Miracle-dz/UniFAR-FTUs huggingface dataset:
SaNet: Scale-aware Neural Network for Semantic Labelling of Multiple Spatial Resolution Aerial Images

: Assigning geospatial objects of aerial images with specific categories at the pixel level is a fundamental task in urban scene interpretation. Along with rapid developments in sensor technologies, aerial images can be captured at multiple spatial resolutions (MSR) with information content manifested at different scales. Extracting information from these MSR aerial images represents huge opportunities for enhanced feature representation and characterisation. However, MSR images suffer from two critical issues: 1) increased variation in the sizes of geospatial objects and 2) information and informative feature loss at coarse spatial resolutions. In this paper, we propose a novel scale-aware neural network (SaNet) for semantic labelling of MSR aerial images to address these two issues. SaNet deploys a densely connected feature network (DCFPN) module to capture high-quality multi-scale context, such as to address the scale variation issue and increase the quality of segmentation for both large and small objects simultaneously. A spatial feature recalibration (SFR) module is further incorporated into the network to learn complete semantic features with enhanced spatial relationships, where the effects of information and informative feature loss are addressed. The combination of DCFPN and SFR allows the proposed SaNet to learn scale-aware features from MSR aerial images. Extensive experiments undertaken on ISPRS semantic segmentation datasets demonstrated the outstanding accuracy of the proposed SaNet in cross-resolution segmentation, with an average OA of 83.4% on the Vaihingen dataset and an average F1 score of 80.4% on the Potsdam dataset, outperforming state-of-the-art deep learning approaches, including FPN (80.2% and 76.6%), PSPNet (79.8% and 76.2%) and Deeplabv3+ (80.8% and 76.1%) as well as DDCM-Net (81.7% and 77.6%) and EaNet (81.5% and 78.3%).
Silver nanoparticles for melamine detection in milk based on transmitted light intensity

Melamine (C3H6N6) is a commercially synthesised organic compound which is illegally added to milk during processing to boost the apparent protein content. Melamine cannot be metabolised by the body and forms insoluble crystals which causes tissue damage and renal dysfunction. Hence, there is a need to develop a rapid and reliable method for melamine detection. Attempted here is to develop a simple procedure to detect melamine in milk using silver nanoparticles (AgNPs). Borohydride-reduced AgNPs, citrate-capped AgNPs and polyvinyl pyrrolidone-capped AgNPs are synthesised and characterised for their size, composition, structure etc. and studied for the suitability in this application. The first two NPs are determined to be suitable for this detection and exhibit a colour change from pale yellow to red at different concentration of melamine. To measure the melamine concentration, quantification of colour change and the transmitted light intensity is done using a colour sensor when the mixture is exposed to standard light source. Compared with visual observation in colour change, the transmitted light intensity provides more information to quantify melamine, more specifically citrate-capped AgNPs are found to provide better results than borohydride-reduced AgNPs. The results of this study are much agreeable to that obtained through standard laboratory experiment. Hence, this proposed method would be useful in developing an electronic-based melamine testing instrument that can be used rapidly at home.
Synthesis of Cobalt (Co) doped ZnO Spintronic Nano Powders by Solution Combustion Method and Their Characterization

In this current work, we report the synthesis of transition metal (TM) Cobalt(Co) doped ZnO (Zn1−x Cox O where x = 0.01, 0.05, 0.1) powders by solution combustion process using urea (NH2CONH2) as fuel. The synthesized powders were characterized by XRD for phase anlalysis and SEM for microstructure. The powder XRD patterns of the samples confirms the formation of polycrystalline ZnO of hexagonal wurtzite structure by comparing with the standard peaks of JCPDS card no 36-1451. The crystallite size of the powder ranges from 18-30nm when calculated by Scherrer formula. SEM images shows the leaf like ZnO particales of thikness 60nm with 400-600nm length. Keywords—— Combustion, Zinc Nitrate, Cobalt Nitrate, Urea.
Can Visualization Alleviate Dichotomous Thinking? Effects of Visual Representations on the Cliff Effect

Common reporting styles for statistical results in scientific articles, such as <inline-formula><tex-math notation="LaTeX">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="helske-ieq1-3073466.gif"/></alternatives></inline-formula>-values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the <inline-formula><tex-math notation="LaTeX">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="helske-ieq2-3073466.gif"/></alternatives></inline-formula>-value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recommended to reduce such dichotomous interpretations but their effectiveness has also been challenged. We ran two experiments on researchers with expertise in statistical analysis to compare several alternative representations of confidence intervals and used Bayesian multilevel models to estimate the effects of the representation styles on differences in researchers’ subjective confidence in the results. We also asked the respondents’ opinions and preferences in representation styles. Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations – measured as the ‘cliff effect’: the sudden drop in confidence around <inline-formula><tex-math notation="LaTeX">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="helske-ieq3-3073466.gif"/></alternatives></inline-formula>-value 0.05 – compared with classic CI visualization and textual representation of the CI with <inline-formula><tex-math notation="LaTeX">$p$</tex-math><alternatives><mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="helske-ieq4-3073466.gif"/></alternatives></inline-formula>-values. All data and analyses are publicly available at <uri>https://github.com/helske/statvis</uri>.
Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets

Objective: Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically. Methods: In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors. Results: Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24 dB in the brain and 21.2 dB in tumor regions. Conclusion and Significance: Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning.
An application of neighbourhoods in digraphs to the classification of binary dynamics

Abstract A binary state on a graph means an assignment of binary values to its vertices. A time-dependent sequence of binary states is referred to as binary dynamics. We describe a method for the classification of binary dynamics of digraphs, using particular choices of closed neighbourhoods. Our motivation and application comes from neuroscience, where a directed graph is an abstraction of neurons and their connections, and where the simplification of large amounts of data is key to any computation. We present a topological/graph theoretic method for extracting information out of binary dynamics on a graph, based on a selection of a relatively small number of vertices and their neighbourhoods. We consider existing and introduce new real-valued functions on closed neighbourhoods, comparing them by their ability to accurately classify different binary dynamics. We describe a classification algorithm that uses two parameters and sets up a machine learning pipeline. We demonstrate the effectiveness of the method on simulated activity on a digital reconstruction of cortical tissue of a rat, and on a nonbiological random graph with similar density.
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions

In hybrid human-AI systems, users need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown. To accommodate such settings, we introduce RETRO-VIZ, a method for (i) estimating and (ii) explaining trustworthiness of regression predictions. It consists of RETRO, a quantitative estimate of the trustworthiness of a prediction, and VIZ, a visual explanation that helps users identify the reasons for the (lack of) trustworthiness of a prediction. We find that RETRO-scores negatively correlate with prediction error across 117 experimental settings, indicating that RETRO provides a useful measure to distinguish trustworthy predictions from untrustworthy ones. In a user study with 41 participants, we find that VIZ-explanations help users identify whether a prediction is trustworthy or not: on average, 95.1% of participants correctly select the more trustworthy prediction, given a pair of predictions. In addition, an average of 75.6% of participants can accurately describe why a prediction seems to be (not) trustworthy. Finally, we find that the vast majority of users subjectively experience RETRO-VIZ as a useful tool to assess the trustworthiness of algorithmic predictions.
UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction

Reading is a complex process which requires proper understanding of texts in order to create coherent mental representations. However, comprehension problems may arise due to hard-to-understand sections, which can prove troublesome for readers, while accounting for their specific language skills. As such, steps towards simplifying these sections can be performed, by accurately identifying and evaluating difficult structures. In this paper, we describe our approach for the SemEval-2021 Task 1: Lexical Complexity Prediction competition that consists of a mixture of advanced NLP techniques, namely Transformer-based language models, pre-trained word embeddings, Graph Convolutional Networks, Capsule Networks, as well as a series of hand-crafted textual complexity features. Our models are applicable on both subtasks and achieve good performance results, with a MAE below 0.07 and a Person correlation of . 73 for single word identification, as well as a MAE below 0.08 and a Person correlation of . 79 for multiple word targets. Our results are just 5.46% and 6.5% lower than the top scores obtained in the competition on the first and the second subtasks, respectively.
ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration

In the last decade, convolutional neural networks (ConvNets) have dominated and achieved state-of-the-art performances in a variety of medical imaging applications. However, the performances of ConvNets are still limited by lacking the understanding of long-range spatial relations in an image. The recently proposed Vision Transformer (ViT) for image classification uses a purely self-attention-based model that learns long-range spatial relations to focus on the relevant parts of an image. Nevertheless, ViT emphasizes the low-resolution features because of the consecutive downsamplings, result in a lack of detailed localization information, making it unsuitable for image registration. Recently, several ViT-based image segmentation methods have been combined with ConvNets to improve the recovery of detailed localization information. Inspired by them, we present ViT-V-Net, which bridges ViT and ConvNet to provide volumetric medical image registration. The experimental results presented here demonstrate that the proposed architecture achieves superior performance to several top-performing registration methods.
Self‐adjointness of non‐semibounded covariant Schrödinger operators on Riemannian manifolds

In the context of a geodesically complete Riemannian manifold M, we study the self‐adjointness of ∇†∇+V$\nabla ^{\dagger }\nabla +V$ , where ∇ is a metric covariant derivative (with formal adjoint ∇†$\nabla ^{\dagger }$ ) on a Hermitian vector bundle V$\mathcal {V}$ over M, and V is a locally square integrable section of EndV$\operatorname{End}\mathcal {V}$ such that the (fiberwise) norm of the “negative” part V−$V^{-}$ belongs to the local Kato class (or, more generally, local contractive Dynkin class). Instead of the lower semiboundedness hypothesis, we assume that there exists a number ε∈[0,1]$\varepsilon \in [0,1]$ and a positive function q on M satisfying certain growth conditions, such that ε∇†∇+V≥−q$\varepsilon \nabla ^{\dagger }\nabla +V\ge -q$ , the inequality being understood in the quadratic form sense over Cc∞(V)$C_{c}^{\infty }(\mathcal {V})$ . In the first result, which pertains to the case ε∈[0,1)$\varepsilon \in [0,1)$ , we use the elliptic equation method. In the second result, which pertains to the case ε=1$\varepsilon =1$ , we use the hyperbolic equation method.
Zaionc paradox revisited

Canonical expressions are representative of implicative proposi- tions (i.e., propositions with only implications) up-to renaming of variables. Using a Monte-Carlo approach, we explore the model of canonical expressions in order to conﬁrm the paradox that says that asymptotically almost all classical theorems are intuitionistic . Actually we found that more than 96 , 6% of classical theorems are intuitionistic among propositions of size 100.
Irreversibility and Biased Ensembles in Active Matter: Insights from Stochastic Thermodynamics

Active systems evade the rules of equilibrium thermodynamics by constantly dissipating energy at the level of their microscopic components. This energy flux stems from the conversion of a fuel, present in the environment, into sustained individual motion. It can lead to collective effects without any equilibrium equivalent, some of which can be rationalized by using equilibrium tools to recapitulate nonequilibrium transitions. An important challenge is then to delineate systematically to what extent the character of these active transitions is genuinely distinct from equilibrium analogs. We review recent works that use stochastic thermodynamics tools to identify, for active systems, a measure of irreversibility comprising a coarse-grained or informatic entropy production. We describe how this relates to the underlying energy dissipation or thermodynamic entropy production, and how it is influenced by collective behavior. Then, we review the possibility of constructing thermodynamic ensembles out-of-equilibrium, where trajectories are biased toward atypical values of nonequilibrium observables. We show that this is a generic route to discovering unexpected phase transitions in active matter systems, which can also inform their design. Expected final online publication date for the Annual Review of Condensed Matter Physics, Volume 13 is March 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-Goals

Two key challenges within Reinforcement Learning involve improving (a) agent learning within environments with sparse extrinsic rewards and (b) the explainability of agent actions. We describe a curious subgoal focused agent to address both these challenges. We use a novel method for curiosity produced from a Generative Adversarial Network (GAN) based model of environment transitions that is robust to stochastic environment transitions. Additionally, we use a subgoal generating network to guide navigation. The explainability of the agent's behavior is increased by decomposing complex tasks into a sequence of interpretable subgoals that do not require any manual design. We show that this method also enables the agent to solve challenging procedurally-generated tasks that contain stochastic transitions above other state-of-the-art methods.
Anisotropic effective redshift and evolving clustering amplitude

A typical galaxy survey geometry results in galaxy pairs of different separation and angle to the line-of-sight having different distributions in redshift and consequently a different effective redshift. However, clustering measurements are analysed assuming that the clustering is representative of that at a single effective redshift. We investigate the impact of variations in the galaxy-pair effective redshift on the large-scale clustering measured in galaxy surveys. We find that galaxy surveys spanning a large redshift range have different effective redshifts as a function of both pair separation and angle. Furthermore, when considering tracers whose clustering amplitude evolves strongly with redshift, this combination can result in an additional scale-dependent clustering anisotropy. We demonstrate the size of this effect on the eBOSS DR16 Quasar sample and show that, while the impact on monopole is negligible, neglecting this effect can result in a tilt of ∼ 4% and ∼40% in quadrupole and hexadecapole, respectively, on scales ∼1 h -1Gpc. We discuss strategies to mitigate this effect when making measurements.
Uncertainty measures: The big picture

Probability theory is far from being the most general mathematical theory of uncertainty. A number of arguments point at its inability to describe second-order ('Knightian') uncertainty. In response, a wide array of theories of uncertainty have been proposed, many of them generalisations of classical probability. As we show here, such frameworks can be organised into clusters sharing a common rationale, exhibit complex links, and are characterised by different levels of generality. Our goal is a critical appraisal of the current landscape in uncertainty theory.
Zero-Resource Multi-Dialectal Arabic Natural Language Understanding

—A reasonable amount of annotated data is required for ﬁne-tuning pre-trained language models (PLM) on downstream tasks. However, obtaining labeled examples for different language varieties can be costly. In this paper, we investigate the zero-shot performance on Dialectal Arabic (DA) when ﬁne- tuning a PLM on modern standard Arabic (MSA) data only — identifying a signiﬁcant performance drop when evaluating such models on DA. To remedy such performance drop, we propose self-training with unlabeled DA data and apply it in the context of named entity recognition (NER), part-of-speech (POS) tagging, and sarcasm detection (SRD) on several DA varieties. Our results demonstrate the effectiveness of self-training with unlabeled DA data: improving zero-shot MSA-to-DA transfer by as large as ∼ 10% F 1 (NER), 2% accuracy (POS tagging), and 4.5% F 1 (SRD). We conduct an ablation experiment and show that the performance boost observed directly results from the unlabeled DA examples used for self-training. Our work opens up opportunities for leveraging the relatively abundant labeled MSA datasets to develop DA models for zero and low-resource dialects. We also report new state-of-the-art performance on all three tasks and open-source our ﬁne-tuned models for the research community.
Survey on homological flips and homological flops

We give a survey for the results in [13, 14, 15], which attempts to relate the derived categories under general classes of flips and flops. We indicate how the approach fails because of what appears to be a formal problem. We give some ideas, and record some failed attempts, to fix this problem. We also present some new examples.
Political Polarization in Online News Consumption

Political polarization appears to be on the rise, as measured by voting behavior, general affect towards opposing partisans and their parties, and contents posted and consumed online. Research over the years has focused on the role of the Web as a driver of polarization. In order to further our understanding of the factors behind online polarization, in the present work we collect and analyze Web browsing histories of tens of thousands of users alongside careful measurements of the time spent browsing various news sources. We show that online news consumption follows a polarized pattern, where users' visits to news sources aligned with their own political leaning are substantially longer than their visits to other news sources. Next, we show that such preferences hold at the individual as well as the population level, as evidenced by the emergence of clear partisan communities of news domains from aggregated browsing patterns. Finally, we tackle the important question of the role of user choices in polarization. Are users simply following the links proffered by their Web environment, or do they exacerbate partisan polarization by intentionally pursuing like-minded news sources? To answer this question, we compare browsing patterns with the underlying hyperlink structure spanned by the considered news domains, finding strong evidence of polarization in partisan browsing habits beyond that which can be explained by the hyperlink structure of the Web.