OpenRaiser/Intern-Atlas huggingface dataset:
Source-Relaxed Domain Adaptation for Image Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Domain adaptation (DA) has drawn high interests for its capacity to adapt a model trained on labeled source data to perform well on unlabeled or weakly labeled target data from a different domain. Most common DA techniques require the concurrent access to the input images of both the source and target domains. However, in practice, it is common that the source images are not available in the adaptation phase. This is a very frequent DA scenario in medical imaging, for instance, when the source and target images come from different clinical sites. We propose a novel formulation for adapting segmentation networks, which relaxes such a constraint. Our formulation is based on minimizing a label-free entropy loss defined over target-domain data, which we further guide with a domain invariant prior on the segmentation regions. Many priors can be used, derived from anatomical information. Here, a class-ratio prior is learned via an auxiliary network and integrated in the form of a Kullback-Leibler (KL) divergence in our overall loss function. We show the effectiveness of our prior-aware entropy minimization in adapting spine segmentation across different MRI modalities. Our method yields comparable results to several state-of-the-art adaptation techniques, even though is has access to less information, the source images being absent in the adaptation phase. Our straight-forward adaptation strategy only uses one network, contrary to popular adversarial techniques, which cannot perform without the presence of the source images. Our framework can be readily used with various priors and segmentation problems.
Detecting and Counting Pistachios based on Deep Learning
Venue: arXiv cs.CV 2021
Year: 2021

Pistachios are nutritious nuts that are sorted based on the shape of their shell into two categories: Open-mouth and Closed-mouth. The open-mouth pistachios are higher in price, value, and demand than the closed-mouth pistachios. Because of these differences, it is considerable for production companies to precisely count the number of each kind. This paper aims to propose a new system for counting the different types of pistachios with computer vision. We have introduced and shared a new dataset of pistachios, including six videos with a total length of 167 seconds and 3927 labeled pistachios. Unlike many other works, our model counts pistachios in videos, not images. Counting objects in videos need assigning each object between the video frames so that each object be counted once. The main two challenges in our work are the existence of pistachios' occlusion and deformation of pistachios in different frames because open-mouth pistachios that move and roll on the transportation line may appear as closed-mouth in some frames and open-mouth in other frames. Our novel model first is trained on the RetinaNet object detector network using our dataset to detect different types of pistachios in video frames. After gathering the detections, we apply them to a new counter algorithm based on a new tracker to assign pistachios in consecutive frames with high accuracy. Our model is able to assign pistachios that turn and change their appearance (e.g., open-mouth pistachios that look closed-mouth) to each other so does not count them incorrectly. Our algorithm performs very fast and achieves good counting results. The computed accuracy of our algorithm on six videos (9486 frames) is 94.75%.
Graph Consistency as a Graduated Property: Consistency-Sustaining and -Improving Graph Transformations
Venue: arXiv cs.LO 2021
Year: 2021

Where graphs are used for modelling and specifying systems, consistency is an important concern. To be a valid model of a system, the graph structure must satisfy a number of constraints. To date, consistency has primarily been viewed as a binary property: a graph either is or is not consistent with respect to a set of graph constraints. This has enabled the definition of notions such as constraint-preserving and constraint-guaranteeing graph transformations. Many practical applications - for example model repair or evolutionary search - implicitly assume a more graduated notion of consistency, but without an explicit formalisation only limited analysis of these applications is possible. In this paper, we introduce an explicit notion of consistency as a graduated property, depending on the number of constraint violations in a graph. We present two new characterisations of transformations (and transformation rules) enabling reasoning about the gradual introduction of consistency: while consistency-sustaining transformations do not decrease the consistency level, consistency-improving transformations strictly reduce the number of constraint violations. We show how these new definitions refine the existing concepts of constraint-preserving and constraint-guaranteeing transformations. To support a static analysis based on our characterisations, we present criteria for deciding which form of consistency ensuring transformations is induced by the application of a transformation rule. We illustrate our contributions in the context of an example from search-based model engineering.
Physical Security in the Post-quantum Era: A Survey on Side-channel Analysis, Random Number Generators, and Physically Unclonable Functions
Venue: arXiv cs.CR 2021
Year: 2021

Over the past decades, quantum technology has seen consistent progress, with notable recent developments in the field of quantum computers. Traditionally, this trend has been primarily seen as a serious risk for cryptography; however, a positive aspect of quantum technology should also be stressed. In this regard, viewing this technology as a resource for honest parties rather than adversaries, it may enhance not only the security, but also the performance of specific cryptographic schemes. While considerable effort has been devoted to the design of quantum-resistant and quantum-enhanced schemes, little effort has been made to understanding their physical security. Physical security deals with the design and implementation of security measures fulfilling the practical requirements of cryptographic primitives, which are equally essential for classic and quantum ones. This survey aims to draw greater attention to the importance of physical security, with a focus on secure key generation and storage as well as secure execution. More specifically, the possibility of performing side-channel analysis in the quantum world is discussed and compared to attacks launched in the classic world. Besides, proposals for quantum random number generation and quantum physically unclonable functions are compared to their classic counterparts and further analyzed to give a better understanding of their features, advantages, and shortcomings. Finally, seen from these three perspectives, this survey provides an outlook for future research in this direction.
A First Look into DeFi Oracles
Venue: arXiv cs.CR 2021
Year: 2021

Recently emerging Decentralized Finance (DeFi) takes the promise of cryptocurrencies a step further, leveraging their decentralized networks to transform traditional financial products into trustless and transparent protocols that run without intermediaries. However, these protocols often require critical external information, like currency or commodity exchange rates, and in this respect they rely on special oracle nodes. In this paper, we present the first study of DeFi oracles deployed in practice. First, we investigate designs of mainstream DeFi platforms that rely on data from oracles. We find that these designs, surprisingly, position oracles as trusted parties with no or low accountability. Then, we present results of large-scale measurements of deployed oracles. We find and report that prices reported by oracles regularly deviate from current exchange rates, oracles are not free from operational issues, and their reports include anomalies. Finally, we compare the oracle designs and propose potential improvements.
A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

In a class of piecewise-constant image segmentation models, we propose to incorporate a weighted difference of anisotropic and isotropic total variation (AITV) to regularize the partition boundaries in an image. In particular, we replace the total variation regularization in the Chan-Vese segmentation model and a fuzzy region competition model by the proposed AITV. To deal with the nonconvex nature of AITV, we apply the difference-of-convex algorithm (DCA), in which the subproblems can be minimized by the primal-dual hybrid gradient method with linesearch. The convergence of the DCA scheme is analyzed. In addition, a generalization to color image segmentation is discussed. In the numerical experiments, we compare the proposed models with the classic convex approaches and the two-stage segmentation methods (smoothing and then thresholding) on various images, showing that our models are effective in image segmentation and robust with respect to impulsive noises.
IterDet: Iterative Scheme for Object Detection in Crowded Environments
Venue: arXiv cs.CV 2021
Year: 2021

Deep learning-based detectors usually produce a redundant set of object bounding boxes including many duplicate detections of the same object. These boxes are then filtered using non-maximum suppression (NMS) in order to select exactly one bounding box per object of interest. This greedy scheme is simple and provides sufficient accuracy for isolated objects but often fails in crowded environments, since one needs to both preserve boxes for different objects and suppress duplicate detections. In this work we develop an alternative iterative scheme, where a new subset of objects is detected at each iteration. Detected boxes from the previous iterations are passed to the network at the following iterations to ensure that the same object would not be detected twice. This iterative scheme can be applied to both one-stage and two-stage object detectors with just minor modifications of the training and inference procedures. We perform extensive experiments with two different baseline detectors on four datasets and show significant improvement over the baseline, leading to state-of-the-art performance on CrowdHuman and WiderPerson datasets. The source code and the trained models are available at https://github.com/saic-vul/iterdet.
Creative Quantum Computing: Inverse FFT, Sound Synthesis, Adaptive Sequencing and Musical Composition
Venue: arXiv cs.SD 2021
Year: 2021

Quantum computing is emerging as an alternative computing technology, which is built on the principles of subatomic physics. In spite of continuing progress in developing increasingly more sophisticated hardware and software, access to quantum computing still requires specialist expertise that is largely confined to research laboratories. Moreover, the target applications for these developments remain primarily scientific. This chapter introduces research aimed at improving this scenario. Our research is aimed at extending the range of applications of quantum computing towards the arts and creative applications, music being our point of departure. This chapter reports on initial outcomes, whereby quantum information processing controls an inverse Fast Fourier Transform (FFT) sound synthesizer and an adaptive musical sequencer. A composition called Zeno is presented to illustrate a practical real-world application.
The JuliaConnectoR: a functionally oriented interface for integrating Julia in R
Venue: arXiv cs.MS 2021
Year: 2021

Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the CRAN repository and GitHub (https://github.com/stefan-m-lenz/JuliaConnectoR), in particular for making advanced deep learning tools available. For maintainability and stability, we decided to base communication between R and Julia on TCP, using an optimized binary format for exchanging data. Our package also specifically contains features that allow for a convenient interactive use in R. This makes it easy to develop R extensions with Julia or to simply call functionality from Julia packages in R. Interacting with Julia objects and calling Julia functions becomes user-friendly, as Julia functions and variables are made directly available as objects in the R workspace. We illustrate the further features of our package with code examples, and also discuss advantages over the two alternative packages JuliaCall and XRJulia. Finally, we demonstrate the usage of the package with a more extensive example for employing neural ordinary differential equations, a recent deep learning technique that has received much attention. This example also provides more general guidance for integrating deep learning techniques from Julia into R.
On the Performance Analysis of Streaming Codes over the Gilbert-Elliott Channel
Venue: arXiv cs.IT 2021
Year: 2021

The Gilbert-Elliot (GE) channel is a commonly-accepted model for packet erasures in networks. Streaming codes are a class of packet-level erasure codes designed to provide reliable communication over the GE channel. The design of a streaming code may be viewed as a two-step process. In the first, a more tractable, delay-constrained sliding window (DCSW) channel model is considered as a proxy to the GE channel. The streaming code is then designed to reliably recover from all erasures introduced by the DCSW channel model. Simulation is typically used to evaluate the performance of the streaming code over the original GE channel, as analytic performance evaluation is challenging. In the present paper, we take an important first step towards analytical performance evaluation. Recognizing that most, efficient constructions of a streaming code are based on the diagonal embedding or horizontal embedding of scalar block codes within a packet stream, this paper provides upper and lower bounds on the block-erasure probability of the underlying scalar block code when operated over the GE channel.
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we propose and analyze SQuARM-SGD, a communication-efficient algorithm for decentralized training of large-scale machine learning models over a network. In SQuARM-SGD, each node performs a fixed number of local SGD steps using Nesterov's momentum and then sends sparsified and quantized updates to its neighbors regulated by a locally computable triggering criterion. We provide convergence guarantees of our algorithm for general (non-convex) and convex smooth objectives, which, to the best of our knowledge, is the first theoretical analysis for compressed decentralized SGD with momentum updates. We show that the convergence rate of SQuARM-SGD matches that of vanilla SGD. We empirically show that including momentum updates in SQuARM-SGD can lead to better test performance than the current state-of-the-art which does not consider momentum updates.
Formalizing of Category Theory in Agda
Venue: arXiv cs.LO 2021
Year: 2021

The generality and pervasiness of category theory in modern mathematics makes it a frequent and useful target of formalization. It is however quite challenging to formalize, for a variety of reasons. Agda currently (i.e. in 2020) does not have a standard, working formalization of category theory. We document our work on solving this dilemma. The formalization revealed a number of potential design choices, and we present, motivate and explain the ones we picked. In particular, we find that alternative definitions or alternative proofs from those found in standard textbooks can be advantageous, as well as "fit" Agda's type theory more smoothly. Some definitions regarded as equivalent in standard textbooks turn out to make different "universe level" assumptions, with some being more polymorphic than others. We also pay close attention to engineering issues so that the library integrates well with Agda's own standard library, as well as being compatible with as many of supported type theories in Agda as possible.
Natural Way to Overcome the Catastrophic Forgetting in Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Not so long ago, a method was discovered that successfully overcomes the catastrophic forgetting in neural networks. Although we know about the cases of using this method to preserve skills when adapting pre-trained networks to particular tasks, it has not obtained widespread distribution yet. In this paper, we would like to propose an alternative method of overcoming catastrophic forgetting based on the total absolute signal passing through each connection in the network. This method has a simple implementation and seems to us essentially close to the processes occurring in the brain of animals to preserve previously learned skills during subsequent learning. We hope that the ease of implementation of this method will serve its wide application.
Construction $C^\star$ from Self-Dual Codes
Venue: arXiv cs.IT 2021
Year: 2021

Construction $C^\star$ was recently introduced as a generalization of the multilevel Construction C (or Forney's code-formula), such that the coded levels may be dependent. Both constructions do not produce a lattice in general, hence the central idea of this paper is to present a 3-level lattice Construction $C^\star$ scheme that admits an efficient nearest-neighborhood decoding. In order to achieve this objective, we choose coupled codes for levels 1 and 3, and set the second level code C2 as an independent linear binary self-dual code, which is known to have a rich mathematical structure among families of linear codes. Our main result states a necessary and sufficient condition for this construction to generate a lattice. We then present examples of efficient lattices and also non-lattice constellations with good packing properties.
Joint Planning of Network Slicing and Mobile Edge Computing: Models and Algorithms
Venue: arXiv cs.NI 2021
Year: 2021

Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. This paper considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios.
Predicting User Emotional Tone in Mental Disorder Online Communities
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, Online Social Networks have become an important medium for people who suffer from mental disorders to share moments of hardship, and receive emotional and informational support. In this work, we analyze how discussions in Reddit communities related to mental disorders can help improve the health conditions of their users. Using the emotional tone of users' writing as a proxy for emotional state, we uncover relationships between user interactions and state changes. First, we observe that authors of negative posts often write rosier comments after engaging in discussions, indicating that users' emotional state can improve due to social support. Second, we build models based on SOTA text embedding techniques and RNNs to predict shifts in emotional tone. This differs from most of related work, which focuses primarily on detecting mental disorders from user activity. We demonstrate the feasibility of accurately predicting the users' reactions to the interactions experienced in these platforms, and present some examples which illustrate that the models are correctly capturing the effects of comments on the author's emotional tone. Our models hold promising implications for interventions to provide support for people struggling with mental illnesses.
Efficient Load-Balancing through Distributed Token Dropping
Venue: arXiv cs.DC 2021
Year: 2021

We introduce a new graph problem, the token dropping game, and we show how to solve it efficiently in a distributed setting. We use the token dropping game as a tool to design an efficient distributed algorithm for stable orientations and more generally for locally optimal semi-matchings. The prior work by Czygrinow et al. (DISC 2012) finds a stable orientation in $O(Δ^5)$ rounds in graphs of maximum degree $Δ$, while we improve it to $O(Δ^4)$ and also prove a lower bound of $Ω(Δ)$.
Construction of 1-Bit Transmit Signal Vectors for Downlink MU-MISO Systems: QAM constellations
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we investigate the construction of a transmit signal for a base station with a massive number of antenna arrays under the cost-effective 1-bit digital-to-analog converters. Due to the coarse nonlinear property, conventional precoding methods could not yield an attractive performance with a severe error-floor problem. Moreover, finding an optimal transmit signal is computationally implausible because of its combinatorial nature. Thus, it is still an open problem to construct a 1-bit transmit signal efficiently. As an extension of our earlier work, we propose an efficient method to construct an 1-bit transmit-signal under quadrature-amplitude-modulation constellations. Toward this, we first derive the so-called feasibility condition which ensures that every user's noiseless observation belongs to a desired decision region, and then transform it as linear constraints. Taking into account the robustness to an additive noise, the proposed construction method is formulated as a well-defined mixed-integer-linear-programming problem. Based on this, we develop a low-complexity algorithm to solve it (equivalently, to generate a 1-bit transmit signal). Via simulations, we verify the superiority of the proposed method in terms of a computational complexity and detection performance.