Reviewerly/exHarmony huggingface dataset:
Artificial intelligence in computational pathology – challenges and future directions

The field of digital histopathology has seen incredible growth in recent years. Digital pathology is becoming a relevant tool in healthcare, industrial and research sectors to reduce the saturation of pathology departments and improve the productivity of pathologists by increasing diagnostic accuracy and reducing turnaround times. Artificial Intelligence (AI) algorithms may be used for the identification of relevant regions, extraction of features from a histological image and overall classification of images into specific classes. The combination of digital histopathology imaging and AI therefore presents a significant opportunity for the support of the pathologists' tasks and opens up a whole new world of computational analysis. In this paper, we have analysed the present, the challenges and the future of the computational pathology discussing the different existing strategies to overcome its main limitations and ensure the computational pathology acceptance. The lack of labelled data, which is the possibly largest challenge for all medical AI applications, is even more pronounced in computational pathology because of the multi-gigapixel nature of the images and high data heterogeneity. We consider the future of the computational pathology is the combination of weak label strategies with active learning and crowdsourcing scenarios since it would remove some of the workload from clinical experts and manual annotation obtaining clinically satisfactory performance with minimal annotation effort. In addition, we believe areas such as explainable AI, data fusion and secure role-based data sharing will be receiving increasing research attention in computational pathology in the close future.
TSFNet: Two-Stage Fusion Network for RGB-T Salient Object Detection

Salient object detection (SOD) based on convolutional neural networks has achieved remarkable success. However, further improving the detection performance on challenging scenes (e.g., low-light scenes) requires additional investigation. Thermal infrared imaging captures thermal radiation from the surface of objects. Thus, it is insensitive to lighting conditions and can provide uniform imaging of objects. Accordingly, we propose a two-stage fusion network (TSFNet) integrating RGB and thermal information for RGB-T SOD. For the first fusion stage, we propose a feature-wise fusion module that captures and aggregates united information and intersecting information in each local region of the RGB and thermal images, and then independent decoding is applied to the RGB and thermal features. For the second fusion stage, we propose a bilateral auxiliary fusion module that extracts auxiliary spatial features from the foreground and background of the thermal and RGB modalities. Finally, we use multiple supervision to further improve the SOD performance. Comprehensive experiments demonstrate that TSFNet outperforms 11 state-of-the-art models under various indicators on three RGB-T SOD datasets.
QUEST: Multi-Purpose Log-Quantized DNN Inference Engine Stacked on 96-MB 3-D SRAM Using Inductive Coupling Technology in 40-nm CMOS

QUEST is a programmable multiple instruction, multiple data (MIMD) parallel accelerator for general-purpose state-of-the-art deep neural networks (DNNs). It features die-to-die stacking with three-cycle latency, 28.8 GB/s, 96 MB, and eight SRAMs using an inductive coupling technology called the ThruChip interface (TCI). By stacking the SRAMs instead of DRAMs, lower memory access latency and simpler hardware are expected. This facilitates in balancing the memory capacity, latency, and bandwidth, all of which are in demand by cutting-edge DNNs at a high level. QUEST also introduces log-quantized programmable bit-precision processing for achieving faster (larger) DNN computation (size) in a 3-D module. It can sustain higher recognition accuracy at a lower bitwidth region compared to linear quantization. The prototype QUEST chip is integrated in the 40-nm CMOS technology, and it achieves 7.49 tera operations per second (TOPS) peak performance in binary precision, and 1.96 TOPS in 4-bit precision at 300-MHz clock.
Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms

Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.
Automatic Needle Segmentation and Localization in MRI With 3-D Convolutional Neural Networks: Application to MRI-Targeted Prostate Biopsy

Image guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperineal MRI-targeted biopsy procedure at our institution. The images were divided into two independent training-validation and test sets at the patient level. A deep 3-D fully convolutional neural network model was developed, trained, and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80-mm average in needle tip detection and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared with the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.
Raptor: A Practical Lattice-Based (Linkable) Ring Signature

We present Raptor, the first practical lattice-based (linkable) ring signature scheme with implementation. Raptor is as fast as classical solutions; while the size of the signature is roughly 1.3 KB per user. Prior to our work, all existing lattice-based solutions are analogues of their discrete-log or pairing-based counterparts. We develop a generic construction of (linkable) ring signatures based on the well-known generic construction from Rivest et al., which is not fully compatible with lattices. Our generic construction is provably secure in random oracle model. We also give instantiations from both standard lattice, as a proof of concept, and NTRU lattice, as an efficient instantiation. We show that the latter construction, called Raptor, is almost as efficient as the classical RST ring signatures and thus may be of practical interest.
Visual Haze Removal by a Unified Generative Adversarial Network

Existence of haze significantly degrades visual quality and hence negatively affects the performance of visual surveillance, video analysis, and human–machine interaction. To remove haze from a visual signal, in this paper, we propose a generative adversarial network for visual haze removal called HRGAN. HRGAN consists of a generator network and a discriminator network. A unified network jointly estimating transmission maps, atmospheric light, and haze-free images (called UNTA) is proposed as the generator network of HRGAN. Instead of being optimized by minimizing the pixel-wise loss, HRGAN is optimized by minimizing a novel loss function consisting of pixel-wise loss, perceptual loss, and adversarial loss produced by a discriminator network. Classical model-based image dehazing algorithms consist of three separate stages: 1) estimating transmission map; 2) estimating atmospheric light; and 3) restoring haze-free image by using an atmospheric scattering model to process the transmission map and atmospheric light. Such a separate scheme is not guaranteed to achieve optimal results. On the contrary, UNTA performs transmission map estimation and atmospheric light estimation simultaneously to obtain joint optimal solutions. The experimental results on both synthetic and real-world image databases demonstrate that HRGAN outperforms the state-of-the-art algorithms in terms of both effectiveness and efficiency.
NeXtVLAD: An Efficient Neural Network to Aggregate Frame-Level Features for Large-Scale Video Classification

This paper introduces a fast and efficient network architecture, NeXtVLAD, to aggregate frame-level features into a compact feature vector for large-scale video classification. Briefly speaking, the basic idea is to decompose a high-dimensional feature into a group of relatively low-dimensional vectors with attention before applying NetVLAD aggregation over time. This NeXtVLAD approach turns out to be both effective and parameter efficient in aggregating temporal information. In the 2nd Youtube-8M video understanding challenge, a single NeXtVLAD model with less than 80M parameters achieves a GAP score of 0.87846 in private leaderboard. A mixture of 3 NeXtVLAD models results in 0.88722, which is ranked 3rd over 394 teams. The code is publicly available at https://github.com/linrongc/youtube-8m .
Universal (meta-)logical reasoning: Recent successes

Classical higher-order logic, when utilized as a meta-logic in which various other (classical and non-classical) logics can be shallowly embedded, is suitable as a foundation for the development of a universal logical reasoning engine.Such an engine may be employed, as already envisioned by Leibniz, to support the rigorous formalisation and deep logical analysis of rational arguments on the computer.A respective universal logical reasoning framework is described in this article and a range of successful first applications in philosophy, artificial intelligence and mathematics are surveyed.
Design of multi-view based email classification for IoT systems via semi-supervised learning

Suspicious emails are one big threat for Internet of Things (IoT) security, which aim to induce users to click and then redirect them to a phishing webpage. To protect IoT systems, email classification is an essential mechanism to classify spam and legitimate emails. In the literature, most email classification approaches adopt supervised learning algorithms that require a large number of labeled data for classifier training. However, data labeling is very time consuming and expensive, making only a very small set of data available in practice, which would greatly degrade the effectiveness of email classification. To mitigate this problem, in this work, we develop an email classification approach based on multi-view disagreement-based semi-supervised learning. The idea behind is that multi-view method can offer richer information for classification, which is often ignored by the literature. The use of semi-supervised learning can help leverage both labeled and unlabeled data. In the evaluation, we investigate the performance of our proposed approach with two datasets and in a real network environment. Experimental results demonstrate that the use of multi-view data can achieve more accurate email classification than the use of single-view data, and that our approach is more effective as compared to several existing similar algorithms.
Seamless Dynamic Adaptive Streaming in LTE/Wi-Fi Integrated Network under Smartphone Resource Constraints

Exploiting both LTE and Wi-Fi links simultaneously enhances the performance of video streaming services in a smartphone. However, it is challenging to achieve seamless and high quality video while saving battery energy and LTE data usage to prolong the usage time of a smartphone. In this paper, we propose REQUEST, a video chunk request policy for Dynamic Adaptive Streaming over HTTP (DASH) in a smartphone, which can utilize both LTE and Wi-Fi. REQUEST enables seamless DASH video streaming with near optimal video quality under given budgets of battery energy and LTE data usage. Through extensive simulation and measurement in a real environment, we demonstrate that REQUEST significantly outperforms other existing schemes in terms of average video bitrate, rebuffering, and resource waste.
A Low Effort Approach to Structured CNN Design Using PCA

Deep learning models hold state of the art performance in many fields, yet their design is still based on heuristics or grid search methods that often result in overparametrized networks. This work proposes a method to analyze a trained network and deduce an optimized, compressed architecture that preserves accuracy while keeping computational costs tractable. Model compression is an active field of research that targets the problem of realizing deep learning models in hardware. However, most pruning methodologies tend to be experimental, requiring large compute and time intensive iterations of retraining the entire network. We introduce structure into model design by proposing a single shot analysis of a trained network that serves as a first order, low effort approach to dimensionality reduction, by using PCA (Principal Component Analysis). The proposed method simultaneously analyzes the activations of each layer and considers the dimensionality of the space described by the filters generating these activations. It optimizes the architecture in terms of number of layers, and number of filters per layer without any iterative retraining procedures, making it a viable, low effort technique to design efficient networks. We demonstrate the proposed methodology on AlexNet and VGG style networks on the CIFAR-10, CIFAR-100 and ImageNet datasets, and successfully achieve an optimized architecture with a reduction of up to 3.8X and 9X in the number of operations and parameters respectively, while trading off less than 1% accuracy. We also apply the method to MobileNet, and achieve 1.7X and 3.9X reduction in the number of operations and parameters respectively, while improving accuracy by almost one percentage point.
Multiview Latent Space Learning With Feature Redundancy Minimization

Multiview learning has received extensive research interest and has demonstrated promising results in recent years. Despite the progress made, there are two significant challenges within multiview learning. First, some of the existing methods directly use original features to reconstruct data points without considering the issue of feature redundancy. Second, existing methods cannot fully exploit the complementary information across multiple views and meanwhile preserve the view-specific properties; therefore, the degraded learning performance will be generated. To address the above issues, we propose a novel multiview latent space learning framework with feature redundancy minimization. We aim to learn a latent space to mitigate the feature redundancy and use the learned representation to reconstruct every original data point. More specifically, we first project the original features from multiple views onto a latent space, and then learn a shared dictionary and view-specific dictionaries to, respectively, exploit the correlations across multiple views as well as preserve the view-specific properties. Furthermore, the Hilbert-Schmidt independence criterion is adopted as a diversity constraint to explore the complementarity of multiview representations, which further ensures the diversity from multiple views and preserves the local structure of the data in each view. Experimental results on six public datasets have demonstrated the effectiveness of our multiview learning approach against other state-of-the-art methods.
Group Maximum Differentiation Competition: Model Comparison with Few Samples

In many science and engineering fields that require computational models to predict certain physical quantities, we are often faced with the selection of the best model under the constraint that only a small sample set can be physically measured. One such example is the prediction of human perception of visual quality, where sample images live in a high dimensional space with enormous content variations. We propose a new methodology for model comparison named group maximum differentiation (gMAD) competition. Given multiple computational models, gMAD maximizes the chances of falsifying a "defender" model using the rest models as "attackers". It exploits the sample space to find sample pairs that maximally differentiate the attackers while holding the defender fixed. Based on the results of the attacking-defending game, we introduce two measures, aggressiveness and resistance, to summarize the performance of each model at attacking other models and defending attacks from other models, respectively. We demonstrate the gMAD competition using three examples-image quality, image aesthetics, and streaming video quality-of-experience. Although these examples focus on visually discriminable quantities, the gMAD methodology can be extended to many other fields, and is especially useful when the sample space is large, the physical measurement is expensive and the cost of computational prediction is low.
An Outlook for Quantum Computing [Point of View]

We have ubiquitous presence of computers today, ranging from simple controllers in modern appliances to smartphones in our pockets that provide a wide range of everyday services, to powerful supercomputers and large data centers that carry out the most computationally intensive tasks. These computational machines have a few things in common: for example, the information they handle is stored in bits (0 or 1), and the procedure for processing the information is specified by a program. A great deal is known about the limits of what such computational machines can and cannot do efficiently. There are many important computational problems that are believed to be very difficult to solve using even the most powerful computers, where the resource requirement-whether it is the size of the machine or the time it takes to finish the task-increases exponentially as a function of the problem size.
A General View for Network Embedding as Matrix Factorization

We propose a general view that demonstrates the relationship between network embedding approaches and matrix factorization. Unlike previous works that present the equivalence for the approaches from a skip-gram model perspective, we provide a more fundamental connection from an optimization (objective function) perspective. We demonstrate that matrix factorization is equivalent to optimizing two objectives: one is for bringing together the embeddings of similar nodes; the other is for separating the embeddings of distant nodes. The matrix to be factorized has a general form: S-β. The elements of $\mathbfS $ indicate pairwise node similarities. They can be based on any user-defined similarity/distance measure or learned from random walks on networks. The shift number β is related to a parameter that balances the two objectives. More importantly, the resulting embeddings are sensitive to β and we can improve the embeddings by tuning β. Experiments show that matrix factorization based on a new proposed similarity measure and β-tuning strategy significantly outperforms existing matrix factorization approaches on a range of benchmark networks.
Kernel-Based Distance Metric Learning for Supervised $k$ -Means Clustering

Finding an appropriate distance metric that accurately reflects the (dis)similarity between examples is a key to the success of k -means clustering. While it is not always an easy task to specify a good distance metric, we can try to learn one based on prior knowledge from some available clustered data sets, an approach that is referred to as supervised clustering. In this paper, a kernel-based distance metric learning method is developed to improve the practical use of k -means clustering. Given the corresponding optimization problem, we derive a meaningful Lagrange dual formulation and introduce an efficient algorithm in order to reduce the training complexity. Our formulation is simple to implement, allowing a large-scale distance metric learning problem to be solved in a computationally tractable way. Experimental results show that the proposed method yields more robust and better performances on synthetic as well as real-world data sets compared to other state-of-the-art distance metric learning methods.
Path Planning and Control of Mobile Robot in Road Environments Using Sensor Fusion and Active Force Control

A path planning and control approach of a non-holonomic three-wheeled mobile robot (WMR) for online navigation in road following and roundabout environments is presented in this paper. We proposed a complete navigation algorithm that enables the WMR to autonomously navigate on the road with various scenarios. With such an algorithm, the robot is able to localize itself within the road environment and find a collision free-path starting from a pre-defined start position to a goal point using a novel approach called laser simulator (LS). The path planning and roundabout detection are determined based on LS and sensor fusion of a laser range finder, camera, and odometry measurements. The sensor fusion algorithm is used to remove noises and uncertainties from sensors' data and provide optimum measurements for path planning. A robot motion control scheme is used for the purpose of controlling the kinematic parameters of WMR using a resolved acceleration control coupled with an active force control for rejecting the disturbances. Experimental results show the capability of the proposed algorithms to robustly drive the robot on the road following and roundabout environments.
Atom-only descriptions of the driven-dissipative Dicke model

We investigate how to describe the dissipative spin dynamics of the driven-dissipative Dicke model, describing $N$ two-level atoms coupled to a cavity mode, after adiabatic elimination of the cavity mode. To this end, we derive a Redfield master equation which goes beyond the standard secular approximation and large detuning limits. We show that the secular (or rotating wave) approximation and the large detuning approximation both lead to inadequate master equations, that fail to predict the Dicke transition or the damping rates of the atomic dynamics. In contrast, the full Redfield theory correctly predicts the phase transition and the effective atomic damping rates. Our work provides a reliable framework to study the full quantum dynamics of atoms in a multimode cavity, where a quantum description of the full model becomes intractable.