Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface energy and adhesion of perfluoropolyether nanofilms on carbon overcoat: The end group and backbone chain effect

In this paper, we have investigated the surface energy and adhesion of one functional PFPE (Zdol) and two series of nonfunctional PFPEs (Z and D) on carbon-overcoated disk surfaces. The effects of end group functionality, backbone chain flexibility, molecular weight, and film thickness were systematically examined. Our results indicated that nonfunctional PFPEs have weak attraction with carbon overcoat. However, due to backbone chain effect, Z has slightly stronger attraction than D. Based on the surface energy analyses and bonded thickness results, schematic bonding models were proposed, which indicate strong hydrogen bonding∕ordered packing structure∕low mobility for functional PFPE films and weak attraction∕less-ordered packing structure∕high mobility for nonfunctional PFPE films.
Image Captioning using Deep Stacked LSTMs, Contextual Word Embeddings and Data Augmentation

Image Captioning, or the automatic generation of descriptions for images, is one of the core problems in Computer Vision and has seen considerable progress using Deep Learning Techniques. We propose to use Inception-ResNet Convolutional Neural Network as encoder to extract features from images, Hierarchical Context based Word Embeddings for word representations and a Deep Stacked Long Short Term Memory network as decoder, in addition to using Image Data Augmentation to avoid over-fitting. For data Augmentation, we use Horizontal and Vertical Flipping in addition to Perspective Transformations on the images. We evaluate our proposed methods with two image captioning frameworks- Encoder-Decoder and Soft Attention. Evaluation on widely used metrics have shown that our approach leads to considerable improvement in model performance.
Actor-Critic Method for High Dimensional Static Hamilton-Jacobi-Bellman Partial Differential Equations based on Neural Networks

We propose a novel numerical method for high dimensional Hamilton--Jacobi--Bellman (HJB) type elliptic partial differential equations (PDEs). The HJB PDEs, reformulated as optimal control problems, are tackled by the actor-critic framework inspired by reinforcement learning, based on neural network parametrization of the value and control functions. Within the actor-critic framework, we employ a policy gradient approach to improve the control, while for the value function, we derive a variance reduced least-squares temporal difference method using stochastic calculus. To numerically discretize the stochastic control problem, we employ an adaptive step size scheme to improve the accuracy near the domain boundary. Numerical examples up to $20$ spatial dimensions including the linear quadratic regulators, the stochastic Van der Pol oscillators, the diffusive Eikonal equations, and fully nonlinear elliptic PDEs derived from a regulator problem are presented to validate the effectiveness of our proposed method.
Probabilistic spatial analysis in quantitative microscopy with uncertainty-aware cell detection using deep Bayesian regression

The investigation of biological systems with three-dimensional microscopy demands automatic cell identification methods that not only are accurate but also can imply the uncertainty in their predictions. The use of deep learning to regress density maps is a popular successful approach for extracting cell coordinates from local peaks in a postprocessing step, which then, however, hinders any meaningful probabilistic output. We propose a framework that can operate on large microscopy images and output probabilistic predictions (i) by integrating deep Bayesian learning for the regression of uncertainty-aware density maps, where peak detection algorithms generate cell proposals, and (ii) by learning a mapping from prediction proposals to a probabilistic space that accurately represents the chances of a successful prediction. Using these calibrated predictions, we propose a probabilistic spatial analysis with Monte Carlo sampling. We demonstrate this in a bone marrow dataset, where our proposed methods reveal spatial patterns that are otherwise undetectable.
Factorization of Fact-Checks for Low Resource Indian Languages

The advancement in technology and accessibility of internet to each individual is revolutionizing the real time information. The liberty to express your thoughts without passing through any credibility check is leading to dissemination of fake content in the ecosystem. It can have disastrous effects on both individuals and society as a whole. The amplification of fake news is becoming rampant in India too. Debunked information often gets republished with a replacement description, claiming it to depict some different incidence. To curb such fabricated stories, it is necessary to investigate such deduplicates and false claims made in public. The majority of studies on automatic fact-checking and fake news detection is restricted to English only. But for a country like India where only 10% of the literate population speak English, role of regional languages in spreading falsity cannot be undermined. In this paper, we introduce FactDRIL: the first large scale multilingual Fact-checking Dataset for Regional Indian Languages. We collect an exhaustive dataset across 7 months covering 11 low-resource languages. Our propose dataset consists of 9,058 samples belonging to English, 5,155 samples to Hindi and remaining 8,222 samples are distributed across various regional languages, i.e. Bangla, Marathi, Malayalam, Telugu, Tamil, Oriya, Assamese, Punjabi, Urdu, Sinhala and Burmese. We also present the detailed characterization of three M's (multi-lingual, multi-media, multi-domain) in the FactDRIL accompanied with the complete list of other varied attributes making it a unique dataset to study. Lastly, we present some potential use cases of the dataset. We expect this dataset will be a valuable resource and serve as a starting point to fight proliferation of fake news in low resource languages.
Simultaneous Detection of Ascorbic Acid and Hydrogen Peroxide by Flow-Injection Analysis with a Thin Layer Dual-Electrode Detector

Based on preliminary voltammetric investigations at unmodified and modified electrodes in aqueous solutions buffered at different pHs, a profitable thin layer dual-electrode detector was developed for the selective and simultaneous determination of ascorbic acid (AA) and hydrogen peroxide present in the same samples. It consists of a small thickness (0.1 mm) cell, used as a detector for a flow injection system, in which a glassy carbon (GC) and a polyeugenol coated Pt electrode are encased. The GC electrode is selective for AA, thanks to the potential applied (0.3 V vs. Ag/AgCl,Cl−sat), while H2O2 alone can be oxidized at the Pt electrode (0.75 V), thanks to the size selectivity and electrostatic screen properties displayed by the polyeugenol coating layer which prevents oxidation of ascorbate anions. Repeatable sharp peaks (±4.5 %) were detected for both analytes over wide linear ranges (0.005–1.000 mM) and detection limits of about 10−6 M (176 ppb) and 5×10−7 M (16 ppb) were inferred for AA and H2O2 respectively. This flow injection approach was applied to the analysis of some orange-taste soft drinks, used as prototype real samples, spiked with controlled amounts of both analytes, thus inferring good recoveries which pointed out that deviations never exceeding 5 % affected the relevant accuracy.
Interpretability in Contact-Rich Manipulation via Kinodynamic Images

Deep Neural Networks (NNs) have been widely utilized in contact-rich manipulation tasks to model the complicated contact dynamics. However, NN-based models are often difficult to decipher which can lead to seemingly inexplicable behaviors and unidentifiable failure cases. In this work, we address the interpretability of NN-based models by introducing the kinodynamic images. We propose a methodology that creates images from kinematic and dynamic data of contact-rich manipulation tasks. By using images as the state representation, we enable the application of interpretability modules that were previously limited to vision-based tasks. We use this representation to train a Convolutional Neural Network (CNN) and we extract interpretations with Grad-CAM to produce visual explanations. Our method is versatile and can be applied to any classification problem in manipulation tasks to visually interpret which parts of the input drive the model’s decisions and distinguish its failure modes, regardless of the features used. Our experiments demonstrate that our method enables detailed visual inspections of sequences in a task, and high-level evaluations of a model’s behavior. Code for this work is available at [1].
Mathematical Properties of Generalized Shape Expansion-Based Motion Planning Algorithms

Motion planning is an essential aspect of autonomous systems and robotics and is an active area of research. A recently-proposed sampling-based motion planning algorithm, termed 'Generalized Shape Expansion' (GSE), has been shown to possess significant improvement in computational time over several existing well-established algorithms. The GSE has also been shown to be probabilistically complete. However, asymptotic optimality of the GSE is yet to be studied. To this end, in this paper we show that the GSE algorithm is not asymptotically optimal by studying its behaviour for the promenade problem. In order to obtain a probabilistically complete and asymptotically optimal generalized shape-based algorithm, a modified version of the GSE, namely 'GSE*' algorithm, is subsequently presented. The forementioned desired mathematical properties of the GSE* algorithm are justified by its detailed analysis. Numerical simulations are found to be in line with the theoretical results on the GSE* algorithm.
Evaluasi Kepuasan Pengguna Aplikasi Tapp MarketMenggunakan Metode Eucs(End User Computing Satisfaction)

Evaluasi kepuasan pengguna sangat diperlukan karena Tapp Market merupakan online shop yang baru berdiri pada tahun 2013 di Finlandia dan memasuki Indonesia pada tahun 2015. Dengan adanya evaluasi ini diharapkan aplikasi Tapp Market dapat bersaing dengan para pendahulunya. Evaluasi dilakukan dengan menggunakan metode End User Computing Satisfaction (EUCS) dimana terdapat 5 variabel yaitu content, accuracy, format, ease of use dan timeliness. Pada variabel content terdapat 4 indikator, accuracy terdapat 2 indikator, format terdapat 1 indikator, ease of use terdapat 2 indikator dan timeliness terdapat 1 indikator. Data didapatkan dari jumlah sampel sebesar 100 responden menggunakan kuesioner. Dari hasil evaluasi yang dilakukan dinyatakan bahwa kepuasan pengguna pada variabel content, format,dan ease of use masuk kedalam kategori Tingggi dengan masing-masing nilai yang diperoleh adalah 74,7%, 73,2%, 70,9% yang berarti pengguna merasa Puas terhadap aplikasi Tapp Market jika diukur dengan ketiga variabel tersebut. Pada variabel accuracy masuk kedalam kategori Sangat Tinggi dengan nilai yang dihasilkan adalah 76,6% yang berarti pengguna merasa Sangat Puas terhadap aplikasi Tapp Market jika diukur dari variabel tersebut. Pada variabel timeliness masuk kedalam kategori Rendah dengan nilai yang diperoleh adalah 50,0 yang berarti pengguna marasa puas terhadap aplikasi Tapp Market jika diukur dari variabel tersebut. Rekomendasi yang diberikan dalam penelitian ini adalah variabel dengan kategori Rendah dan kategori Tinggi perlu untuk meningkatkan kualitas layanan agar ketegorinya dapat mencapai kategori Sangat Tinggi agar dapat meningkatkan kepuasan pengguna aplikasi Tapp Market.
First-principles study of energetic and electronic properties of δ-Re6MO12 (Re = Ho, Gd, Y; M = U, W)

First-principles calculations have been performed to study the structural, energetic, and electronic properties of δ-Re6MO12 (Re = Ho, Gd, Y; M = U, W). The calculated results indicated that the energetic tendencies for the formation of Frenkel-pair defects of δ-Re6MO12 are consistent with the experimental results, and the Frenkel-pair defects have a significant influence on radiation-induced phase transformation. Density of states (DOS) analysis showed hybridizations between W 5d and O 2p states in Y6WO12 and between U 6d and O 2p states in Re6UO12 (Re = Ho, Gd, Y) are observed, but the interaction ⟨M-O⟩ orbital is much stronger in Re6UO12 (Re = Ho, Gd, Y) than that in Y6WO12. Bader charge analysis revealed that ⟨U-O⟩ bond in Re6UO12 (Re = Ho, Gd, Y) is more covalent than ⟨W-O⟩ bond in Y6WO12. It was proposed that ⟨M-O⟩ bond may play a more significant role in determining their radiation tolerance.
Taking the Culture out of the Lab and Into the Office: A “Non-Lab” Approach to Public Service Transformation

Over the years, innovation labs have come and gone in public sector organizations. At Immigration, Refugees and Citizenship Canada, one low-key, co-design project over-delivered on client insights, service experience improvements and operational efficiencies. This case study shares one department’s success in embedding human-centred design into organizational culture by: competing against graduate design students, co-designing across the organization (from call agents to policy, immigration officers, and communications), creating a design project alumni community, and adhering to rigorous measurement and experimentation. The case study will share opportunities and challenges that emerged from the process of embedding human-centred design (via a “non-innovation lab”) into the department.
RUBERT: A Bilingual Roman Urdu BERT Using Cross Lingual Transfer Learning

In recent studies, it has been shown that Multilingual language models underperform their monolingual counterparts. It is also a well-known fact that training and maintaining monolingual models for each language is a costly and time-consuming process. Roman Urdu is a resource-starved language used popularly on social media platforms and chat apps. In this research, we propose a novel dataset of scraped tweets containing 54M tokens and 3M sentences. Additionally, we also propose RUBERT a bilingual Roman Urdu model created by additional pretraining of English BERT. We compare its performance with a monolingual Roman Urdu BERT trained from scratch and a multilingual Roman Urdu BERT created by additional pretraining of Multilingual BERT. We show through our experiments that additional pretraining of the English BERT produces the most notable performance improvement.
Spectral density of random graphs: convergence properties and application in model fitting

Random graph models are used to describe the complex structure of real-world networks in diverse fields of knowledge. Studying their behavior and fitting properties are still critical challenges, that in general, require model specific techniques. An important line of research is to develop generic methods able to fit and select the best model among a collection. Approaches based on spectral density (i.e., distribution of the graph adjacency matrix eigenvalues) are appealing for that purpose: they apply to different random graph models. Also, they can benefit from the theoretical background of random matrix theory. This work investigates the convergence properties of model fitting procedures based on the graph spectral density and the corresponding cumulative distribution function. We also review results on the convergence of the spectral density for the most widely used random graph models. Moreover, we explore through simulations the limits of these graph spectral density convergence results, particularly in the case of the block model, where only partial results have been established.
The rotational and divergent components of atmospheric circulation on tidally locked planets

Significance Tidally locked exoplanets have a permanent day side and night side. Understanding the atmospheric circulation on these planets is crucial for interpreting telescope observations and assessing their habitability. We show that the main components of the circulation—a jet going around the planet, stationary atmospheric waves, and direct flow from the day side to the night side—can be separated using a simple mathematical decomposition. This decomposition will significantly aid future study of tidally locked atmospheres. As an illustration, we use it to quantify heat transport due to different components of the circulation. This analysis reveals that the direct day–night component can dominate heat transport from the day side to the night side, even when the jet is strong. Tidally locked exoplanets likely host global atmospheric circulations with a superrotating equatorial jet, planetary-scale stationary waves, and thermally driven overturning circulation. In this work, we show that each of these features can be separated from the total circulation by using a Helmholtz decomposition, which splits the circulation into rotational (divergence-free) and divergent (vorticity-free) components. This technique is applied to the simulated circulation of a terrestrial planet and a gaseous hot Jupiter. For both planets, the rotational component comprises the equatorial jet and stationary waves, and the divergent component contains the overturning circulation. Separating out each component allows us to evaluate their spatial structure and relative contribution to the total flow. In contrast with previous work, we show that divergent velocities are not negligible when compared with rotational velocities and that divergent, overturning circulation takes the form of a single, roughly isotropic cell that ascends on the day side and descends on the night side. These conclusions are drawn for both the terrestrial case and the hot Jupiter. To illustrate the utility of the Helmholtz decomposition for studying atmospheric processes, we compute the contribution of each of the circulation components to heat transport from day side to night side. Surprisingly, we find that the divergent circulation dominates day–night heat transport in the terrestrial case and accounts for around half of the heat transport for the hot Jupiter. The relative contributions of the rotational and divergent components to day–night heat transport are likely sensitive to multiple planetary parameters and atmospheric processes and merit further study.
GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning

Ofﬂine reinforcement learning approaches can generally be divided to proximal and uncertainty-aware methods. In this work, we demonstrate the beneﬁt of combining the two in a latent variational model. We impose a latent representation of states and actions and leverage its intrinsic Riemannian geometry to measure distance of latent samples to the data. Our proposed metrics measure both the quality of out of distribution samples as well as the discrepancy of examples in the data. We integrate our metrics in a model-based ofﬂine optimization framework, in which proximity and uncertainty can be carefully controlled. We illustrate the geodesics on a simple grid-like environment, depicting its natural inherent topology. Finally, we analyze our approach and improve upon contemporary ofﬂine RL benchmarks.
Remote VR Studies: A Framework for Running Virtual Reality Studies Remotely Via Participant-Owned HMDs

We investigate opportunities and challenges of running virtual reality (VR) studies remotely. Today, many consumers own head-mounted displays (HMDs), allowing them to participate in scientific studies from their homes using their own equipment. Researchers can benefit from this approach by being able to recruit study populations normally out of their reach, and to conduct research at times when it is difficult to get people into the lab (cf. the COVID pandemic). In an initial online survey (N = 227), we assessed HMD owners’ demographics, their VR setups and their attitudes toward remote participation. We then identified different approaches to running remote studies and conducted two case studies for an in-depth understanding. We synthesize our findings into a framework for remote VR studies, discuss strengths and weaknesses of the different approaches, and derive best practices. Our work is valuable for Human-Computer Interaction (HCI) researchers conducting VR studies outside labs.
SliceNStitch: Continuous CP Decomposition of Sparse Tensor Streams

Consider traffic data (i.e., triplets in the form of source-destination-timestamp) that grow over time. Tensors (i.e., multi-dimensional arrays) with a time mode are widely used for modeling and analyzing such multi-aspect data streams. In such tensors, however, new entries are added only once per period, which is often an hour, a day, or even a year. This discreteness of tensors has limited their usage for real-time applications, where new data should be analyzed instantly as it arrives.How can we analyze time-evolving multi-aspect sparse data ‘continuously’ using tensors where time is ‘discrete’? We propose SLICENSTITCH for continuous CANDECOMP/PARAFAC (CP) decomposition, which has numerous time-critical applications, including anomaly detection, recommender systems, and stock market prediction. SLICENSTITCH changes the starting point of each period adaptively, based on the current time, and updates factor matrices (i.e., outputs of CP decomposition) instantly as new data arrives. We show, theoretically and experimentally, that SLICENSTITCH is (1) ‘Any time’: updating factor matrices immediately without having to wait until the current time period ends, (2) Fast: with constant-time updates up to 464× faster than online methods, and (3) Accurate: with fitness comparable (specifically, 72 − 100%) to offline methods.
CAC: A Clustering Based Framework for Classification

In data containing heterogeneous subpopulations, classiﬁcation performance beneﬁts from incorporating the knowledge of cluster structure in the classiﬁer. Previous methods for such combined clustering and classiﬁcation either are classiﬁer-speciﬁc and not generic, or independently perform clustering and classiﬁer training, which may not form clusters that can potentially beneﬁt classiﬁer performance. The question of how to perform clustering to improve the performance of classiﬁers trained on the clusters has received scant attention in previous literature despite its importance in several real-world applications. In this paper, we theoretically analyze when and how clustering may help in obtaining accurate classiﬁers. We design a simple, efﬁcient, and generic framework called C lassiﬁcation A ware C lustering (CAC), to ﬁnd clusters that are well suited for being used as training datasets by classiﬁers for each underlying subpopulation. Our experiments on synthetic and real benchmark datasets demonstrate the efﬁ-cacy of CAC over previous methods for combined clustering and classiﬁcation.