Reviewerly/exHarmony huggingface dataset:
Drama Without a Generation

In this paper, we will concentrate on the playwriting of the so-called young generation. In doing so, we will ask how this generation can be defined, given that the writers are quite individualised and dispersed among themselves, and their texts are quite diverse in terms of content, genre and form. We will argue that being born and growing up concurrently with the development of the internet, social networks and other new technologies, which are nowadays fundamentally inscribed in the social fabric, has significantly defined the young generation. Consequently, the boundaries between the virtual and the reality are constantly being blurred, and the symbiosis between the two, summed up in the term “biovirtual”, is continuously being established. Using examples of four theatre texts (The Thirty Somethings by Eva Mahkovic and Tereza Gregorič, It All Began with the Bunny Rabbit Goulash by Varja Hrvatin, The Interpretation of Sanja by Ela Božič and Work and the Maiden I-V: Serf Dramas by Nika Švab), we will reflect on how young authors are incorporating the post-internet reality, defined by the ubiquity of new technologies, into their playwriting. We will consider whether these writers’ generation generates new playwriting forms and dramaturgical approaches. Finally, we will draw attention to the conditions under which these authors operate, which (from systemic education and the repertory logic of staging to the pervasive precarisation and instability) do not provide economic security. Hence, there is no room for experiment and error. This forces writers to opt for already tested dramatic forms, strategies and themes. How, then, can one define the playwriting of a generation if it lacks a clear break with the tradition that would characterise this generation?
Hierarchical Congestion Control (HCC): Fairness and Fast Convergence for Data Centers

Congestion control protocols face several challenges for achieving max-min fairness and high throughput in a timely manner. In the absence of explicit signals, flows infer the congestion state solely based on their limited view of the network. Any attempt to expand the view with support from a centralized entity or network switches needs to deal with overheads as the number of flows scales. We present a system called Hierarchical Congestion Control (HCC) for data center networks. Flows that share bottlenecks are iteratively aggregated to build a hierarchy for propagating congestion control signals. HCC enables flow cooperation for sharing congestion information and collectively reacting to congestion. Aggregate control of multiple flows improves fairness and helps flows to converge faster in case of fluctuations in flow demands or network state. To limit the overheads, we compress congestion signals as they move up the hierarchy, pruning signals that do not involve significant changes. Our evaluation on real workload shows HCC converges to full utilization very fast with an empty queue and near-optimal fairness. Moreover, comparing to a centralized solution, HCC reduces the communication overhead to the core by 73% and the communication overhead from the core by 99.5%.
Qunity: A Unified Language for Quantum and Classical Computing (Extended Version)

We introduce Qunity, a new quantum programming language designed to treat quantum computing as a natural generalization of classical computing. Qunity presents a unified syntax where familiar programming constructs can have both quantum and classical effects. For example, one can use sum types to implement the direct sum of linear operators, exception-handling syntax to implement projective measurements, and aliasing to induce entanglement. Further, Qunity takes advantage of the overlooked BQP subroutine theorem, allowing one to construct reversible subroutines from irreversible quantum algorithms through the uncomputation of "garbage" outputs. Unlike existing languages that enable quantum aspects with separate add-ons (like a classical language with quantum gates bolted on), Qunity provides a unified syntax and a novel denotational semantics that guarantees that programs are quantum mechanically valid. We present Qunity's syntax, type system, and denotational semantics, showing how it can cleanly express several quantum algorithms. We also detail how Qunity can be compiled into a low-level qubit circuit language like OpenQASM, proving the realizability of our design.
Segmentation of Mammogram Abnormalities Using Ant System Based Contour Clustering Algorithm

Background:The Computer-Aided Detection Systems (CADs) can locate and identify the normal and pathological tissues in mammogram images by segmentation.The existing segmentation methods have to test each and every pixel of the image at least once, which is computationally expensive.Objective: This research focuses on detection of microcalcifications from the digital mammograms by segmentation, where the abnormal tissues are segmented from the normal tissues.Methods: To detect microcalcifications from the digital mammograms by segmentation, a novel segmentation approach based on Ant Clustering method namely Ant System based Contour Clustering (ASCC), simulates the ants' foraging behavior is proposed.The proposed ASCC is compared with the state-of art existing methods with respect to area, pixel and edge based metrics on the Mammographic Image Analysis Society (MIAS) Dataset. Results:The segmentation performance of the proposed ASCC method is experimented on 312 digitized mammogram images acquired from the 161 patient's left and right breast Screening.The segmentation by the proposed ASCC is evaluated by the area, pixel and edge based metrics shows that 62.47% common area between overlapping segmented and the reference region by Jaccard index, Goodness based on inter-region contrast of 66.59%, Low Segmentation Error of 9.51%, precision of 93.67%, Recall of 90.90%, 0.85% Figure of Merit, Over-segmented Pixel Rate of 0.43%, and Under-Segmented Pixel Rate of 0.26%.Conclusion: Segmentation is key preprocessing method to accurately locate and identify the normal and pathological tissues in digital mammogram images.This study proposes an ASCC method for segmentation task by hybridizing clustering and contour based segmentation approaches.The evaluated results with respect to area, pixel and edge based metrics shows added advantage in segmentation tasks compared to the other approaches.
RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation

The segmentation task has traditionally been formulated as a complete-label pixel classification task to predict a class for each pixel from a fixed number of predefined semantic categories shared by all images or videos. Yet, following this formulation, standard architectures will inevitably encounter various challenges under more realistic settings where the scope of categories scales up (e.g., beyond the level of 1k). On the other hand, in a typical image or video, only a few categories, i.e., a small subset of the complete label are present. Motivated by this intuition, in this paper, we propose to decompose segmentation into two sub-problems: (i) image-level or video-level multi-label classification and (ii) pixel-level rank-adaptive selected-label classification. Given an input image or video, our framework first conducts multi-label classification over the complete label, then sorts the complete label and selects a small subset according to their class confidence scores. We then use a rank-adaptive pixel classifier to perform the pixel-wise classification over only the selected labels, which uses a set of rank-oriented learnable temperature parameters to adjust the pixel classifications scores. Our approach is conceptually general and can be used to improve various existing segmentation frameworks by simply using a lightweight multi-label classification head and rank-adaptive pixel classifier. We demonstrate the effectiveness of our framework with competitive experimental results across four tasks, including image semantic segmentation, image panoptic segmentation, video instance segmentation, and video semantic segmentation. Especially, with our RankSeg, Mask2Former gains +0.8%/+0.7%/+0.7% on ADE20K panoptic segmentation/YouTubeVIS 2019 video instance segmentation/VSPW video semantic segmentation benchmarks respectively.
Efficient Privacy Preserving Logistic Regression for Horizontally Distributed Data

Internet of Things devices are expanding rapidly and generating huge amount of data. There is an increasing need to explore data collected from these devices. Collaborative learning provides a strategic solution for the Internet of Things settings but also raises public concern over data privacy. In recent years, large amount of privacy preserving techniques have been developed based on secure multi-party computation and differential privacy. A major challenge of collaborative learning is to balance disclosure risk and data utility while maintaining high computation efficiency. In this paper, we proposed privacy preserving logistic regression model using matrix encryption approach. The secure scheme is resilient to chosen plaintext attack, known plaintext attack, and collusion attack that could compromise any agencies in the collaborative learning. Encrypted model estimate is decrypted to provide true model results with no accuracy degradation. Verification phase is implemented to examine dishonest behavior among agencies. Experimental evaluations demonstrate fast convergence rate and high efficiency of proposed scheme.
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning

Reinforcement Learning (RL) can be considered as a sequence modeling task: given a sequence of past state-action-reward experiences, an agent predicts a sequence of next actions. In this work, we propose State-Action-Reward Transformer (StARformer) for visual RL, which explicitly models short-term state-action-reward representations (StAR-representations), essentially introducing a Markovian-like inductive bias to improve long-term modeling. Our approach first extracts StAR-representations by self-attending image state patches, action, and reward tokens within a short temporal window. These are then combined with pure image state representations -- extracted as convolutional features, to perform self-attention over the whole sequence. Our experiments show that StARformer outperforms the state-of-the-art Transformer-based method on image-based Atari and DeepMind Control Suite benchmarks, in both offline-RL and imitation learning settings. StARformer is also more compliant with longer sequences of inputs. Our code is available at https://github.com/elicassion/StARformer.
Clustering of consecutive numbers in permutations avoiding a pattern and in separable permutations

Let $S_n$ denote the set of permutations of $[n]:=\{1,\cdots, n\}$, and denote a permutation $\sigma\in S_n$ by $\sigma=\sigma_1\sigma_2\cdots \sigma_n$. For $l\ge2$ an integer, let $A^{(n)}_{l;k}\subset S_n$ denote the event that the set of $l$ consecutive numbers $\{k, k+1,\cdots, k+l-1\}$ appears in a set of consecutive positions: $\{k,k+1,\cdots, k+l-1\}=\{\sigma_a,\sigma_{a+1},\cdots, \sigma_{a+l-1}\}$, for some $a$. For $\tau\in S_m$, let $S_n(\tau)$ denote the set of $\tau$-avoiding permutations in $S_n$, and let $P_n^{\text{av}(\tau)}$ denote the uniform probability measure on $S_n(\tau)$. Also, let $S_n^{\text{sep}}$ denote the set of separable permutations in $S_n$, and let $P_n^{\text{sep}}$ denote the uniform probability measure on $S_n^{\text{sep}}$. We investigate the quantities $P_n^{\text{av}(\tau)}(A^{(n)}_{l;k})$ and $P_n^{\text{sep}}(A^{(n)}_{l;k})$ for fixed $n$, and the limiting behavior as $n\to\infty$. We also consider the asymptotic properties of this limiting behavior as $l\to\infty$.
MaCLR: Motion-aware Contrastive Learning of Representations for Videos

We present MaCLR, a novel method to explicitly perform cross-modal self-supervised video representations learning from visual and motion modalities. Compared to previous video representation learning methods that mostly focus on learning motion cues implicitly from RGB inputs, MaCLR enriches standard contrastive learning objectives for RGB video clips with a cross-modal learning objective between a Motion pathway and a Visual pathway. We show that the representation learned with our MaCLR method focuses more on foreground motion regions and thus generalizes better to downstream tasks. To demonstrate this, we evaluate MaCLR on five datasets for both action recognition and action detection, and demonstrate state-of-the-art self-supervised performance on all datasets. Furthermore, we show that MaCLR representation can be as effective as representations learned with full supervision on UCF101 and HMDB51 action recognition, and even outperform the supervised representation for action recognition on VidSitu and SSv2, and action detection on AVA.
Backdoor Attacks on the DNN Interpretation System

Interpretability is crucial to understand the inner workings of deep neural networks (DNNs) and many interpretation methods generate saliency maps that highlight parts of the input image that contribute the most to the prediction made by the DNN. In this paper we design a backdoor attack that alters the saliency map produced by the network for an input image only with injected trigger that is invisible to the naked eye while maintaining the prediction accuracy. The attack relies on injecting poisoned data with a trigger into the training data set. The saliency maps are incorporated in the penalty term of the objective function that is used to train a deep model and its influence on model training is conditioned upon the presence of a trigger. We design two types of attacks: targeted attack that enforces a specific modification of the saliency map and untargeted attack when the importance scores of the top pixels from the original saliency map are significantly reduced. We perform empirical evaluation of the proposed backdoor attacks on gradient-based and gradient-free interpretation methods for a variety of deep learning architectures. We show that our attacks constitute a serious security threat when deploying deep learning models developed by untrusty sources. Finally, in the Supplement we demonstrate that the proposed methodology can be used in an inverted setting, where the correct saliency map can be obtained only in the presence of a trigger (key), effectively making the interpretation system available only to selected users.
On improving Cheng’s backpropagation for fully connectedcascade networks

Abstract In this journal, Cheng has proposed a backpropagation (BP) procedure calledBPFCC for deep L-stage fully connected cascaded (FCC) neural network learning in comparisonwith a neuron-by-neuron (NBN) algorithm of Wilamowski and Yu; yet, Cheng apparentlyneglected two major points of the NBN algorithm: (1) the merit of NBN is emphasizedon the multiple q-output (q &gt; 1) case; and (2) NBN is described for a q-outputFCC different in structure from Cheng’s FCC type. In nonlinear regression (for minimizingthe sum of squared residuals), NBN employs forward passes only on the q-output FCC toevaluate the Gauss-Newton (approximate Hessian) matrix ∇rT∇r, the cross product of theJacobian matrix ∇r of the residual vector r. Notably, both BPFCC and NBN are designedto reduce the cost for evaluating (q rows per datum of) the Jacobian matrix ∇r rather thanthe dominant cost for forming the cross product ∇rT∇r by rank updates. The purpose ofthis paper is to present a new BP procedure that exploits the special FCC network structureof Cheng’s type for reducing the dominant cost (with no rows of ∇r explicitly), evaluatingthe Gauss-Newton matrix efficiently in a block arrow matrix form when q &gt; 1.
Successor Representation Active Inference

Recent work has uncovered close links between between classical reinforcement learning algorithms, Bayesian filtering, and Active Inference which lets us understand value functions in terms of Bayesian posteriors. An alternative, but less explored, model-free RL algorithm is the successor representation, which expresses the value function in terms of a successor matrix of expected future state occupancies. In this paper, we derive the probabilistic interpretation of the successor representation in terms of Bayesian filtering and thus design a novel active inference agent architecture utilizing successor representations instead of model-based planning. We demonstrate that active inference successor representations have significant advantages over current active inference agents in terms of planning horizon and computational cost. Moreover, we demonstrate how the successor representation agent can generalize to changing reward functions such as variants of the expected free energy.
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks

It is hard to collect enough flaw images for training deep learning network in industrial production. Therefore, existing industrial anomaly detection methods prefer to use CNN-based unsupervised detection and localization network to achieve this task. However, these methods always fail when there are varieties happened in new signals since traditional end-to-end networks suffer barriers of fitting nonlinear model in high-dimensional space. Moreover, they have a memory library by clustering the feature of normal images essentially, which cause it is not robust to texture change. To this end, we propose the Vision Transformer based (VIT-based) unsupervised anomaly detection network. It utilizes a hierarchical task learning and human experience to enhance its interpretability. Our network consists of pattern generation and comparison networks. Pattern generation network uses two VIT-based encoder modules to extract the feature of two consecutive image patches, then uses VIT-based decoder module to learn the human designed style of these features and predict the third image patch. After this, we use the Siamese-based network to compute the similarity of the generation image patch and original image patch. Finally, we refine the anomaly localization by the bi-directional inference strategy. Comparison experiments on public dataset MVTec dataset show our method achieves 99.8% AUC, which surpasses previous state-of-the-art methods. In addition, we give a qualitative illustration on our own leather and cloth datasets. The accurate segment results strongly prove the accuracy of our method in anomaly detection.
BYEL : Bootstrap Your Emotion Latent

With the improved performance of deep learning, the number of studies trying to apply deep learning to human emotion analysis is increasing rapidly. But even with this trend going on, it is still difficult to obtain high-quality images and annotations. For this reason, the Learning from Synthetic Data (LSD) Challenge, which learns from synthetic images and infers from real images, is one of the most interesting areas. In general, Domain Adaptation methods are widely used to address LSD challenges, but there is a limitation that target domains (real images) are still needed. Focusing on these limitations, we propose a framework Bootstrap Your Emotion Latent (BYEL), which uses only synthetic images in training. BYEL is implemented by adding Emotion Classifiers and Emotion Vector Subtraction to the BYOL framework that performs well in Self-Supervised Representation Learning. We train our framework using synthetic images generated from the Aff-wild2 dataset and evaluate it using real images from the Aff-wild2 dataset. The result shows that our framework (0.3084) performs 2.8% higher than the baseline (0.3) on the macro F1 score metric.
Stabilised Inverse Flowline Evolution for Anisotropic Image Sharpening

The central limit theorem suggests Gaussian convolution as a generic blur model for images. Since Gaussian convolution is equivalent to homogeneous diffusion filtering, one way to deblur such images is to diffuse them backwards in time. However, backward diffusion is highly ill-posed. Thus, it requires stabilisation in the model as well as highly sophisticated numerical algorithms. Moreover, sharpening is often only desired across image edges but not along them, since it may cause very irregular contours. This creates the need to model a stabilised anisotropic backward evolution and to devise an appropriate numerical algorithm for this ill-posed process. We address both challenges. First we introduce stabilised inverse flowline evolution (SIFE) as an anisotropic image sharpening flow. Outside extrema, its partial differential equation (PDE) is backward parabolic in gradient direction. Interestingly, it is sufficient to stabilise it in extrema by imposing a zero flow there. We show that morphological derivatives - which are not common in the numerics of PDEs - are ideal for the numerical approximation of SIFE: They effortlessly approximate directional derivatives in gradient direction. Our scheme adapts one-sided morphological derivatives to the underlying image structure. It allows to progress in subpixel accuracy and enables us to prove stability properties. Our experiments show that SIFE allows nonflat steady states and outperforms other sharpening flows.
E-Graph: Minimal Solution for Rigid Rotation with Extensibility Graphs

Minimal solutions for relative rotation and translation estimation tasks have been explored in different scenarios, typically relying on the so-called co-visibility graph. However, how to build direct rotation relationships between two frames without overlap is still an open topic, which, if solved, could greatly improve the accuracy of visual odometry. In this paper, a new minimal solution is proposed to solve relative rotation estimation between two images without overlapping areas by exploiting a new graph structure, which we call Extensibility Graph (E-Graph). Differently from a co-visibility graph, high-level landmarks, including vanishing directions and plane normals, are stored in our E-Graph, which are geometrically extensible. Based on E-Graph, the rotation estimation problem becomes simpler and more elegant, as it can deal with pure rotational motion and requires fewer assumptions, e.g. Manhattan/Atlanta World, planar/vertical motion. Finally, we embed our rotation estimation strategy into a complete camera tracking and mapping system which obtains 6-DoF camera poses and a dense 3D mesh model. Extensive experiments on public benchmarks demonstrate that the proposed method achieves state-of-the-art tracking performance.
On a Generalized Framework for Time-Aware Knowledge Graphs

Knowledge graphs have emerged as an effective tool for managing and standardizing semistructured domain knowledge in a human- and machine-interpretable way. In terms of graph-based domain applications, such as embeddings and graph neural networks, current research is increasingly taking into account the time-related evolution of the information encoded within a graph. Algorithms and models for stationary and static knowledge graphs are extended to make them accessible for time-aware domains, where time-awareness can be interpreted in different ways. In particular, a distinction needs to be made between the validity period and the traceability of facts as objectives of time-related knowledge graph extensions. In this context, terms and definitions such as dynamic and temporal are often used inconsistently or interchangeably in the literature. Therefore, with this paper we aim to provide a short but well-defined overview of time-aware knowledge graph extensions and thus faciliate future research in this field as well.