Reviewerly/exHarmony huggingface dataset:
ElixirNet: Relation-Aware Network Architecture Adaptation for Medical Lesion Detection

Most advances in medical lesion detection network are limited to subtle modification on the conventional detection network designed for natural images. However, there exists a vast domain gap between medical images and natural images where the medical image detection often suffers from several domain-specific challenges, such as high lesion/background similarity, dominant tiny lesions, and severe class imbalance. Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain? Is there more powerful operations, filters, and sub-networks that better fit the medical lesion detection problem to be discovered? In this paper, we introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporates relation-aware operations among region proposals, and leads to more suitable and efficient classification and localization. 3) Relation transfer module incorporates the semantic relationship and transfers the relevant contextual information with an interpretable graph, thus alleviates the problem of lack of annotations for all types of lesions. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters.
Enhanced Gaze Following via Object Detection and Human Pose Estimation

The aim of gaze following is to estimate the gaze direction, which is useful for the understanding of human behaviour in various applications. However, it is still an open problem that has not been fully studied. In this paper, we present a novel framework for gaze following problem, where both the front/side face case and the back face case are taken into account. For the front/side face case, head pose estimation is applied to estimate the gaze, and then object detection is used to further refine the gaze direction by selecting the object that intersects with the gaze in a certain range. For the back face case, a deep neural network with the human pose information is proposed for gaze estimation. Experiments are carried out to demonstrate the superiority of the proposed method, as compared with the state-of-the-art method.
Individual Sensitivity Preprocessing for Data Privacy

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Individual Sensitivity Preprocessing for Data PrivacyRachel Cummings and David DurfeeRachel Cummings and David Durfeepp.528 - 547Chapter DOI:https://doi.org/10.1137/1.9781611975994.32PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract The sensitivity metric in differential privacy, which is informally defined as the largest marginal change in output between neighboring databases, is of substantial significance in determining the accuracy of private data analyses. Techniques for improving accuracy when the average sensitivity is much smaller than the worst-case sensitivity have been developed within the differential privacy literature, including tools such as smooth sensitivity, Sample-and-Aggregate, Propose-Test-Release, and Lipschitz extensions. In this work, we provide a new and general Sensitivity-Preprocessing framework for reducing sensitivity, where efficient application gives state-of-the-art accuracy for privately outputting the important statistical metrics median and mean when no underlying assumptions are made about the database. In particular, our framework compares favorably to smooth sensitivity for privately outputting median, in terms of both running time and accuracy. Furthermore, because our framework is a preprocessing step, it can also be complementary to smooth sensitivity and any other private mechanism, where applying both can achieve further gains in accuracy. We additionally introduce a new notion of individual sensitivity and show that it is an important metric in the variant definition of personalized differential privacy. We show that our algorithm can extend to this context and serve as a useful tool for this variant definition and its applications in markets for privacy. Given the effectiveness of our framework in these important statistical metrics, we further investigate its properties and show that: (1) Our construction is conducive to efficient implementation with strong accuracy guarantees, evidenced by an O(n) implementation for median (with presorted data), and O(n2) implementation for more complicated functions such as mean, α-trimmed mean, and variance. (2) Our construction is both NP-hard and also optimal in the general setting (3) Our construction can be extended to higher dimensions, although it incurs accuracy loss that is linear in the dimension. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011
Risk management practices in information security: Exploring the status quo in the DACH region

Information security management aims at ensuring proper protection of information values and information processing systems (i.e. assets). Information security risk management techniques are incorporated to deal with threats and vulnerabilities that impose risks to information security properties of these assets. This paper investigates the current state of risk management practices being used in information security management in the DACH region (Germany, Austria, Switzerland). We used an anonymous online survey targeting strategic and operative information security and risk managers and collected data from 26 organizations. We analyzed general practices, documentation artifacts, patterns of stakeholder collaboration as well as tool types and data sources used by enterprises to conduct information security management activities. Our findings show that the state of practice of information security risk management is in need of improvement. Current industrial practice heavily relies on manual data collection and complex potentially subjective decision processes with multiple stakeholders involved. Dedicated risk management tools and methods are used selectively and neglected in favor of general-purpose documentation tools and direct communication between stakeholders. In light of our results we propose guidelines for the development of risk management practices that are better aligned with the current operational situation in information security management.
Color Image Demosaicing Using Progressive Collaborative Representation

In this paper, a progressive collaborative representation (PCR) framework is proposed that is able to incorporate any existing color image demosaicing method for further boosting its demosaicing performance. Our PCR consists of two phases: (i) offline training and (ii) online refinement. In phase (i), multiple training-and-refining stages will be performed. In each stage, a new dictionary will be established through the learning of a large number of feature-patch pairs, extracted from the demosaicked images of the current stage and their corresponding original full-color images. After training, a projection matrix will be generated and exploited to refine the current demosaicked image. The updated image with improved image quality will be used as the input for the next training-and-refining stage and performed the same processing likewise. At the end of phase (i), all the projection matrices generated as above-mentioned will be exploited in phase (ii) to conduct online demosaicked image refinement of the test image. Extensive simulations conducted on two commonly-used test datasets (i.e., the IMAX and Kodak) for evaluating the demosaicing algorithms have clearly demonstrated that our proposed PCR framework is able to constantly boost the performance of any image demosaicing method we experimented, in terms of the objective and subjective performance evaluations.
Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space With Softplus Loss

Filtering out unrealistic images from trained generative adversarial networks (GANs) has attracted considerable attention recently. Two density ratio based subsampling methods-Discriminator Rejection Sampling (DRS) and Metropolis-Hastings GAN (MH-GAN)-were recently proposed, and their effectiveness in improving GANs was demonstrated on multiple datasets. However, DRS and MH-GAN are based on discriminator-based density ratio estimation (DRE) methods, so they may not work well if the discriminator in the trained GAN is far from optimal. Moreover, they do not apply to some GANs (e.g., MMD-GAN). In this paper, we propose a novel Softplus (SP) loss for DRE. Based on it, we develop a sample-based DRE method in a feature space learned by a specially designed and pre-trained ResNet-34, termed DRE-F-SP. We derive the rate of convergence of a density ratio model trained under the SP loss. Then, we propose three density ratio based subsampling methods for GANs based on DRE-F-SP. Our subsampling methods do not rely on the optimality of the discriminator and are suitable for all types of GANs. We empirically show our subsampling approach can substantially outperform DRS and MH-GAN on a synthetic dataset, CIFAR-10, MNIST and CelebA, using multiple GANs.
Weak-value-amplification analysis beyond the Aharonov-Albert-Vaidman limit

The weak-value (WV) measurement proposed by Aharonov, Albert, and Vaidman (AAV) has attracted a great deal of interest in connection with quantum metrology. In this paper, we extend the analysis beyond the AAV limit and obtain a few main results. (i) We obtain a nonperturbative result for the signal-to-noise ratio (SNR). In contrast to the AAV prediction, we find that the SNR asymptotically gets worse when the AAV WV ${A}_{w}$ becomes large, i.e., in the case $g|{A}_{w}{|}^{2}\ensuremath{\gg}1$, where $g$ is the measurement strength. (ii) With the increase of $g$ (but also small), we find that the SNR is comparable to the result under the AAV limit, while both can reach---actually the former can slightly exceed---the SNR of the standard measurement. However, with a further increase of $g$, the WV technique will become less efficient than the standard measurement, despite the fact that the postselection probability is increased. (iii) We find that the Fisher information can characterize the estimate precision qualitatively well as the SNR, yet the difference will become more prominent with the increase of $g$. (iv) We carry out analytic expressions of the SNR in the presence of technical noises and illustrate the particular advantage of the imaginary WV measurement. The nonperturbative result of the SNR manifests a favorable range of the noise strength and allows an optimal determination.
Multiphoton resonance and chiral transport in the generalized Rabi model

The generalized Rabi model (gRM) with both one- and two-photon coupling terms has been successfully implemented in circuit quantum electrodynamics systems. In this paper, we examine theoretically multiphoton resonances in the gRM and derive their effective Hamiltonians. With different detunings in the system, we show that all three- to six-photon resonances can be achieved by involving two intermediate states. Furthermore, we study the interplay between multiphoton resonance and chiral transport of photon Fock states in a resonator junction with broken time-reversal symmetry. Depending on the qubit-photon interaction and photon-hopping amplitude, we find that the system can demonstrate different short-time dynamics.
Resource-Optimized Fermionic Local-Hamiltonian Simulation on a Quantum Computer for Quantum Chemistry

The ability to simulate a fermionic system on a quantum computer is expected to revolutionize chemical engineering, materials design, nuclear physics, to name a few. Thus, optimizing the simulation circuits is of significance in harnessing the power of quantum computers. Here, we address this problem in two aspects. In the fault-tolerant regime, we optimize the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>R</mml:mi><mml:mi>z</mml:mi></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">T gate</mml:mtext></mml:mrow></mml:math> counts along with the ancilla qubit counts required, assuming the use of a product-formula algorithm for implementation. We obtain a savings ratio of two in the gate counts and a savings ratio of eleven in the number of ancilla qubits required over the state of the art. In the pre-fault tolerant regime, we optimize the two-qubit gate counts, assuming the use of the variational quantum eigensolver (VQE) approach. Specific to the latter, we present a framework that enables bootstrapping the VQE progression towards the convergence of the ground-state energy of the fermionic system. This framework, based on perturbation theory, is capable of improving the energy estimate at each cycle of the VQE progression, by about a factor of three closer to the known ground-state energy compared to the standard VQE approach in the test-bed, classically-accessible system of the water molecule. The improved energy estimate in turn results in a commensurate level of savings of quantum resources, such as the number of qubits and quantum gates, required to be within a pre-specified tolerance from the known ground-state energy. We also explore a suite of generalized transformations of fermion to qubit operators and show that resource-requirement savings of up to more than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>20</mml:mn><mml:mi mathvariant="normal">%</mml:mi></mml:math>, in small instances, is possible.
Globally Optimal Vertical Direction Estimation in Atlanta World

In man-made environments, most of the objects and structures are organized in the form of orthogonal and parallel planes. These planes can be approximated by an Atlanta world assumption, in which the normals of planes can be represented by Atlanta frames. The Atlanta world assumption has one vertical frame and multiple horizontal frames. Conventionally, given a set of inputs such as surface normals, the Atlanta frame estimation problem can be solved by a branch-and-bound (BnB) algorithm. However, the runtime of the BnB algorithm will increase greatly when the dimensionality (i.e., the number of horizontal frames) increases. In this paper, we estimate only the vertical direction, instead of all Atlanta frames at once. Accordingly, we propose a vertical direction estimation method by considering the relationship between the vertical frame and horizontal frames. Concretely, our approach employs a BnB algorithm to search the vertical direction, thereby guaranteeing global optimality without requiring prior knowledge of the number of Atlanta frames. In order to guarantee convergence, four novel bounds are investigated, by mapping a 3D hemisphere to a 2D region. We verify the feasibility of the proposed method using various challenging synthetic and real-world data.
Predicting the Orbifold Origin of the MSSM

MSSM-like string models from the compactification of the heterotic string on toroidal orbifolds (of the kind $T^6/P$) have distinct phenomenological properties, like the spectrum of vector-like exotics, the scale of supersymmetry breaking, and the existence of non-Abelian flavor symmetries. We show that these characteristics depend crucially on the choice of the underlying orbifold point group $P$. In detail, we use boosted decision trees to predict $P$ from phenomenological properties of MSSM-like orbifold models. As this works astonishingly well, we can utilize machine learning to predict the orbifold origin of the MSSM.
Using case-level context to classify cancer pathology reports

Individual electronic health records (EHRs) and clinical reports are often part of a larger sequence—for example, a single patient may generate multiple reports over the trajectory of a disease. In applications such as cancer pathology reports, it is necessary not only to extract information from individual reports, but also to capture aggregate information regarding the entire cancer case based off case-level context from all reports in the sequence. In this paper, we introduce a simple modular add-on for capturing case-level context that is designed to be compatible with most existing deep learning architectures for text classification on individual reports. We test our approach on a corpus of 431,433 cancer pathology reports, and we show that incorporating case-level context significantly boosts classification accuracy across six classification tasks—site, subsite, laterality, histology, behavior, and grade. We expect that with minimal modifications, our add-on can be applied towards a wide range of other clinical text-based tasks.
Fourier Transform to Group Feature on Generated Coarser Contours for Fast 2D Shape Matching

Fourier descriptors are classical global shape descriptors with high matching speed but low accuracy. To obtain higher accuracy, a novel framework for forming Fourier descriptors is proposed and named as MSFDGF (multiscale Fourier descriptor using group feature). MSFDGF achieves multiscale description by generating coarser contours. Then, a group of complementary features are extracted on the generated coarser contours. Finally, Fourier transform is performed on the features. MSFDGF-SH is a new global descriptor using the MSFDGF framework and shape histograms. Experiments are conducted on four databases, which are MPEG-7 CE-1 Part B, Swedish Plant Leaf, Kimia 99 and Expanded Articulated Database, to evaluate the performance of MSFDGF-SH. The experimental results show that MSFDGF-SH is an effective and efficient global shape descriptor. This new descriptor has a high accuracy of 87.76%, which exceeds the Shape Tree on the MPEG-7 CE-1 Part B dataset. This is the first Fourier descriptor that surpasses the Shape Tree method in terms of both accuracy and speed on this dataset.
A comparative study of different state-of-the-art hate speech detection methods in Hindi-English code-mixed data

This publication has emanated from research supported inpart by a research grant from Science Foundation Ireland(SFI) under Grant Number SFI/12/RC/2289 (Insight),SFI/12/RC/2289 P2 (Insight 2), & SFI/18/CRT/6223(CRT-Centre for Research Training in Artficial Intelligence) co-funded by the European Regional DevelopmentFund as well as by the EU H2020 programme under grantagreements 731015 (ELEXIS-European Lexical Infrastructure), 825182 (Pret- ˆ a-LLOD), and Irish Research Council `grant IRCLA/2017/129 (CARDAMOM-ComparativeDeep Models of Language for Minority and HistoricalLanguages). The authors are grateful to Ajay Bohra andhis team for sharing their data set and for their support.We would also like to thank our annotators for theircontribution and lending us their precious time.
Opportunities and Challenges of Text Input in Portable Virtual Reality

Text input in virtual reality is not widespread outside of labs, although being increasingly researched. Current setups require powerful components that are expensive or not portable, hence preventing effective in-the-wild use. Latest technological advances enable portable mixed reality experiences on smartphones. In this work, we propose a portable low-fidelity solution for text input in mixed reality on a physical keyboard that employs accessible off-the-shelf components. Through a user study with 24 participants, we show that our prototype leads to a significantly higher text input performance compared to soft keyboards. However, it falls behind on copy editing compared to soft keyboards. Qualitative inquiries revealed that participants enjoyed the ample display space and perceived the accompanied privacy as beneficial. Finally, we conclude with challenges and future research that builds upon the presented findings.
Feature Quantization Improves GAN Training

The instability in GAN training has been a long-standing problem despite remarkable research efforts. We identify that instability issues stem from difficulties of performing feature matching with mini-batch statistics, due to a fragile balance between the fixed target distribution and the progressively generated distribution. In this work, we propose Feature Quantization (FQ) for the discriminator, to embed both true and fake data samples into a shared discrete space. The quantized values of FQ are constructed as an evolving dictionary, which is consistent with feature statistics of the recent distribution history. Hence, FQ implicitly enables robust feature matching in a compact space. Our method can be easily plugged into existing GAN models, with little computational overhead in training. We apply FQ to 3 representative GAN models on 9 benchmarks: BigGAN for image generation, StyleGAN for face synthesis, and U-GAT-IT for unsupervised image-to-image translation. Extensive experimental results show that the proposed FQ-GAN can improve the FID scores of baseline methods by a large margin on a variety of tasks, achieving new state-of-the-art performance.
Voltage-Tunable Superconducting Resonators: A Platform for Random Access Quantum Memory

In quantum computing architectures, one important factor is the trade-off between the need to couple qubits to each other and to an external drive and the need to isolate them well enough in order to protect the information for an extended period of time. In the case of superconducting circuits, one approach is to utilize fixed frequency qubits coupled to coplanar waveguide resonators such that the system can be kept in a configuration that is relatively insensitive to noise. Here, we propose a scalable voltage-tunable quantum memory (QuMem) design concept compatible with superconducting qubit platforms. Our design builds on the recent progress in fabrication of Josephson field effect transistors (JJ-FETs) which use InAs quantum wells. The JJ-FET is incorporated into a tunable coupler between a transmission line and a high-quality resonator in order to control the overall inductance of the coupler. A full isolation of the high-quality resonator can be achieved by turning off the JJ-FET. This could allow for long coherence times and protection of the quantum information inside the storage cavity. The proposed design would facilitate the implementation of random access memory for storage of quantum information in between computational gate operations.
An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis

Multimodal registration is a challenging task due to the significant variations exhibited from images of different modalities. CT and MRI are two of the most commonly used medical images in clinical diagnosis, since MRI with multicontrast images, together with CT, can provide complementary auxiliary information. The deformable image registration between MRI and CT is essential to analyze the relationships among different modality images. Here, we proposed an indirect multimodal image registration method, i.e., sCT-guided multimodal image registration and problematic image completion method. In addition, we also designed a deep learning-based generative network, Conditional Auto-Encoder Generative Adversarial Network, called CAE-GAN, combining the idea of VAE and GAN under a conditional process to tackle the problem of synthetic CT (sCT) synthesis. Our main contributions in this work can be summarized into three aspects: (1) We designed a new generative network called CAE-GAN, which incorporates the advantages of two popular image synthesis methods, i.e., VAE and GAN, and produced high-quality synthetic images with limited training data. (2) We utilized the sCT generated from multicontrast MRI as an intermediary to transform multimodal MRI-CT registration into monomodal sCT-CT registration, which greatly reduces the registration difficulty. (3) Using normal CT as guidance and reference, we repaired the abnormal MRI while registering the MRI to the normal CT.