Reviewerly/exHarmony huggingface dataset:
Holography and the KKLT Scenario

The KKLT scenario, one of the few ideas to realize dS vacua in string theory, consists of two steps: the first involves the construction of a supersymmetric AdS vacuum with a small negative cosmological constant, and the second involves breaking supersymmetry and uplifting the energy to achieve dS. In this paper we use conventional holography to argue why it is not possible to complete the first step. We obtain this by putting a bound on the central charge of the dual theory which involves branes wrapping special Lagrangian cycles in CY 4-folds. We find that $l_{\rm AdS}^2 \lesssim χ(CY_4)$. Since $l_{\rm species}^2\gtrsim χ(CY_4)$ this leads to $l_{\rm AdS}/l_{\rm species}\lesssim 1$ leading at best to a highly curved AdS which is beyond the validity of the EFT.
Residual Swin Transformer Channel Attention Network for Image Demosaicing

Image demosaicing is problem of interpolating full- resolution color images from raw sensor (color filter array) data. During last decade, deep neural networks have been widely used in image restoration, and in particular, in demosaicing, attaining significant performance improvement. In recent years, vision transformers have been designed and successfully used in various computer vision applications. One of the recent methods of image restoration based on a Swin Transformer (ST), SwinIR, demonstrates state-of-the-art performance with a smaller number of parameters than neural network-based methods. Inspired by the success of SwinIR, we propose in this paper a novel Swin Transformer-based network for image demosaicing, called RSTCANet. To extract image features, RSTCANet stacks several residual Swin Transformer Channel Attention blocks (RSTCAB), introducing the channel attention for each two successive ST blocks. Extensive experiments demonstrate that RSTCANet out- performs state-of-the-art image demosaicing methods, and has a smaller number of parameters.
Image Restoration using Deep Learning Techniques

In the modern era, due to the emergence of various technologies, most of the human work is now being performed by the computer system. The computer’s capacity to make everything possible is increasing as by the time. Photos are used to capture or freeze the moments in one’s life. We can embrace those moments at any time by looking at the pictures. It is natural that, as time passes by, these photos gets damaged due to environmental conditions that leads to loss of our important moments. Hence, preserving the photos is as important as taking them. The process of taking corrupt or noisy image and estimating the clean, original image is image restoration. Many forms of noise such as motion blur, camera misfocus etc., increases the complexity to restore the image. Image corruption comes in varying degrees of severity, the complexity of restoring photos in real-world applications will likewise vary greatly. Also, manual restoration is time consuming leading to lots of work to be piled up. To increase the capability of restoring old images from various defects, we must address several degradations intermingled in one old photo, such as structural defects like scratches and dust spots, and unstructured defects like sounds and blurriness. Furthermore, we may use a different face refinement network to restore small details of faces in ancient pictures, resulting in higher-quality photos. The aim of the work is to create a image restoration system that will be used to restore the images irrespective of the type of noise. In this paper, we present a model that would take image as an input and remove all the noises present in it to give a clean and restored image.
A Multitask Deep Learning Framework for DNER

Over the years, the explosive growth of drug-related text information has resulted in heavy loads of work for manual data processing. However, the domain knowledge hidden is believed to be crucial to biomedical research and applications. In this article, the multi-DTR model that can accurately recognize drug-specific name by joint modeling of DNER and DNEN was proposed. Character features were extracted by CNN out of the input text, and the context-sensitive word vectors were obtained using ELMo. Next, the pretrained biomedical words were embedded into BiLSTM-CRF and the output labels were interacted to update the task parameters until DNER and DNEN would support each other. The proposed method was found with better performance on the DDI2011 and DDI2013 datasets.
Cocoercivity, smoothness and bias in variance-reduced stochastic gradient methods

With the purpose of examining biased updates in variance-reduced stochastic gradient methods, we introduce SVAG, a SAG/SAGA-like method with adjustable bias. SVAG is analyzed in a cocoercive root-finding setting, a setting which yields the same results as in the usual smooth convex optimization setting for the ordinary proximal-gradient method. We show that the same is not true for SVAG when biased updates are used. The step-size requirements for when the operators are gradients are significantly less restrictive compared to when they are not. This highlights the need to not rely solely on cocoercivity when analyzing variance-reduced methods meant for optimization. Our analysis either match or improve on previously known convergence conditions for SAG and SAGA. However, in the biased cases they still do not correspond well with practical experiences and we therefore examine the effect of bias numerically on a set of classification problems. The choice of bias seem to primarily affect the early stages of convergence and in most cases the differences vanish in the later stages of convergence. However, the effect of the bias choice is still significant in a couple of cases.
Agribuzz-Agriculture Management System

Abstract: Farming is the Prime Occupation in India in spite of this; today the people involved in farming belong to the lower class and are in deep poverty. The Advanced techniques and the Automated machines which are leading the world to new heights are been lagging when it is concerned to Farming, either the lack of awareness of the advanced facilities or the unavailability leads to poverty in Farming. Even after all the hard work and the production done by the farmers, in today’s market, the farmers are cheated by the Agents, leading to poverty. Agro-marketing would make all things automatic which makes it easier to serve as the best solution to all the problems. Agribuzz farming will serve as a way for the farmers to sell their products across the country just with some basic knowledge about how to use the website. The site will guide the farmers in all aspects, including the current market rate of different products, the total sale and the earned profit for the sold products, access to the new farming techniques through learning, and a centralized approach to view different governments' agriculture schemes including the compensation schemes for farming. Getting availed of the required information related to the markets and different products can be made possible through the SMS facility provided by the system. Keywords: Website, farm-marketing, market rate, bill, eLearning, SMS facility.
Global Counterfactual Explanations: Investigations, Implementations and Improvements

Counterfactual explanations have been widely studied in explainability, with a range of application dependent methods emerging in fairness, recourse and model understanding. However, the major shortcoming associated with these methods is their inability to provide explanations beyond the local or instance-level. While some works touch upon the notion of a global explanation, typically suggesting to aggregate masses of local explanations in the hope of ascertaining global properties, few provide frameworks that are either reliable or computationally tractable. Meanwhile, practitioners are requesting more efficient and interactive explainability tools. We take this opportunity to investigate existing global methods, with a focus on implementing and improving Actionable Recourse Summaries (AReS), the only known global counterfactual explanation framework for recourse.
Data-heterogeneity-aware Mixing for Decentralized Learning

Decentralized learning provides an effective framework to train machine learning models with data distributed over arbitrary communication graphs. However, most existing approaches toward decentralized learning disregard the interaction between data heterogeneity and graph topology. In this paper, we characterize the dependence of convergence on the relationship between the mixing weights of the graph and the data heterogeneity across nodes. We propose a metric that quantifies the ability of a graph to mix the current gradients. We further prove that the metric controls the convergence rate, particularly in settings where the heterogeneity across nodes dominates the stochasticity between updates for a given node. Motivated by our analysis, we propose an approach that periodically and efficiently optimizes the metric using standard convex constrained optimization and sketching techniques. Through comprehensive experiments on standard computer vision and NLP benchmarks, we show that our approach leads to improvement in test performance for a wide range of tasks.
Leveraging convergence behavior to balance conflicting tasks in multi-task learning

Multi-Task Learning is a learning paradigm that uses correlated tasks to improve performance generalization. A common way to learn multiple tasks is through the hard parameter sharing approach, in which a single architecture is used to share the same subset of parameters, creating an inductive bias between them during the training process. Due to its simplicity, potential to improve generalization, and reduce computational cost, it has gained the attention of the scientific and industrial communities. However, tasks often conflict with each other, which makes it challenging to define how the gradients of multiple tasks should be combined to allow simultaneous learning. To address this problem, we use the idea of multi-objective optimization to propose a method that takes into account temporal behaviour of the gradients to create a dynamic bias that adjust the importance of each task during the backpropagation. The result of this method is to give more attention to the tasks that are diverging or that are not being benefited during the last iterations, allowing to ensure that the simultaneous learning is heading to the performance maximization of all tasks. As a result, we empirically show that the proposed method outperforms the state-of-art approaches on learning conflicting tasks. Unlike the adopted baselines, our method ensures that all tasks reach good generalization performances.
Characterizing Error Mitigation by Symmetry Verification in QAOA

Hardware errors are a major obstacle to demonstrating quantum advantage with the quantum approximate optimization algorithm (QAOA). Recently, symmetry verification has been proposed and empirically demonstrated to boost the quantum state fidelity, the expected solution quality, and the success probability of QAOA on a superconducting quantum processor. Symmetry verification uses parity checks that leverage the symmetries of the objective function to be optimized. We develop a theoretical framework for analyzing this approach under local noise and derive explicit formulas for fidelity improvements on problems with global $\mathbb{Z}_2$ symmetry. We numerically investigate the symmetry verification on the MaxCut problem and identify the error regimes in which this approach improves the QAOA objective. We observe that these regimes correspond to the error rates present in near-term hardware. We further demonstrate the efficacy of symmetry verification on an IonQ trapped ion quantum processor where an improvement in the QAOA objective of up to 19.2\% is observed.
Robust Cross-Modal Representation Learning with Progressive Self-Distillation

The learning objective of vision-language approach of CLIP does not effectively account for the noisy many-to-many correspondences found in web-harvested image captioning datasets, which contributes to its compute and data inefficiency. To address this challenge, we introduce a novel training framework based on cross-modal contrastive learning that uses progressive self-distillation and soft image-text alignments to more efficiently learn robust representations from noisy data. Our model distills its own knowledge to dynamically generate soft-alignment targets for a subset of images and captions in every minibatch, which are then used to update its parameters. Extensive evaluation across 14 benchmark datasets shows that our method consistently outperforms its CLIP counterpart in multiple settings, including: (a) zero-shot classification, (b) linear probe transfer, and (c) image-text retrieval, without incurring added computational cost. Analysis using an ImageNet-based robustness test-bed reveals that our method offers better effective robustness to natural distribution shifts compared to both ImageNet-trained models and CLIP itself. Lastly, pretraining with datasets spanning two orders of magnitude in size shows that our improvements over CLIP tend to scale with number of training examples.
SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework

Multi-animal pose estimation is essential for studying animals' social behaviors in neuroscience and neuroethology. Advanced approaches have been proposed to support multi-animal estimation and achieve state-of-the-art performance. However, these models rarely exploit unlabeled data during training even though real world applications have exponentially more unlabeled frames than labeled frames. Manually adding dense annotations for a large number of images or videos is costly and labor-intensive, especially for multiple instances. Given these deficiencies, we propose a novel semi-supervised architecture for multi-animal pose estimation, leveraging the abundant structures pervasive in unlabeled frames in behavior videos to enhance training, which is critical for sparsely-labeled problems. The resulting algorithm will provide superior multi-animal pose estimation results on three animal experiments compared to the state-of-the-art baseline and exhibits more predictive power in sparsely-labeled data regimes.
Memory effects displayed in the evolution of continuous variable system

We analyze non-Markovian memory effects displayed by the quantum Brownian motion modelled as quantum harmonic oscillators coupled to a bath consisting of harmonic oscillators. We study the time evolution of fidelity, Petz-Rényi relative entropy and quantum entanglement for a family of 1-, 2- and 3-mode Gaussian states.
Analysis of Domestic Digital and Energy Policy Trends Using Text Mining method

Objectives : The objective of this study is to understand digitalization and energy conversion trends and suggest future directions using text-mining-based analysis.Methods : From 2015 to 2021, published domestic research reports and policy briefings related to digitalization and energy from government departments and major media outlets were analyzed using text-mining techniques. Frequency, time-series, and association analyses were conducted to understand current trends and patterns.Results and Discussion : Frequency analysis of reports and articles published for the 2015-2021 period found that the most common keywords were, in descending order, ‘education’, ‘finance’, ‘hydrogen’, and ‘solar power’. This indicates that the core technologies of the fourth industrial revolution have been employed in various fields, with a specific focus on renewable energy for carbon neutrality. Time-series analysis confirmed that the direction of government policy has changed, and it was found that digital and energy conversion was accelerating before and after the outbreak of COVID-19 and the Korean version of the New Deal policies. Association analysis revealed that government policies associated with fourth industrial revolution technologies have been established in various fields and the commercialization of renewable energy has been active.Conclusion Analyzing domestic policy directions using text mining revealed an association between the fourth industrial revolution and carbon neutrality. Text mining techniques can be used to more effectively understanding of domestic policy trends, and it is expected that they will apply a wide variety of fields that can utilize them in the future.
Anomalous Sound Detection Based on Machine Activity Detection

We have developed an unsupervised anomalous sound detection method for machine condition monitoring that utilizes an auxiliary task -- detecting when the target machine is active. First, we train a model that detects machine activity by using normal data with machine activity labels and then use the activity-detection error as the anomaly score for a given sound clip if we have access to the ground-truth activity labels in the inference phase. If these labels are not available, the anomaly score is calculated through outlier detection on the embedding vectors obtained by the activity-detection model. Solving this auxiliary task enables the model to learn the difference between the target machine sounds and similar background noise, which makes it possible to identify small deviations in the target sounds. Experimental results showed that the proposed method improves the anomaly-detection performance of the conventional method complementarily by means of an ensemble.
The Structure Research and Design for Beam Steering and Adjustment in Golay3 Sparse-Aperture Imaging System

In order to further explore the technical difficulties involved in orienting the sparse-aperture imaging system towards practical applications, this paper proposes an optical–mechanical structure design scheme and performance simulation, as well as actual alignment on the beam steering and adjustment structure, which is the core component of the Golay3 sparse-aperture imaging system. The beam steering and adjustment structure corrects the cophasing error by using a combination of mechanical rough adjustment and piezoelectric ceramic precision adjustment. The beam adjustment capability of the beam steering and adjustment structure is analyzed by simulation when the system contains the piston error and the tilt error. The piston error is controlled within 12 μm and the tilt error is controlled within 1200 μrad through the mechanical rough adjustment light path using a large-diameter collimation as the point light source, which realizes the confocality of three beams and mutual interference between two beams. With the USAF1951 resolution test panel display in the distance as the surface target, the adjustable piezoelectric ceramics control the piston error within 55 nm and the tilt error within 0.25 μrad, obtaining the surface target imaging result of the sparse aperture.
Operations on greedoids

We explore the operations of deletion, contraction, direct sum and ordered sum of greedoids. Moreover, we introduce the notion of balanced greedoid and give a necessary and sufficient condition for the direct sum and ordered sum of balanced greedoids to be balanced.
A Study on Enhanced Recommendation Performance with User Qualitative and Quantitative Preference

With the recent rapid development of ICT (Information and Communication Technology) and mobile devices, most users receive various types of information. Thus, users would face information overload issues, which takes much time to select products and services they need or prefer. Therefore, a personalized recommender system has become a practical methodology to address such issues. Existing studies mainly utilized quantitative preferences (e.g., star ratings, click). However, such methodology has limitations in that quantitative information can not fully reflect the user
Where Do You Want To Invest? Predicting Startup Funding From Freely, Publicly Available Web Information

We consider in this paper the problem of predicting the ability of a startup to attract investments using freely, publicly available data. Information about startups on the web usually comes either as unstructured data from news, social networks, and websites or as structured data from commercial databases, such as Crunchbase. The possibility of predicting the success of a startup from structured databases has been studied in the literature and it has been shown that initial public offerings (IPOs), mergers and acquisitions (M\&A) as well as funding events can be predicted with various machine learning techniques. In such studies, heterogeneous information from the web and social networks is usually used as a complement to the information coming from databases. However, building and maintaining such databases demands tremendous human effort. We thus study here whether one can solely rely on readily available sources of information, such as the website of a startup, its social media activity as well as its presence on the web, to predict its funding events. As illustrated in our experiments, the method we propose yields results comparable to the ones making also use of structured data available in private databases.
On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART

Word ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language models have not been studied in word ordering, let alone why they help. We use BART as an instance and show its effectiveness in the task. To explain why BART helps word ordering, we extend analysis with probing and empirically identify that syntactic dependency knowledge in BART is a reliable explanation. We also report performance gains with BART in the related partial tree linearization task, which readily extends our analysis.
Solar Energy Potentials of Some Selected Locations in Northeastern Nigeria

Sunlight is the primary source of clean and cheap energy but its intensity varies from one location to another depending on the latitude, altitude, atmospheric absorption and scattering by air molecules, dust particle and water vapour among others. The aim of this work is to determine the solar energy potentials of Gombe ( latitude 10° 16' 59.9988'' N and longitude of 11° 10' 0.0012'' E), Jalingo (latitude 8° 53' 34.2672'' N and Longitude 11° 22' 37.74'' E), Damaturu (Latitude 11° 44' 49.1856'' N and Longitude 11° 57' 58.2912'' E) and Bauchi (latitude 10° 18' 50.9724'' N and longitude 9° 50' 46.6152'' E) in northeastern Nigeria, the process involves obtaining the solar irradiation data for these four locations from a renewable energy simulation package ( photovoltaic geographical information system, PVGIS) for a period of 10 years, a daily, monthly and annual solar irradiation was obtained for each of these locations. A peak monthly values was obtained in the month of January for Gombe, Jalingo and Bauchi while the month of March has the peak value for Damaturu, an average daily solar irradiation of 5.8977kWh/m2/day, 5.9026 kWh/m2/day, 6.0337 kWh/m2/day and 5.9821kWh/m2/day and an annual values of 2154.44kWh/m2/yr, 2056.17 kWh/m2/yr, 2202.30kWh/m2/yr and 2150.60kWh/m2/yr for Gombe, Jalingo, Damaturu and Bauchi respectively. These locations have very similar atmospheric conditions and hence a closer range of solar energy potentials compared to places like Lagos, Onitsha and Sapele in Delta state with (4.42kWh/m2/day), (4.43kWh/m2/day, and 5.31 kWh/m2/day respectively. Damaturu has the highest solar irradiance among the four locations studied. Grid- connected PV system and thermal system (concentration thermal technology) can be built to support electricity generation and hot water production respectively for residential and industrial use. The utilization of Solar oven, cookers and coolers technology should be encourage by government and non-governmental organization, this will help stop global warning and desert encouragement and hence conserve other natural resources( such as forest) for other use ( furniture construction).