Reviewerly/exHarmony huggingface dataset:
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes

There has recently been much work on the of neural networks, where Bayesian neural networks (BNNs) are shown to converge to a Gaussian (GP) as all hidden layers are sent to infinite width. However, these results do not apply to architectures that require one or more of the hidden layers to remain narrow. In this paper, we consider the wide limit of BNNs where some hidden layers, called bottlenecks, are held at finite width. The result is a composition of GPs that we term a neural network Gaussian process (bottleneck NNGP). Although intuitive, the subtlety of the proof is in showing that the wide limit of a composition of networks is in fact the composition of the limiting GPs. We also analyze theoretically a single-bottleneck NNGP, finding that the bottleneck induces dependence between the outputs of a multi-output network that persists through extreme post-bottleneck depths, and prevents the kernel of the network from losing discriminative power at extreme post-bottleneck depths.
Global Linear Convergence of Evolution Strategies on More than Smooth Strongly Convex Functions

Evolution strategies (ESs) are zeroth-order stochastic black-box optimization heuristics invariant to monotonic transformations of the objective function. They evolve a multivariate normal distribution, from which candidate solutions are generated. Among different variants, CMA-ES is nowadays recognized as one of the state-of-the-art zeroth-order optimizers for difficult problems. Despite ample empirical evidence that ESs with a step-size control mechanism converge linearly, theoretical guarantees of linear convergence of ESs have been established only on limited classes of functions. In particular, theoretical results on convex functions are missing, where zeroth-order and also first-order optimization methods are often analyzed. In this paper, we establish almost sure linear convergence and a bound on the expected hitting time of an ES family, namely, the $(1+1)_\kappa$-ES, which includes the (1+1)-ES with (generalized) one-fifth success rule and an abstract covariance matrix adaptation with bounded condition number, on a broad class of functions. The analysis holds for monotonic transformations of positively homogeneous functions and of quadratically bounded functions, the latter of which particularly includes monotonic transformation of strongly convex functions with Lipschitz continuous gradient. As far as the authors know, this is the first work that proves linear convergence of ES on such a broad class of functions.
UltraGAN: Ultrasound Enhancement Through Adversarial Generation

Ultrasound images are used for a wide variety of medical purposes because of their capacity to study moving structures in real time. However, the quality of ultrasound images is significantly affected by external factors limiting interpretability. We present UltraGAN, a novel method for ultrasound enhancement that transfers quality details while preserving structural information. UltraGAN incorporates frequency loss functions and an anatomical coherence constraint to perform quality enhancement. We show improvement in image quality without sacrificing anatomical consistency. We validate UltraGAN on a publicly available dataset for echocardiography segmentation and demonstrate that our quality-enhanced images are able to improve downstream tasks. To ensure reproducibility we provide our source code and training models.
Temporal context guides visual exploration during scene recognition.

Memories for episodes are temporally structured. Cognitive models derived from list-learning experiments attribute this structure to the retrieval of temporal context information that indicates when a memory occurred. These models predict key features of memory recall, such as the strong tendency to retrieve studied items in the order in which they were first encountered. Can such models explain ecological memory behaviors, such as eye movements during encoding and retrieval of complex visual stimuli? We tested predictions from retrieved-context models using three data sets involving recognition memory and free viewing of complex scenes. Subjects reinstated sequences of eye movements from one scene-viewing episode to the next. Moreover, sequence reinstatement decayed over time and was associated with successful memory. We observed memory-driven reinstatement even after accounting for intrinsic scene properties that produced consistent eye movements. These findings confirm predictions of retrieved-context models, suggesting retrieval of temporal context influences complex behaviors generated during naturalistic memory experiences. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Exact equations to measure highly aberrated wavefronts with the Hartmann test

An exact vector expression for the deformations of a wavefront from any chosen reference surface, as a function of the directions of the real and reference rays, is deduced. It can be used with slope measuring test methods, such as Hartmann or Ronchi tests, but the need for a spherical reference is removed. We present simulated and experimental results to show the feasibility of this proposal.
Verifying Individual Fairness in Machine Learning Models

We consider the problem of whether a given decision model, working with structured data, has individual fairness. Following the work of Dwork, a model is individually biased (or unfair) if there is a pair of valid inputs which are close to each other (according to an appropriate metric) but are treated differently by the model (different class label, or large difference in output), and it is unbiased (or fair) if no such pair exists. Our objective is to construct verifiers for proving individual fairness of a given model, and we do so by considering appropriate relaxations of the problem. We construct verifiers which are sound but not complete for linear classifiers, and kernelized polynomial/radial basis function classifiers. We also report the experimental results of evaluating our proposed algorithms on publicly available datasets.
An Autonomous Intercept Drone with Image-based Visual Servo

For most people on the ground, facing an unwanted drone buzzing around overhead, there is not a lot that we can do, especially if it is out of gun (radio wave gun or shotgun) range. A solution to this is to use intercept drones that seek out and bring down other drones. In order to make the interception autonomous, an image-based visual servo algorithm is designed with a forward-looking monocular camera. The control command, namely the angular velocity and thrust, is generated for intercept drones to implement accurate and fast interception. The proposed method is demonstrated in both hardware-in-the-loop simulation and demonstrative flight experiments.
Natural speckle-based watermarking with random-like illuminated decoding

We propose an optical watermarking method based on a natural speckle pattern. In the watermarking process, the watermark information is embedded into the natural speckle pattern. Then the random-like watermarked image is generated with the proposed grayscale reordering algorithm. During the extraction procedure, the watermarked image is projected to the natural speckle pattern as illumination. Subsequently, they are incoherently superimposed to extract the watermark information directly by human vision. Optical experiments and a hypothesis test are conducted to demonstrate the proposed method with high reliability, imperceptibility and robustness. The proposed method is the first watermarking method utilizing the natural diffuser as the core element in encoding and decoding.
The Prince’s tears, a large cable-driven parallel robot for an artistic exhibition

This paper presents the development and results of a large 3 d.o.f cable-driven parallel robot (CDPR) that has been extensively used between June and August 2019 for an artistic exhibition. The purpose of the exhibition was to 3D print a wall of glass powder, which will slowly collapse after the deposit of each layer. Positioning control on the assigned trajectory was an issue because of the CDPR geometry imposed by the specific configuration of the exhibition place. We describe how this problem was solved using a combination of cable length estimation based on the winch rotation measured by encoder, together with 3 on-board lidars that were used to provide a measure of the robot position. To the best of our knowledge this is the first time that such method was used for controlling a large CDPR. This CDPR has run for 174 hours since 6/18/2019, averaging a run time of 4h15mn per day. The 3D printing of the wall started on 7/18/2019 and stops on 8/31/2019. During this period the robot was used for 32 days with an average of 2h18mn run-time per day. The robot has traveled on a total distance of 4757 meters, of which 3893 meters on the assigned trajectory. During the period 76 layers have been deposited, representing a mass of 1.5 tons of glass powder.
Control of interference and diffraction of a three-level atom in a double-slit scheme with cavity fields

A double-cavity with a quantum mechanical and a classical field is placed immediately behind a double-slit in order to analyze the wave-particle duality. Both fields have common nodes and antinodes through which a three-level atom passes after crossing the double-slit. The atom-field interaction is maximum when the atom crosses a common antinode and path information can be recorded on the phase of the quantum field. On the other hand, if the atom crosses a common node, the interaction is null and no path information is stored. A quadrature measurement on the quantum field can reveal the path followed by the atom, depending on its initial amplitude $\ensuremath{\alpha}$ and the classical amplitude $\ensuremath{\varepsilon}$. In this report we show that the classical radiation acts like a focusing element of the interference and diffraction patterns and how it alters the visibility and distinguishabilily. Furthermore, in our double-slit scheme the two possible paths are correlated with the internal atomic states, which allows us to study the relationship between concurrence and wave-particle duality considering different cases.
On the Use of Memristive Hyperchaotic System to Design Color Image Encryption Scheme

In this article a novel encryption scheme based on memristive hyperchaotic system was proposed. Firstly, combined with SHA-512 hash function and plain image, key sequences can be generated by iterating the memristive hyperchaotic system. Then, permutation operation and diffusion operation are performed for each row and column of the plain image. Finally, a serious of simulation experiments are conducted to evaluate the proposed encryption scheme, showing that the proposed scheme has well performance. Further, NIST SP800-22 test results show that the randomness of the hyperchaotic pseudorandom sequence used by the algorithm is better than the general linear congruence pseudorandom sequence.
A Pseudonymisation Protocol With Implicit and Explicit Consent Routes for Health Records in Federated Ledgers

Healthcare data for primary use (diagnosis) may be encrypted for confidentiality purposes; however, secondary uses such as feeding machine learning algorithms requires open access. Full anonymity has no traceable identifiers to report diagnosis results. Moreover, implicit and explicit consent routes are of practical importance under recent data protection regulations (GDPR), translating directly into break-the-glass requirements. Pseudonymisation is an acceptable compromise when dealing with such orthogonal requirements and is an advisable measure to protect data. Our work presents a pseudonymisation protocol that is compliant with implicit and explicit consent routes. The protocol is constructed on a (t,n)-threshold secret sharing scheme and public key cryptography. The pseudonym is safely derived from a fragment of public information without requiring any data-subject's secret. The method is proven secure under reasonable cryptographic assumptions and scalable from the experimental results.
Financial Sentiment Analysis Based on Pre-training and TextCNN

Since the research of sentiment analysis is mostly concentrated in the field of sentiment analysis on Weibo, and there is less research on sentiment analysis of financial text, this thesis proposes a financial sentiment analysis model based on pre-training and TextCNN. First, the pre-trained model is used to initially extract the emotional features of the text. It can extract text features well, and can extract information between words at arbitrary intervals when processing text sequences. Then use the improved TextCNN to construct a sentiment analysis network to further extract the sentiment features of the text, effectively identify the sentiment of the text, and complete the sentiment analysis of financial text. This thesis conducts experiments on a balanced corpus data set based on financial texts, and compares it with other classic sentiment analysis algorithms. Experimental results show that the proposed method works best in the field of financial text sentiment analysis.
LBPH Algorithm for Frontal and Side Profile Face Recognition on GPU

Face recognition is a method which identifies a person based on the profile or features of the face of that person. LBPH (Local Binary Patterns Histogram) is a method to detect and recognize the face of a person. In LBP, first, some portion of an image which is in grayscale is taken as 3×3 window size and the pixel value of neighborhood is compared with the central pixel value and then the binary value is assigned which is then converted to a decimal value. LBP is then combined with histograms and so is called an LBPH algorithm. GPU (Graphics Processing Unit) is an electronic circuit which is more powerful than CPU (Central Processing Unit). In this paper, a frontal face and side profile face recognition using LBPH algorithm are implemented on GPU. The performance of the CPU and GPU are then compared.
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining

Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle in the open world, facing various types of adversarial OOD inputs. While methods leveraging auxiliary OOD data have emerged, our analysis reveals a key insight that the majority of auxiliary OOD examples may not meaningfully improve the decision boundary of the OOD detector. In this paper, we provide a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection. We show that, by mining informative auxiliary OOD data, one can significantly improve OOD detection performance, and somewhat surprisingly, generalize to unseen adversarial attacks. ATOM achieves state-of-the-art performance under a broad family of classic and adversarial OOD evaluation tasks. For example, on the CIFAR-10 in-distribution dataset, ATOM reduces the FPR95 by up to 57.99\% under adversarial OOD inputs, surpassing the previous best baseline by a large margin.
Does Blockchain Technology Drive Sustainability? An Exploratory Review

Blockchain technology is an extension of distributed ledger technology and it is used in cryptocurrencies. Many studies describe blockchain technology and cryptocurrency is an application of it in a very broad sense. Blockchain technology has several applications. Some of these applications could have direct or indirect relevance to either or both pillars of sustainability advocated by Crowther, Seifi, and Wond (2019). Extending to cryptocurrencies like bitcoin, one possible connection to sustainability may be the reduction of the use of paper for printing currency notes, which can save forests. Furthermore, the growing cryptocurrency market attracted the investors to focus on the price fluctuations but making them forget about the terrifying carbon problem associated with cryptocurrencies. However, this possibility has not been demonstrated anywhere so far. The issue examined here is how blockchain technology can be used for solving sustainability problems. We initiate a qualitative study of the blockchain technology/cryptocurrency and sustainability using the twin pillars of sustainability: (1) responsibility, (2) governance. An exploratory review linking blockchain technology/cryptocurrency and sustainability and its two pillars revealed many actual and trial applications by corporates as CSR initiatives and other novel programs by various agencies in various countries. In governance, corporates use the CSR route to address sustainability issues. However, no definition is an available linking cryptocurrency, blockchain technology, and sustainability and we developed a definition to fill the gap. This paper stresses that the sustainability perspective has not been used to develop the cryptocurrency definition, but rather technological and legal perspectives have employed.
Quantum discord of the Werner state in noninertial frames

Using the single-mode approximation we study the quantum correlations of a free Dirac field on the Werner state in the noninertial frame. We present analytical quantum discord D and its geometric measurement DG. We notice that the quantum discord still exists for the system ρARI even in the acceleration limit. We also calculate the entanglement of formation (EOF) and the geometric measure of entanglement (EG) to compare the behaviors of the entanglement with the quantum discord. The entanglement is nonzero for the fidelity of the Werner state with a Bell state F > 0.5, while the quantum discord is equal to zero only for F = 0.25. We also observe that the quantum discord is more robust to the change of acceleration than the entanglement, which is more sensitive to the acceleration.
Portable high‐resolution automated 3D imaging for footwear and tire impression capture

Abstract The forensic science community raised the need for improved evidence recognition, collection, and visualization analytical instrumentation for field and laboratory use. While the 3D optical techniques for imaging static objects have been extensively studied, there is still a major gap between current knowledge and collecting high‐quality footwear and tire impression evidence. Among optical means for 3D imaging, digital fringe projection (DFP) techniques reconstruct 3D shape from phase information, achieving camera‐pixel spatial resolution. This paper presents a high‐resolution 3D imaging technology using DFP techniques dedicated to footwear and tire impression capture. We developed fully automated software algorithms and a graphical user interface (GUI) that allow anyone without training to operate for high‐quality 3D data capture. We performed accuracy evaluations and comparisons comparing with the commercial high‐end 3D scanner and carried out qualitative tests for various impressions comparing with the current practices. Overall, our technology achieves similar levels of accuracy and resolution with a high‐end commercially available 3D scanner, while having the merits of being (1) more affordable; (2) much easier to operate; and (3) more robust. Compared with the current practice of casting, our technology demonstrates its superiority because it (1) is non‐destructive; (2) collects more evidence detail than casts, especially when an impression is fragile; (3) requires less time and money to collect each piece of evidence; and (4) results in a digital file that can easily be shared with other examiners.
Depth Super-Resolution via Deep Controllable Slicing Network

Due to the imaging limitation of depth sensors, high-resolution (HR) depth maps are often difficult to be acquired directly, thus effective depth super-resolution (DSR) algorithms are needed to generate HR output from its low-resolution (LR) counterpart. Previous methods treat all depth regions equally without considering different extents of degradation at region-level, and regard DSR under different scales as independent tasks without considering the modeling of different scales, which impede further performance improvement and practical use of DSR. To alleviate these problems, we propose a deep controllable slicing network from a novel perspective. Specifically, our model is to learn a set of slicing branches in a divide-and-conquer manner, parameterized by a distance-aware weighting scheme to adaptively aggregate different depths in an ensemble. Each branch that specifies a depth slice (e.g., the region in some depth range) tends to yield accurate depth recovery. Meanwhile, a scale-controllable module that extracts depth features under different scales is proposed and inserted into the front of slicing network, and enables finely-grained control of the depth restoration results of slicing network with a scale hyper-parameter. Extensive experiments on synthetic and real-world benchmark datasets demonstrate that our method achieves superior performance.
Lab2Pix: Label-Adaptive Generative Adversarial Network for Unsupervised Image Synthesis

Lab2Pix refers to the task of generating photo-realistic images from labels, e.g., semantic labels or sketch labels. Despite inheriting from image-to-image translation, Lab2Pix develops its own characteristics due to the differences between labels and general images. This prevents Lab2Pix task from simply applying general image-to-image translation models. Therefore, we propose an unsupervised framework named Lab2Pix to adaptively synthesize images from labels by elegantly considering the particular properties of label to image synthesis task. Specifically, since the labels contain much less information than the images, we design our generator in a cumulative style which gradually renders synthesized images by fusing features in different levels. Accordingly, the verification process feeds the generated images to a segmentation component and compares the results to the original input label. Furthermore, we propose a sharp enhancement loss, an image consistency loss and a foreground enhancement mask to encourage the network to synthesize photo-realistic images. Experiments conducted on Cityscapes, Facades, Edge2shoes and Edge2handbags datasets demonstrate that our Lab2Pix significantly outperforms existing state-of-the-art unsupervised methods and is even comparable to supervised methods. The source code is available at https://github.com/RoseRollZhu/Lab2Pix.
Real-time-aware reconfiguration decisions for dynamic software product lines

Dynamic Software Product Lines (DSPL) have recently shown promising potentials as integrated engineering methodology for (self-)adaptive software systems. Based on the software-configuration principles of software product lines, DSPL additionally foster reconfiguration capabilities to continuously adapt software products to ever-changing environmental contexts. However, in most recent works concerned with finding near-optimal reconfiguration decisions, real-time aspects of reconfiguration processes are usually out of scope. In this paper, we present a model-based methodology for specifying and automatically analyzing real-time constraints of reconfiguration decisions in a feature-oriented and compositional way. Those real-time aware DSPL specifications are internally translated into timed automata, a well-founded formalism for real-time behaviors. This representation allows for formally reasoning about consistency and worst-case/best-case execution-time behaviors of sequences of reconfiguration decisions. The technique is implemented in a prototype tool and experimentally evaluated with respect to a set of case studies1.