Reviewerly/exHarmony huggingface dataset:
Quantitative features to assist in the diagnostic assessment of chronic lymphocytic leukemia progression<sup>†</sup>

The use of artificial intelligence methods in the image-based diagnostic assessment of hematological diseases is a growing trend in recent years. In these methods, the selection of quantitative features that describe cytological characteristics plays a key role. They are expected to add objectivity and consistency among observers to the geometric, color, or texture variables that pathologists usually interpret from visual inspection. In a recent paper in The Journal of Pathology, El Hussein, Chen et al proposed an algorithmic procedure to assist pathologists in the diagnostic evaluation of chronic lymphocytic leukemia (CLL) progression using whole-slide image analysis of tissue samples. The core of the procedure was a set of quantitative descriptors (biomarkers) calculated from the segmentation of cell nuclei, which was performed using a convolutional neural network. These biomarkers were based on clinical practice and easily calculated with reproducible tools. They were used as input to a machine learning algorithm that provided classification in one of the stages of CLL progression. Works like this can contribute to the integration into the workflow of clinical laboratories of automated diagnostic systems based on the morphological analysis of histological slides and blood smears. © 2021 The Pathological Society of Great Britain and Ireland.
Assessment of memory and reading

Difficulties in learning to read arise when trying to extract the meaning of text from print. Difficulties in spelling occur when writing or typing text to express what the writer is trying to communicate. A first guess from the armchair would be that such difficulties are due to a problem with memory somewhere along the line, or as a second guess, a problem with operations performed on that stored information.
Particle filters with auxiliary Markov transition. Application to crossover and to multitarget tracking

In multitarget tracking, many particle approximations are available to sample from the filtering density, with the effect that multitarget particles are obtained by discarding or replicating globally the existing multitarget particles, i.e. the particles for all the different targets are replicated from the same multitarget particle. A better design would be to produce shuffled multitarget particles such that the particle for each different target can be replicated from a different multitarget particle. An efficient solution has been proposed by Ubéda–Medina et al. under a posterior independence assumption that is almost never met in practical situations. The objective of this work is to propose another solution that does not rely on the posterior independence assumption. This new solution is based on introducing an auxiliary Markov transition, and is seen as an extension of the auxiliary particle filter.
Hamiltonian prior to Disentangle Content and Motion in Image Sequences

We present a deep latent variable model for high dimensional sequential data. Our model factorises the latent space into content and motion variables. To model the diverse dynamics, we split the motion space into subspaces, and introduce a unique Hamiltonian operator for each subspace. The Hamiltonian formulation provides reversible dynamics that learn to constrain the motion path to conserve invariant properties. The explicit split of the motion space decomposes the Hamiltonian into symmetry groups and gives long-term separability of the dynamics. This split also means representations can be learnt that are easy to interpret and control. We demonstrate the utility of our model for swapping the motion of two videos, generating sequences of various actions from a given image and unconditional sequence generation.
MPLR: a novel model for multi-target learning of logical rules for  knowledge graph reasoning

Large-scale knowledge graphs (KGs) provide structured representations of human knowledge. However, as it is impossible to contain all knowledge, KGs are usually incomplete. Reasoning based on existing facts paves a way to discover missing facts. In this paper, we study the problem of learning logic rules for reasoning on knowledge graphs for completing missing factual triplets. Learning logic rules equips a model with strong interpretability as well as the ability to generalize to similar tasks. We propose a model called MPLR that improves the existing models to fully use training data and multi-target scenarios are considered. In addition, considering the deficiency in evaluating the performance of models and the quality of mined rules, we further propose two novel indicators to help with the problem. Experimental results empirically demonstrate that our MPLR model outperforms state-of-the-art methods on five benchmark datasets. The results also prove the effectiveness of the indicators.
Generation of nonclassical states of light via truncation of mixed  states

A possible way of generating nonclassical states of light, especially non-Gaussian states, is via the truncation of a given state in the Fock basis. In recent work, we presented an alternative scheme for such quantum scissors [Phys. Rev. A 104, 033715 (2021)], employing a nondegenerate parametric amplifier, a beam splitter and photodetectors. An advantage of this setup is that it does not require the generation of Fock states beforehand, as in previous proposals. Here we extend this treatment to mixed input states. We show the possibilities of generating truncated states with either a maximum Fock number N or states having a minimum Fock number N. We discuss two specific examples of states to be truncated: i) the thermal state, and ii) the phase-diffused coherent state. In both cases, we show that the generated states can have significant sub-Poissonian statistics as well as non-Gaussian character. The degree of such nonclassical properties, as well as the success probabilities, can be changed by adjusting the parametric amplifier strength and the beam splitter transmittance.
Label-Efficient Semantic Segmentation with Diffusion Models

Denoising diffusion probabilistic models have recently received much research attention since they outperform alternative approaches, such as GANs, and currently provide state-of-the-art generative performance. The superior performance of diffusion models has made them an appealing tool in several applications, including inpainting, super-resolution, and semantic editing. In this paper, we demonstrate that diffusion models can also serve as an instrument for semantic segmentation, especially in the setup when labeled data is scarce. In particular, for several pretrained diffusion models, we investigate the intermediate activations from the networks that perform the Markov step of the reverse diffusion process. We show that these activations effectively capture the semantic information from an input image and appear to be excellent pixel-level representations for the segmentation problem. Based on these observations, we describe a simple segmentation method, which can work even if only a few training images are provided. Our approach significantly outperforms the existing alternatives on several datasets for the same amount of human supervision.
Hierarchical Optimal Transport for Unsupervised Domain Adaptation

In this paper, we propose a novel approach for unsupervised domain adaptation, that relates notions of optimal transport, learning probability measures and unsupervised learning. The proposed approach, HOT-DA, is based on a hierarchical formulation of optimal transport, that leverages beyond the geometrical information captured by the ground metric, richer structural information in the source and target domains. The additional information in the labeled source domain is formed instinctively by grouping samples into structures according to their class labels. While exploring hidden structures in the unlabeled target domain is reduced to the problem of learning probability measures through Wasserstein barycenter, which we prove to be equivalent to spectral clustering. Experiments on a toy dataset with controllable complexity and two challenging visual adaptation datasets show the superiority of the proposed approach over the state-of-the-art.
Simple Adaptive Projection with Pretrained Features for Anomaly  Detection

Deep anomaly detection aims to separate anomaly from normal samples with high-quality representations. Pretrained features bring effective representation and promising anomaly detection performance. However, with one-class training data, adapting the pretrained features is a thorny problem. Specifically, the existing optimization objectives with global target often lead to pattern collapse, i.e. all inputs are mapped to the same. In this paper, we propose a novel adaptation framework including simple linear transformation and self-attention. Such adaptation is applied on a specific input, and its k nearest representations of normal samples in pretrained feature space and the inner-relationship between similar one-class semantic features are mined. Furthermore, based on such framework, we propose an effective constraint term to avoid learning trivial solution. Our simple adaptive projection with pretrained features(SAP2) yields a novel anomaly detection criterion which is more accurate and robust to pattern collapse. Our method achieves state-of-the-art anomaly detection performance on semantic anomaly detection and sensory anomaly detection benchmarks including 96.5% AUROC on CIFAR-100 dataset, 97.0% AUROC on CIFAR-10 dataset and 88.1% AUROC on MvTec dataset.
Embedding Arithmetic for Text-driven Image Transformation

Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man. Such structured semantic relations were not demonstrated on image representations. Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality. We introduce the SIMAT dataset to evaluate the task of text-driven image transformation. SIMAT contains 6k images and 18k "transformation queries" that aim at either replacing scene elements or changing their pairwise relationships. The goal is to retrieve an image consistent with the (source image, transformation) query. We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful. The SIMAT dataset will be publicly available. We use SIMAT to show that vanilla CLIP multimodal embeddings are not very well suited for text-driven image transformation, but that a simple finetuning on the COCO dataset can bring dramatic improvements. We also study whether it is beneficial to leverage the geometric properties of pretrained universal sentence encoders (FastText, LASER and LaBSE).
Score-Based Generative Modeling with Critically-Damped Langevin  Diffusion

Score-based generative models (SGMs) have demonstrated remarkable synthesis quality. SGMs rely on a diffusion process that gradually perturbs the data towards a tractable distribution, while the generative model learns to denoise. The complexity of this denoising task is, apart from the data distribution itself, uniquely determined by the diffusion process. We argue that current SGMs employ overly simplistic diffusions, leading to unnecessarily complex denoising processes, which limit generative modeling performance. Based on connections to statistical mechanics, we propose a novel critically-damped Langevin diffusion (CLD) and show that CLD-based SGMs achieve superior performance. CLD can be interpreted as running a joint diffusion in an extended space, where the auxiliary variables can be considered "velocities" that are coupled to the data variables as in Hamiltonian dynamics. We derive a novel score matching objective for CLD and show that the model only needs to learn the score function of the conditional distribution of the velocity given data, an easier task than learning scores of the data directly. We also derive a new sampling scheme for efficient synthesis from CLD-based diffusion models. We find that CLD outperforms previous SGMs in synthesis quality for similar network architectures and sampling compute budgets. We show that our novel sampler for CLD significantly outperforms solvers such as Euler--Maruyama. Our framework provides new insights into score-based denoising diffusion models and can be readily used for high-resolution image synthesis. Project page and code: https://nv-tlabs.github.io/CLD-SGM.
An Optimized Bidirectional Quantum Teleportation Scheme with the use of  Bell states

Bidirectional quantum teleportation scheme is a two-way quantum communication process, in which two parties simultaneously receive each other information. Recently, in paper [1] (Zhou et al., IEEE Access, 7, 44269 (2019)), a six-qubit cluster state has been used to teleport three-qubit entangled state (Alice) and arbitrary single-qubit unknown quantum state (Bob) bidirectionally. An experimental point of view, the preparation and the maintenance of cluster type entangled state is very difficult and costly. So, in this paper we have designed an optimal scheme for bidirectional quantum teleportation scheme and shown that proposed scheme requires optimized amount of quantum resources.
Entanglement Levels Up

Extending a key resource of quantum technology, entanglement—either by having more than two quantum objects or using "qudits" instead of "qubits"-offers prospects for improvements in quantum communications, computation and other technologies.
Proximal policy optimization with model-based methods

Model-free reinforcement learning methods have successfully been applied to practical applications such as decision-making problems in Atari games. However, these methods have inherent shortcomings, such as a high variance and low sample efficiency. To improve the policy performance and sample efficiency of model-free reinforcement learning, we propose proximal policy optimization with model-based methods (PPOMM), a fusion method of both model-based and model-free reinforcement learning. PPOMM not only considers the information of past experience but also the prediction information of the future state. PPOMM adds the information of the next state to the objective function of the proximal policy optimization (PPO) algorithm through a model-based method. This method uses two components to optimize the policy: the error of PPO and the error of model-based reinforcement learning. We use the latter to optimize a latent transition model and predict the information of the next state. For most games, this method outperforms the state-of-the-art PPO algorithm when we evaluate across 49 Atari games in the Arcade Learning Environment (ALE). The experimental results show that PPOMM performs better or the same as the original algorithm in 33 games.
The Effects of Detector Efficiency when Heralding on the Detection of Zero Photons

Reliable detection of zero photons is important for quantum information applications but presents unique experimental challenges. We demonstrate the role of detector efficiency when “heralding on zero” using a modified Hong-Ou-Mandel interferometer.
Peer review declaration

All conference organisers/editors are required to declare details about their peer review. Therefore, please provide the following information: • Type of peer review: Single-blind / Double-blind / Triple-blind / Open / Other All papers have been reviewed by at least two reviewers and follow a double-blind peer review process. For the peer review, an editor has been assigned to the submitted paper as per the major theme of the conference, i.e., design engineering, thermal engineering and fluid science, material science and nanomaterials, production and industrial engineering. Four editors have been handling the review process by assigning two domain expert reviewers to each paper. The papers have been evaluated as per the quality criteria of IOP Publishing and the decisions were sent to the authors, such as: strong accept, accept, weak accept, borderline paper, and reject. The weakly accepted and borderline papers were sent back to authors for revision and resubmission was done through EasyChair. The resubmitted paper was again evaluated by the editor, and a final decision has been conveyed to the author. • Conference submission management system : Easychair Easychair was chosen as the conference paper submission and review management system for ICRAMEN 2021 . All papers have been submitted and also reviewed by the Easychair account only. A revised submission was also accepted only by the easychair account. All email correspondence with the author was done through easychair email and also from conference@rame.org.in. • Number of submissions received : 52 We have received a total of 52 papers, out of which accepted and registered papers have been considered for presentation at the conference and publication in a journal. • Number of submissions sent for review: 50 Two papers were not considered for review due to late submission and was rejected by the editor. • Number of submissions accepted : 28 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100) : 54% • Average number of reviews per paper : 2.2 • Total number of reviewers involved : 23 • Any additional info on review process : 1. The Plagiarism Checker X software has been used to check the similarity index of the papers. The similarity index of all the papers is not more than 20%. 2. ICRAMEN 2021 was a virtual conference, with all presentations delivered via the Zoom online platform. 3. All papers have been evaluated by the editor and reviewed by a minimum of two reviewers. Also, some papers are sent back to the author for further revision after the presentation at the conference to amend the corrections suggested by the session chair. • Contact person for queries : Dr. Manoj A. Kumbhalkar, Conference Chair, ICRAMEN 2021. President, Research Association of Masters of Engineering (RAME), India. manoj.kumbhalkar@rame.org.in, manoj.kumbhalkar@rediffmail.com
Workflow scheduling in cloud computing environment with classification ordinal optimisation using SVM

Every day, civilisation generates more and more data. The processing cost and performance issues of this massive set of data have become a challenge in distributed computing. Processing multitasks workloads for big-data in a dynamic environment requires real-time scheduling and the additional complication of generating optimal schedules in a large search space with high overhead. In this paper, we propose an adaptive workflow management system that uses ordinal optimisation to acquire suboptimal schedules in much less time. We then introduce a prediction-based workflow scheduler model that predicts the execution time of the next coming workflow by using a support vector machine (SVM). We used a real application Montage workflow for large-volume image data, and the experimental results show that our classification ordinal optimisation (COO) outperforms other existing methods.
Using Tea Leaves and Adsorption Method for Potable Water Treatment

The study aims to treat potable water using waste material (tealeaves) and adsorption method. Some water samples were chosed from varied regions in Baghdad city. The samples were exposed to “pH, conductivity, turbidity, total dissolved solids (TDS), and total suspended solids (TSS)” tests. Before performing the tealeaves treatment to the potable water, the pH and the conductivity values were acceptable per the Iraqi slandered. Adsorption and filtration methods were carried out using tealeaves as an adsorbent material for all potable water regions. The results showed that the adsorption process did not affect the pH and the conductivity values. In contrast, the turbidity values for all regions were decreased, and the TDS and TSS were minimized. As a result, Tealeaves is very effectible and successful in minimizing or eliminating TDS, TSS, and turbidity in treating potable water using the adsorption method.
Quantum fools

Some Twitter comments in response to the April Fool’s Day article “Zoom video call is powered by Google’s quantum computer” (bit.ly/2Q2mi8e) in which we joked about participants being both muted and unmuted simultaneously.
Public-private partnerships and collaboration in digital health: a scoping review and case study analysis of applications during the COVID-19 pandemic (Preprint)

<sec> <title>BACKGROUND</title> The emergence of digital technologies over the past decade has presented a novel opportunity to address healthcare challenges associated with COVID-19 and accelerate progress towards achieving the health-related goals under the 2030 Sustainable Development Agenda. Public-private partnerships (PPPs) have played a vital role in scaling up digital health solutions and disseminating curated scientific information in the face of the infodemic. However, several challenges remain around the effectiveness of PPP-related digital solutions and antagonistic viewpoints of engaging the private sector. We sought to evaluate the role of public-private partnerships in the digital public health space during COVID-19 and identify key lessons learned and challenges in the uptake of digital health solutions globally. </sec> <sec> <title>OBJECTIVE</title> Electronic and grey literature search results from PubMed, Google, and Google Scholar were screened by one reviewer through a two-stage process. We included all relevant systematic reviews, interventional, observational, and descriptive studies published in English published from January 2020 to June 2021. Two case study analyses on digital health chatbots, informed by expert opinion, were also performed to assess for the role of public-private partnerships in advancing digital public health solutions. </sec> <sec> <title>METHODS</title> Electronic and grey literature search results from PubMed, Google, and Google Scholar were screened by one reviewer through a two-stage process. We included all relevant systematic reviews, interventional, observational, and descriptive studies published in English published from January 2020 to June 2021. Two case study analyses on digital health chatbots, informed by expert opinion, were also performed to assess for the role of public-private partnerships in advancing digital public health solutions. </sec> <sec> <title>RESULTS</title> Forty-five articles met the inclusion criteria for qualitative analysis, the majority of which were secondary research. Results of the publications can be broadly categorized into three groups: (1) models and definitions of public-private partnerships used in the healthcare space; (2) purposes of and motivations of public-private partnerships in global public health; and (3) and facilitators, barriers, and challenges to date. </sec> <sec> <title>CONCLUSIONS</title> The literature review as well as the case studies analysis reveal that PPPs can represent a valid option for tackling global healthcare issues with a digital health approach. Further research is needed to complement the initial findings of the present paper, as well as to assess a wider pool of case studies and the different features that they might present. </sec>
An Efficient Methodologies for Data Integrity in Cloud Storage

Majority of the organization uses cloud for storage purpose in order to reduce the cost as well as maintenance. Due to increasing threat from internal and external sources, there would be possibility of corruption in the cloud storage files. Thus the storage must to be monitored periodically for integrity checking. Since most of the Data Owners have limited resources thus the responsibility of integrity checking goes to the Third Party Auditors (TPA). In this paper, we have proposed 2 methodologies of Integrity Checking in Cloud Storage (1) Enhanced Dynamic Hash Tree – n Versions (EDHT-n), which has best performance in term of time and space complexity compared to the existing methods.(2) Hybrid Enhanced Dynamic Hash Tree (HEDHT), which is best suited for very huge number of files in a directory.