-consuming strategies, making them less favorable, in practice, and meanwhile, their performances are far from satisfactory. Instead, we solve this problem by developing a discriminative semicoupled projective dictionary learning (DSPDL) model, which adopts the efficient projective dictionary learning strategy, and jointly learns a pair of dictionaries and a mapping function to model the correspondence of the cross-view data. A parameterless cross-view graph regularizer incorporating both positive and negative pair information is designed to enhance the discriminability of the dictionaries. Another weakness of existing approaches to this problem is that they are only applicable for the scenario where the cross-camera image sets have a globally uniform resolution gap. This fact undermines their practicality because the resolution gaps between cross-camera images often vary person by person in practice. To overcome this hurdle, we extend the proposed DSPDL model to the variational resolution gap scenario, basically by learning multiple pairs of dictionaries and multiple mapping functions. A novel technique is proposed to rerank and fuse the results obtained from all dictionary pairs. Experiments on five public data sets show the proposed method achieves superior performances to the state-of-the-art ones.
The Termination Critic

In this work, we consider the problem of autonomously discovering behavioral abstractions, or options, for reinforcement learning agents. We propose an algorithm that focuses on the termination function, as opposed to - as is common - the policy. The termination function is usually trained to optimize a control objective: an option ought to terminate if another has better value. We offer a different, information-theoretic perspective, and propose that terminations should focus instead on the compressibility of the option’s encoding - arguably a key reason for using abstractions.To achieve this algorithmically, we leverage the classical options framework, and learn the option transition model as a “critic” for the termination function. Using this model, we derive gradients that optimize the desired criteria. We show that the resulting options are non-trivial, intuitively meaningful, and useful for learning.
Polynomial Multiplication over Finite Fields in Time \( O(n \log n \)

Assuming a widely believed hypothesis concerning the least prime in an arithmetic progression, we show that polynomials of degree less than \( n \) over a finite field \( \mathbb {F}_q \) with \( q \) elements can be multiplied in time \( O (n \log q \log (n \log q)) \) , uniformly in \( q \) . Under the same hypothesis, we show how to multiply two \( n \) -bit integers in time \( O (n \log n) \) ; this algorithm is somewhat simpler than the unconditional algorithm from the companion paper [ 22 ]. Our results hold in the Turing machine model with a finite number of tapes.
Rain O’er Me: Synthesizing Real Rain to Derain With Data Distillation

We present a supervised technique for learning to remove rain from images without using synthetic rain software.The method is based on a two-stage data distillation approach: 1) A rainy image is first paired with a coarsely derained version using on a simple filtering technique ("rain-to-clean").2) Then a clean image is randomly matched with the rainy soft-labeled pair.Through a shared deep neural network, the rain that is removed from the first image is then added to the clean image to generate a second pair ("clean-to-rain").The neural network simultaneously learns to map both images such that high resolution structure in the clean images can inform the deraining of the rainy images.Demonstrations show that this approach can address those visual characteristics of rain not easily synthesized by software in the usual way.
Adaptive Approximation and Estimation of Deep Neural Network to Intrinsic Dimensionality

We theoretically prove that the generalization performance of deep neural networks (DNNs) is mainly determined by an intrinsic low-dimensional structure of data. Recently, DNNs empirically provide outstanding performance in various machine learning applications. Motivated by the success, theoretical properties of DNNs (e.g. a generalization error) are actively investigated by numerous studies toward understanding their mechanism. Especially, how DNNs behave with high-dimensional data is one of the most important concerns. However, the problem is not sufficiently investigated from an aspect of characteristics of data, despite it is frequently observed that high-dimensional data have an intrinsic low-dimensionality in practice. In this paper, to clarify a connection between DNNs and such the data, we derive bounds for approximation and generalization errors by DNNs with intrinsic low-dimensional data. To the end, we introduce a general notion of an intrinsic dimension and develop a novel proof technique to evaluate the errors. Consequently, we show that convergence rates of the errors by DNNs do not depend on the nominal high-dimensionality of data, but depend on the lower intrinsic dimension. We also show that the rate is optimal in the minimax sense. Furthermore, we find that DNNs with increasing layers can handle a broader class of intrinsic low-dimensional data. We conduct a numerical simulation to validate (i) the intrinsic dimension of data affects the generalization error of DNNs, and (ii) DNNs outperform other non-parametric estimators which are also adaptive to the intrinsic dimension.
Learning to fly

Hybrid unmanned aerial vehicles (UAV) combine advantages of multicopters and fixed-wing planes: vertical take-off, landing, and low energy use. However, hybrid UAVs are rarely used because controller design is challenging due to its complex, mixed dynamics. In this paper, we propose a method to automate this design process by training a mode-free, model-agnostic neural network controller for hybrid UAVs. We present a neural network controller design with a novel error convolution input trained by reinforcement learning. Our controller exhibits two key features: First, it does not distinguish among flying modes, and the same controller structure can be used for copters with various dynamics. Second, our controller works for real models without any additional parameter tuning process, closing the gap between virtual simulation and real fabrication. We demonstrate the efficacy of the proposed controller both in simulation and in our custom-built hybrid UAVs (Figure 1, 8). The experiments show that the controller is robust to exploit the complex dynamics when both rotors and wings are active in flight tests.
Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation

Photovoltaic (PV) power generation is highly intermittent in nature and any accurate very short-term prediction can decrease the impact of its uncertainties and operation costs and boost the reliable and efficient integration of PV systems into micro/smart grids.This work develops a new generalized technique for very short-term prediction of PV power generation from the lagged power generation data using fuzzy techniques.A preprocessor extracts relevant statistical features from the PV data which are fed to the fuzzy predictor.A modified version of Wang-Mendel training algorithm is employed to directly extract the fuzzy rules from the training data pairs.This methodology exploits the limited training data more efficiently.In addition, an online additive learning routine is proposed, which enables the predictor to learn from new data while running the predictions.So, the prediction accuracy increases over time and the predictor updates to account for long-term changing conditions of weather and PV system performance and its surroundings.Numerical results of the comparison of the proposed approach with simple fuzzy and traditional artificial neural network methods on a live PV system in the United Kingdom demonstrate its improved prediction accuracy, outperforming the benchmark approaches with a normalized mean absolute error (NMAE) of 3.6%.
Efficient Video Generation on Complex Datasets.

Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work. Our proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator. We evaluate on the related tasks of video synthesis and video prediction, and achieve new state-of-the-art Frechet Inception Distance for prediction for Kinetics-600, as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset, alongside establishing a strong baseline for synthesis on Kinetics-600.
Haze Removal Algorithm for Optical Remote Sensing Image Based on Multi-Scale Model and Histogram Characteristic

Optical imaging remote sensing technology is an important technical means to obtain information of ground objects, but it is restricted by bad weather such as clouds, rain, and haze.In haze weather condition, optical images often have poor contrast and blurred details, which has a great impact on subsequent applications and interpretation.If the acquired images are processed, not only their quality can be improved, but also their visual effect and utilization value can be improved, so as to reduce the impact brought by haze.In general, the haze is removed from the view of image processing.In this paper, through in-depth analysis and study of existing algorithms and characteristics of optical remote sensing images, a new idea is proposed to solve this problem from the view of combination of image content and auxiliary information.Furthermore, a new haze removal algorithm is proposed based on the Retinex multi-scale model and the histogram characteristics of remote sensing images.Because the new method combines multi-scale model (MSM) and histogram characteristics (HC), it is referred to as MSMHC algorithm in this paper.The advantage of the new method is that the content and type of image are considered in the whole process, and then two processing schemes are set for haze removal.In the test experiments, one hundred groups of image data were used to carry out comparative experiments.At the same time, single-scale Retinex (SSR) algorithm, multi-scale Retinex (MSR) algorithm, dark channel priori (DCP) method, brightness preserving dynamic fuzzy histogram equalization (BPDFHE) algorithm, histogram equalization (HE) method, and homomorphic filter (HF) algorithm were used for comparative experiments with the MSMHC method.Five parameters, including standard deviation (SD), information entropy (IE), peak signal to noise ratio (PSNR), structural similarity (SSIM), and image contrast (IC), were used to quantitatively evaluate the test results.The experimental results and the parameter values showed that the MSMHC algorithm could not only effectively remove haze from remote sensing images, obtain high contrast and high definition images, but also have better generalization ability.
Multi-agent Attentional Activity Recognition

Multi-modality is an important feature of sensor based activity recognition. In this work, we consider two inherent characteristics of human activities, the spatially-temporally varying salience of features and the relations between activities and corresponding body part motions. Based on these, we propose a multi-agent spatial-temporal attention model. The spatial-temporal attention mechanism helps intelligently select informative modalities and their active periods. And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions. With a joint recognition goal, the agents share gained information and coordinate their selection policies to learn the optimal recognition model. The experimental results on four real-world datasets demonstrate that the proposed model outperforms the state-of-the-art methods.
Overview of CLEF 2019 Lab ProtestNews: Extracting Protests from News in a Cross-Context Setting

We present an overview of the CLEF-2019 Lab ProtestNews on Extracting Protests from News in the context of generalizable natural language processing. The lab consists of document, sentence, and token level information classification and extraction tasks that were referred as task 1, task 2, and task 3 respectively in the scope of this lab. The tasks required the participants to identify protest relevant information from English local news at one or more aforementioned levels in a cross-context setting, which is cross-country in the scope of this lab. The training and development data were collected from India and test data was collected from India and China. The lab attracted 58 teams to participate in the lab. 12 and 9 of these teams submitted results and working notes respectively. We have observed neural networks yield the best results and the performance drops significantly for majority of the submissions in the cross-country setting, which is China.
CancelOut: A Layer for Feature Selection in Deep Neural Networks

Feature ranking (FR) and feature selection (FS) are crucial steps in data preprocessing; they can be used to avoid the curse of dimensionality problem, reduce training time, and enhance the performance of a machine learning model. In this paper, we propose a new layer for deep neural networks - CancelOut, which can be utilized for FR and FS tasks, for supervised and unsupervised learning. Empirical results show that the proposed method can find feature subsets that are superior to traditional feature analysis techniques. Furthermore, the layer is easy to use and requires adding only a few additional lines of code to a deep learning training loop. We implemented the proposed method using the PyTorch framework and published it online (The code is available at: www.github.com/unnir/CancelOut ).
Deep Neural Network based Polyp Segmentation in Colonoscopy Images using a Combination of Color Spaces

Colorectal cancer (CRC) is the second leading cause of cancer death. Colorectal polyps cause most colorectal cancer cases. Colonoscopy is considered as the most common method for diagnosis of colorectal polyps, and early detection and segmentation of them can prevent colorectal cancer. On the other hand, today advances in computer systems persuade researchers all around the world to use computer-aided systems to help physicians in their diagnosis. Many modern types of researches and methods have proposed for this goal, and we have aggregated the methods based on previous convolutional neural networks with more recent networks in this paper to improve the quality of segmentation. We also chose the red channel, green channel and the b* component of CIE-L*a*b* as the input of network to leverage the parameters of segmentation result such as dice and sensitivity. LinkNet is used as the convolutional network, and the results show that it is suitable for segmentation. Performance of our method is evaluated on CVC-ColonDB. The results show that our method outperforms previous works in colorectal polyp segmentation field.
Aggression Detection on Multilingual Social Media Text

With the advancement of technology, social media such as Facebook, Twitter, etc. plays an important role in communication whether it is texting, sharing photos, audio-video calls or expressing views through comments. Along with these advantages, it has some negative sides as well which brings aggression towards some section of people. Such aggression, hatred in social media needs to be detected and prevented automatically which is the main objective of our work. We have worked on Hindi, English and Hindi-English (code-mixed) datasets. We used features like word vectors, aggressive words (manually created dictionary), sentiment scores, parts of speech and emojis for the classification task. We experimented with several machine learning and deep learning models and the results indicate that XGBoost Classifier, Gradient Boosting Classifier (GBM) and Support Vector Machine (SVM) are most suited for the task. Therefore the output of the three classifiers were used for majority voting which provides f-scores of 68.13, 54.82 and 55.31 for the English, Hindi and code-mixed datasets respectively.
UAV Positioning Based on Multi-Sensor Fusion

Real-time and stable positioning data is essential for the UAV to perform various tasks. The traditional multi-sensor data fusion algorithm needs to know the measurement noise of sensor data, and even if there are corresponding adaptive methods to estimate the noise, most methods cannot deal with time-varying noise. In addition, traditional fusion algorithms usually are complicated, causing a large amount of calculation. In this paper, a multi-sliding window classification adaptive unscented Kalman filter (MWCAUKF) method with timestamp sort updating was proposed, which can improve the accuracy and stability of positioning. This method consists of three phases. First, according to the timestamp of sensor data, the multi-sensor data are added with fusion filtering in order. Then it estimates the measurement noise of multiple sensors through multiple sliding Windows. Finally, the sensor data classification method is adopted to deal with the filter instability caused by time-varying noise. Both theoretical analysis and experimental results show that this method has a low computational cost, high accuracy, and good stability.