Reviewerly/exHarmony huggingface dataset:
Single Image Deblurring with Row-dependent Blur Magnitude

Image degradation often occurs during fast camera or object movements, regardless of the exposure modes: global shutter (GS) or rolling shutter (RS). Since these two exposure modes give rise to intrinsically different degradations, two restoration threads have been explored separately, i.e. motion deblurring of GS images and distortion correction of RS images, both of which are challenging restoration tasks, especially in the presence of a single input image. In this paper, we explore a novel in-between exposure mode, called global reset release (GRR) shutter, which produces GS-like blur but with row-dependent blur magnitude. We take advantage of this unique characteristic of GRR to explore the latent frames within a single image and restore a clear counterpart by only relying on these latent contexts. Specifically, we propose a residual spatially-compensated and spectrally-enhanced Transformer (RSS-T) block for row-dependent deblurring of a single GRR image. Its hierarchical positional encoding compensates global positional context of windows and enables order-awareness of the local pixel’s position, along with a novel feed-forward network that simultaneously uses spatial and spectral information for gaining mixed global context. Extensive experimental results demonstrate that our method outperforms the state-of-the-art GS deblurring and RS correction methods on single GRR input.
DDG-Net: Discriminability-Driven Graph Network for Weakly-supervised Temporal Action Localization

Weakly-supervised temporal action localization (WTAL) is a practical yet challenging task. Due to large-scale datasets, most existing methods use a network pretrained in other datasets to extract features, which are not suitable enough for WTAL. To address this problem, researchers design several modules for feature enhancement, which improve the performance of the localization module, especially modeling the temporal relationship between snippets. However, all of them omit that ambiguous snippets deliver contradictory information, which would reduce the discriminability of linked snippets. Considering this phenomenon, we propose Discriminability-Driven Graph Network (DDG-Net), which explicitly models ambiguous snippets and discriminative snippets with well-designed connections, preventing the transmission of ambiguous information and enhancing the discriminability of snippet-level representations. Additionally, we propose feature consistency loss to prevent the assimilation of features and drive the graph convolution network to generate more discriminative representations. Extensive experiments on THUMOS14 and ActivityNet1.2 benchmarks demonstrate the effectiveness of DDG-Net, establishing new state-of-the-art results on both datasets. Source code is available at https://github.com/XiaojunTang22/ICCV2023-DDGNet.
Generative Novel View Synthesis with 3D-Aware Diffusion Models

We present a diffusion-based model for 3D-aware generative novel view synthesis from as few as a single input image. Our model samples from the distribution of possible renderings consistent with the input and, even in the presence of ambiguity, is capable of rendering diverse and plausible novel views. To achieve this, our method makes use of existing 2D diffusion backbones but, crucially, incorporates geometry priors in the form of a 3D feature volume. This latent feature field captures the distribution over possible scene representations and improves our method’s ability to generate view-consistent novel renderings. In addition to generating novel views, our method has the ability to autoregressively synthesize 3D-consistent sequences. We demonstrate state-of-the-art results on synthetic renderings and room-scale scenes; we also show compelling results for challenging, real-world objects.
Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery

In this paper, we address the problem of generalized category discovery (GCD), i.e., given a set of images where part of them are labelled and the rest are not, the task is to automatically cluster the images in the unlabelled data, leveraging the information from the labelled data, while the unlabelled data contain images from the labelled classes and also new ones. GCD is similar to semi-supervised learning (SSL) but is more realistic and challenging, as SSL assumes all the unlabelled images are from the same classes as the labelled ones. We also do not assume the class number in the unlabelled data is known a-priori, making the GCD problem even harder. To tackle the problem of GCD without knowing the class number, we propose an EM-like framework that alternates between representation learning and class number estimation. We propose a semi-supervised variant of the Gaussian Mixture Model (GMM) with a stochastic splitting and merging mechanism to dynamically determine the prototypes by examining the cluster compactness and separability. With these prototypes, we leverage prototypical contrastive learning for representation learning on the partially labelled data subject to the constraints imposed by the labelled data. Our framework alternates between these two steps until convergence. The cluster assignment for an unlabelled instance can then be retrieved by identifying its nearest prototype. We comprehensively evaluate our framework on both generic image classification datasets and challenging fine-grained object recognition datasets, achieving state-of-the-art performance. Our code is available at https://github.com/DTennant/GPC.
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning

Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales. Such models overlook scale-specific information in the data for scale-dependent domains, such as remote sensing. In this paper, we present Scale-MAE, a pretraining method that explicitly learns relationships between data at different, known scales throughout the pretraining process. Scale-MAE pre-trains a network by masking an input image at a known input scale, where the area of the Earth covered by the image determines the scale of the ViT positional encoding, not the image resolution. Scale-MAE encodes the masked image with a standard ViT backbone, and then decodes the masked image through a bandpass filter to reconstruct low/high frequency images at lower/higher scales. We find that tasking the network with reconstructing both low/high frequency images leads to robust multiscale representations for remote sensing imagery. Scale-MAE achieves an average of a 2.4 − 5.6% non-parametric kNN classification improvement across eight remote sensing datasets compared to current state-of-the-art and obtains a 0.9 mIoU to 1.7 mIoU improvement on the SpaceNet building segmentation transfer task for a range of evaluation scales.
A quantum-based oversampling method for classification of highly imbalanced and overlapped data

Data imbalance is a challenging problem in classification tasks, and when combined with class overlapping, it further deteriorates classification performance. However, existing studies have rarely addressed both issues simultaneously. In this article, we propose a novel quantum-based oversampling method (QOSM) to effectively tackle data imbalance and class overlapping, thereby improving classification performance. QOSM utilizes the quantum potential theory to calculate the potential energy of each sample and selects the sample with the lowest potential as the center of each cover generated by a constructive covering algorithm. This approach optimizes cover center selection and better captures the distribution of the original samples, particularly in the overlapping regions. In addition, oversampling is performed on the samples of the minority class covers to mitigate the imbalance ratio (IR). We evaluated QOSM using three traditional classifiers (support vector machines [SVM], k-nearest neighbor [KNN], and naive Bayes [NB] classifier) on 10 publicly available KEEL data sets characterized by high IRs and varying degrees of overlap. Experimental results demonstrate that QOSM significantly improves classification accuracy compared to approaches that do not address class imbalance and overlapping. Moreover, QOSM consistently outperforms existing oversampling methods tested. With its compatibility with different classifiers, QOSM exhibits promising potential to improve the classification performance of highly imbalanced and overlapped data.
The use of technology in facing the COVID-19 negative consequences and the associated opportunity for digital entrepreneurship in KSA

Purpose This article introduces the key findings from investigating technology's role in mitigating the business slowdown enforced by the COVID-19 pandemic and the associated increased reliance on technological means among Saudi firms and citizens to facilitate business operations and other daily life routines, and the impact of this increase in technology adoption on the Saudi digital ecosystem and creating an inviting environment to digital entrepreneurship. Design/methodology/approach A qualitative interview methodology is undertaken to understand Saudi managers’ perceptions about the COVID-19 pandemic and digital entrepreneurship. Findings A significant finding is that now than ever before, the Saudi market is ready to accommodate more digital entrepreneurial ventures and digitalisation support services. Due to the pandemic's negative implications on the Saudi economy and the business slowdown associated with social distancing measures, a substantial increase in digital orientation and a need for technological solutions were noticed among Saudi firms. This means more attention needs to be placed on how Saudi firms can capitalise on the knowledge economy and digital revolution. Originality/value Saudi citizens have become more reliant on technology to manage daily activities and shop for their needs, creating opportunities for digital entrepreneurship to serve and fulfil firms' and people's increased demand for technology solutions.
Aspect-based Argument Generation in Russian

The paper explores the argument generation in Russian based on given aspects. An aspect refers to one of the sides or property of the target object. Five aspects were considered: "Safety", "Impact on health", "Reliability", "Money", "Convenience and comfort". Various approaches were used for aspect-based generation: fine-tuning, prompt-tuning and few-shot learning. The ruGPT-3Large model was used for experiments. The results show that traditionally trained model (with fine-tuning) generates 51.6% of the arguments on given aspects, with the prompttuning approach – 33.9%, and with few-shot learning – 10.6%. The model also demonstrated the ability to generate arguments on new, previously unknown aspects.
Higher Education Institutions Educational Process Digitalization in the Context of the Necessity to Provide a Model for Students' Choice of Training Areas and Academic Specialties

The digital revolution in the Russian economy is leading to a radical change in the labour market, the emergence of new competencies, improved cooperation, increased responsibility of citizens, their ability to make independent decisions. This, in turn, is the reason for the reorganisation of the educational process, largely based on the use of IT technologies. The purpose of the paper is to solve the problem of unification of educational programmes within the framework of digitalisation of the educational process in universities. The unification of educational programmes is proposed, which ensures the conducting of the educational process without losing its quality, taking into account individual educational trajectories, as well as implementing a model for students' choice of a direction or training specialty, starting from the third year of study, and, as a result, making it possible to transfer students between educational levels up to 5 semesters, and within a level - up to 6 semesters. The unification of educational programmes is considered on the example of the Moscow Aviation Institute (National Research University). The proposed unification covers the extended groups of specialties taking into account different levels and forms of training, as well as various territorial sites (branches).
Continuous-Time Decision Transformer for Healthcare Applications.

Offline reinforcement learning (RL) is a promising approach for training intelligent medical agents to learn treatment policies and assist decision making in many healthcare applications, such as scheduling clinical visits and assigning dosages for patients with chronic conditions. In this paper, we investigate the potential usefulness of Decision Transformer (Chen et al., 2021)-a new offline RL paradigm-in medical domains where decision making in continuous time is desired. As Decision Transformer only handles discrete-time (or turn-based) sequential decision making scenarios, we generalize it to Continuous-Time Decision Transformer that not only considers the past clinical measurements and treatments but also the timings of previous visits, and learns to suggest the timings of future visits as well as the treatment plan at each visit. Extensive experiments on synthetic datasets and simulators motivated by real-world medical applications demonstrate that Continuous-Time Decision Transformer is able to outperform competitors and has clinical utility in terms of improving patients' health and prolonging their survival by learning high-performance policies from logged data generated using policies of different levels of quality.
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?

Humans learn from visual inputs at multiple timescales, both rapidly and flexibly acquiring visual knowledge over short periods, and robustly accumulating online learning progress over longer periods. Modeling these powerful learning capabilities is an important problem for computational visual cognitive science, and models that could replicate them would be of substantial utility in real-world computer vision settings. In this work, we establish benchmarks for both real-time and life-long continual visual learning. Our real-time learning benchmark measures a model's ability to match the rapid visual behavior changes of real humans over the course of minutes and hours, given a stream of visual inputs. Our life-long learning benchmark evaluates the performance of models in a purely online learning curriculum obtained directly from child visual experience over the course of years of development. We evaluate a spectrum of recent deep self-supervised visual learning algorithms on both benchmarks, finding that none of them perfectly match human performance, though some algorithms perform substantially better than others. Interestingly, algorithms embodying recent trends in self-supervised learning - including BYOL, SwAV and MAE - are substantially worse on our benchmarks than an earlier generation of self-supervised algorithms such as SimCLR and MoCo-v2. We present analysis indicating that the failure of these newer algorithms is primarily due to their inability to handle the kind of sparse low-diversity datastreams that naturally arise in the real world, and that actively leveraging memory through negative sampling - a mechanism eschewed by these newer algorithms - appears useful for facilitating learning in such low-diversity environments. We also illustrate a complementarity between the short and long timescales in the two benchmarks, showing how requiring a single learning algorithm to be locally context-sensitive enough to match real-time learning changes while stable enough to avoid catastrophic forgetting over the long term induces a trade-off that human-like algorithms may have to straddle. Taken together, our benchmarks establish a quantitative way to directly compare learning between neural networks models and human learners, show how choices in the mechanism by which such algorithms handle sample comparison and memory strongly impact their ability to match human learning abilities, and expose an open problem space for identifying more flexible and robust visual self-supervision algorithms.
Online course: Investment banking

This course reviews the basic structure of an investment bank, including the basic activities of the different functional areas.
Solving Support Vector Machine with Many Examples

Various methods of dealing with linear support vector machine (SVM) problems with a large number of examples are presented and compared. The author believes that some interesting conclusions from this critical analysis applies to many new optimization problems and indicates in which direction the science of optimization will branch in the future. This direction is driven by the automatic collection of large data to be analyzed, and is most visible in telecommunications. A stream SVM approach is proposed, in which the data substantially exceeds the available fast random access memory (RAM) due to a large number of examples. Formally, the use of RAM is constant in the number of examples (though usually it depends on the dimensionality of the examples space). It builds an inexact polynomial model of the problem. Another author’s approach is exact. It also uses a constant amount of RAM but also auxiliary disk files, that can be long but are smartly accessed. This approach bases on the cutting plane method, similarly as Joachims’ method (which, however, relies on early finishing the optimization).
Learning Interrogation Strategies while Considering Deceptions in Detective Interactive Stories

The strategies for interactive characters to select appropriate dialogues remain as an open issue in related research areas. In this paper we propose an approach based on reinforcement learning to learn the strategy of interrogation dialogue from one virtual agent toward another. The emotion variation of the suspect agent is modeled with a hazard function, and the detective agent must learn its interrogation strategies based on the emotion state of the suspect agent. The reinforcement learning reward schemes are evaluated to choose the proper reward in the dialogue. Our contribution is twofold. Firstly, we proposed a new framework of reinforcement learning to model dialogue strategies. Secondly, background knowledge and emotion states of agents are brought into the dialogue strategies. The resulted dialogue strategy in our experiment is sensitive in detecting lies from the suspect, and with it the interrogator may receive more correct answer.
Tech, Regulatory Arbitrage, and Limits

Regulatory arbitrage refers to structuring activity to take advantage of gaps or differences in regulations or laws. Examples include Facebook modifying its terms and conditions to reduce the exposure of its user data to strict European privacy laws, and Uber and other platform companies organizing their affairs to categorize workers as non-employees. This article explores the constraints and limits on regulatory arbitrage through the lens of the technology industry, known for its adaptiveness and access to strategic resources. Specifically, the article explores social license and the bundling of laws and resources as constraining forces on regulatory arbitrage, and the legal mismatch that can arise from new business models and innovations as a key area in which the limits of regulatory arbitrage can be observed.
A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

Negation detection is an important task in biomedical text mining. Particularly in clinical settings, it is of critical importance to determine whether findings mentioned in text are present or absent. Rule-based negation detection algorithms are a common approach to the task, and more recent investigations have resulted in the development of rule-based systems utilising the rich grammatical information afforded by typed dependency graphs. However, interacting with these complex representations inevitably necessitates complex rules, which are time-consuming to develop and do not generalise well. We hypothesise that a heuristic approach to determining negation via dependency graphs could offer a powerful alternative. We describe and implement an algorithm for negation detection based on grammatical distance from a negatory construct in a typed dependency graph. To evaluate the algorithm, we develop two testing corpora comprised of sentences of clinical text extracted from the MIMIC-III database and documents related to hypertrophic cardiomyopathy patients routinely collected at University Hospitals Birmingham NHS trust. Gold-standard validation datasets were built by a combination of human annotation and examination of algorithm error. Finally, we compare the performance of our approach with four other rule-based algorithms on both gold-standard corpora. The presented algorithm exhibits the best performance by f-measure over the MIMIC-III dataset, and a similar performance to the syntactic negation detection systems over the HCM dataset. It is also the fastest of the dependency-based negation systems explored in this study. Our results show that while a single heuristic approach to dependency-based negation detection is ignorant to certain advanced cases, it nevertheless forms a powerful and stable method, requiring minimal training and adaptation between datasets. As such, it could present a drop-in replacement or augmentation for many-rule negation approaches in clinical text-mining pipelines, particularly for cases where adaptation and rule development is not required or possible.
Image Steganalysis via Diverse Filters and Squeeze-and-Excitation Convolutional Neural Network

Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years. However, the diversity of filters has not been fully utilized in the current research. This paper constructs a new effective network with diverse filter modules (DFMs) and squeeze-and-excitation modules (SEMs), which can better capture the embedding artifacts. As the essential parts, combining three different scale convolution filters, DFMs can process information diversely, and the SEMs can enhance the effective channels out from DFMs. The experiments presented that our CNN is effective against content-adaptive steganographic schemes with different payloads, such as S-UNIWARD and WOW algorithms. Moreover, some state-of-the-art methods are compared with our approach to demonstrate the outstanding performance.
A polarization encoded photon-to-spin interface

We propose an integrated photonics device for mapping qubits encoded in the polarization of a photon onto the spin state of a solid-state defect coupled to a photonic crystal cavity: a `Polarization-Encoded Photon-to-Spin Interface' (PEPSI). We perform a theoretical analysis of the state fidelity's dependence on the device's polarization extinction ratio and atom-cavity cooperativity. Furthermore, we explore the rate-fidelity trade-off through analytical and numerical models. In simulation, we show that our design enables efficient, high fidelity photon-to-spin mapping.