Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Being Real: A Sensorimotor Control Perspective on Interactions in Virtual Reality

We can create Virtual Reality (VR) interactions that have no equivalent in the real world by remapping spacetime or altering users’ body representation, such as stretching the user’s virtual arm for manipulation of distant objects or scaling up the user’s avatar to enable rapid locomotion. Prior research has leveraged such approaches, what we call beyond-real techniques, to make interactions in VR more practical, efficient, ergonomic, and accessible. We present a survey categorizing prior movement-based VR interaction literature as reality-based, illusory, or beyond-real interactions. We survey relevant conferences (CHI, IEEE VR, VRST, UIST, and DIS) while focusing on selection, manipulation, locomotion, and navigation in VR. For beyond-real interactions, we describe the transformations that have been used by prior works to create novel remappings. We discuss open research questions through the lens of the human sensorimotor control system and highlight challenges that need to be addressed for effective utilization of beyond-real interactions in future VR applications, including plausibility, control, long-term adaptation, and individual differences.
An Energy-Based Prior for Generative Saliency

We propose a novel generative saliency prediction framework that adopts an informative energy-based model as a prior distribution. The energy-based prior model is defined on the latent space of a saliency generator network that generates the saliency map based on a continuous latent variables and an observed image. Both the parameters of saliency generator and the energy-based prior are jointly trained via Markov chain Monte Carlo-based maximum likelihood estimation, in which the sampling from the intractable posterior and prior distributions of the latent variables are performed by Langevin dynamics. With the generative saliency model, we can obtain a pixel-wise uncertainty map from an image, indicating model confidence in the saliency prediction. Different from existing generative models, which define the prior distribution of the latent variables as a simple isotropic Gaussian distribution, our model uses an energy-based informative prior which can be more expressive in capturing the latent space of the data. With the informative energy-based prior, we extend the Gaussian distribution assumption of generative models to achieve a more representative distribution of the latent space, leading to more reliable uncertainty estimation. We apply the proposed frameworks to both RGB and RGB-D salient object detection tasks with both transformer and convolutional neural network backbones. We further propose an adversarial learning algorithm and a variational inference algorithm as alternatives to train the proposed generative framework. Experimental results show that our generative saliency model with an energy-based prior can achieve not only accurate saliency predictions but also reliable uncertainty maps that are consistent with human perception.
Impact of Tokenization on Language Models: An Analysis for Turkish

Tokenization is an important text preprocessing step to prepare input tokens for deep language models. WordPiece and BPE are de facto methods employed by important models, such as BERT and GPT. However, the impact of tokenization can be different for morphologically rich languages, such as Turkic languages, in which many words can be generated by adding prefixes and suffixes. We compare five tokenizers at different granularity levels, that is, their outputs vary from the smallest pieces of characters to the surface form of words, including a Morphological-level tokenizer. We train these tokenizers and pretrain medium-sized language models using the RoBERTa pretraining procedure on the Turkish split of the OSCAR corpus. We then fine-tune our models on six downstream tasks. Our experiments, supported by statistical tests, reveal that the morphological-level tokenizer delivers a challenging performance with de facto tokenizers. Furthermore, we find that increasing the vocabulary size improves the performance of Morphological- and Word-level tokenizers more than that of de facto tokenizers. The ratio of the number of vocabulary parameters to the total number of model parameters can be empirically chosen as 20% for de facto tokenizers and 40% for other tokenizers to obtain a reasonable trade-off between model size and performance.
Complexity phase transitions in instantaneous quantum polynomial-time circuits

We study a subclass of the Instantaneous Quantum Polynomial-time (IQP) circuit with a varying density of two-qubit gates. In addition to a known anticoncentration regime, we identify novel parameter conditions where the model is classically simulable or the output distribution follows the Porter-Thomas distribution. By showing that those parameter regimes do not coincide, we argue the presence of more than two phases in the model. The learnability of the output distribution of this model is further studied, which indicates that an energy-based model fails to learn the output distribution even when it is not anticoncentrated. Our study reveals that a quantum circuit model can have multiple fine-grained complexity phases, suggesting the potential for quantum advantage even when the output distribution is far from the Porter-Thomas distribution.
Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones

With the increasing use of smartphones in our daily lives, these devices have become capable of performing many complex tasks. Concerning the need for continuous monitoring of vital signs, especially for the elderly or those with certain types of diseases, the development of algorithms that can estimate vital signs using smartphones has attracted researchers worldwide. In particular, researchers have been exploring ways to estimate vital signs, such as heart rate, oxygen saturation levels, and respiratory rate, using algorithms that can be run on smartphones. However, many of these algorithms require multiple pre-processing steps that might introduce some implementation overheads or require the design of a couple of hand-crafted stages to obtain an optimal result. To address this issue, this research proposes a novel end-to-end solution to mobile-based vital sign estimation using deep learning that eliminates the need for pre-processing. By using a fully convolutional architecture, the proposed model has much fewer parameters and less computational complexity compared to the architectures that use fully-connected layers as the prediction heads. This also reduces the risk of overfitting. Additionally, a public dataset for vital sign estimation, which includes 62 videos collected from 35 men and 27 women, is provided. Overall, the proposed end-to-end approach promises significantly improved efficiency and performance for on-device health monitoring on readily available consumer electronics.
Detecting Text Formality: A Study of Text Classification Approaches

Formality is one of the important characteristics of text documents. The automatic detection of the formality level of a text is potentially beneficial for various natural language processing tasks. Before, two large-scale datasets were introduced for multiple languages featuring formality annotation—GYAFC and X-FORMAL. However, they were primarily used for the training of style transfer models. At the same time, the detection of text formality on its own may also be a useful application. This work proposes the first to our knowledge systematic study of formality detection methods based on statistical, neural-based, and Transformer-based machine learning methods and delivers the best-performing models for public usage. We conducted three types of experiments – monolingual, multilingual, and cross-lingual. The study shows the overcome of Char BiLSTM model over Transformer-based ones for the monolingual and multilingual formality classification task, while Transformer-based classifiers are more stable to cross-lingual knowledge transfer.
Unit-Disk Range Searching and Applications

Given a set $P$ of $n$ points in the plane, we consider the problem of computing the number of points of $P$ in a query unit disk (i.e., all query disks have the same radius). We show that the main techniques for simplex range searching in the plane can be adapted to this problem. For example, by adapting Matou\v{s}ek's results, we can build a data structure of $O(n)$ space so that each query can be answered in $O(\sqrt{n})$ time. Our techniques lead to improvements for several other classical problems, such as batched range searching, counting/reporting intersecting pairs of unit circles, distance selection, discrete 2-center, etc. For example, given a set of $n$ unit disks and a set of $n$ points in the plane, the batched range searching problem is to compute for each disk the number of points in it. Previous work [Katz and Sharir, 1997] solved the problem in $O(n^{4/3}\log n)$ time while our new algorithm runs in $O(n^{4/3})$ time.
Finding an almost perfect matching in a hypergraph avoiding forbidden submatchings

In 1973, Erd˝os conjectured the existence of high girth ( n, 3 , 2)-Steiner systems. Recently, Glock, K¨uhn, Lo, and Osthus and independently Bohman and Warnke proved the approximate version of Erd˝os’ conjecture. Just this year, Kwan, Sah, Sawhney, and Simkin proved Erd˝os’ conjecture. As for Steiner systems with more general parameters, Glock, K¨uhn, Lo, and Osthus conjectured the existence of high girth ( n, q, r )-Steiner systems. We prove the approximate version of their conjecture. This result follows from our general main results which concern ﬁnding perfect or almost perfect matchings in a hypergraph G avoiding a given set of submatchings (which we view as a hypergraph H where V ( H ) = E ( G )). Our ﬁrst main result is a common generalization of the classical theorems of Pippenger (for ﬁnding an almost perfect matching) and Ajtai, Koml´os, Pintz, Spencer, and Szemer´edi (for ﬁnding an independent set in girth ﬁve hypergraphs). More generally, we prove this for coloring and even list coloring, and also generalize this further to when H is a hypergraph with small codegrees (for which high girth designs is a speciﬁc instance). Indeed, the coloring version of our result even yields an almost partition of K rn into approximate high girth ( n, q, r )-Steiner systems. Our main results also imply the existence of a perfect matching in a bipartite hypergraph where the parts have slightly unbalanced degrees. This has a number of other applications; for example, it proves the existence of ∆ pairwise disjoint list colorings in the setting of Kahn’s theorem for list coloring the edges of a hypergraph; it also proves asymptotic versions of various rainbow matching results in the sparse setting (where the number of times a color appears could be much smaller than the number of colors) and even the existence of many pairwise disjoint rainbow matchings in such circumstances.
Rumor detection with self-supervised learning on texts and social graph

Rumor detection has become an emerging and active research field in recent years. At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth. However, existing works on rumor detection fall short in modeling heterogeneous information, either using one single information source only (e.g., social network, or post content) or ignoring the relations among multiple sources (e.g., fusing social and content features via simple concatenation). Therefore, they possibly have drawbacks in comprehensively understanding the rumors, and detecting them accurately. In this work, we explore contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better. Technically, we supplement the main supervised task of detection with an auxiliary self-supervised task, which enriches post representations via post self-discrimination. Specifically, given two heterogeneous views of a post (i.e., representations encoding social patterns and semantic patterns), the discrimination is done by maximizing the mutual information between different views of the same post compared to that of other posts. We devise cluster-wise and instance-wise approaches to generate the views and conduct the discrimination, considering different relations of information sources. We term this framework as self-supervised rumor detection (SRD). Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.
Influence of Repetition through Limited Recall

Decision makers who receive many signals are subject to imperfect recall. This is especially important when learning from feeds that aggregate messages from many senders on social media platforms. In this paper, we study a stylized model of learning from feeds and highlight the inefficiencies that arise due to imperfect recall. In our model, failure to recall a specific message comes from the accumulation of messages which creates interference. We characterize the influence of each sender according to the rate at which she sends messages and to the strength of interference. Our analysis indicates that imperfect recall not only leads to double-counting and extreme opinions in finite populations, but also impedes the ability of the receiver to learn the true state as the population of the senders increases. We estimate the strength of interference in an online experiment where participants are exposed to (non-informative) repeated messages and they need to estimate the opinion of others. Results show that interference plays a significant role and is weaker among participants who disagree with each other. Our work has implication for the diffusion of information in networks, especially when it is false because it is shared and repeated more than true information.
Flat bubble regime and laminar plasma flow in a plasma wakefield accelerator

A simple 2D model of the bubble formation in a plasma wakefield accelerator is developed and investigated. It is shown that in the case of a flat driver the bubble may consist of two parts that correspond to two different types of the plasma flow: a laminar flow where plasma electron streams do not cross and a two-stream (turbulent) flow. The laminar flow turns out to be robust to the symmetry breaking. Building-of of the developed model we demonstrate that in the case of the laminar flow and non-relativistic plasma electrons the transverse wake field is absent inside the bubble even in the case of a transversely nonuniform plasma.
Bidimensional Sand Pile and Ice Pile Models

In this paper we deﬁne an extension of the Sand Pile Model SPM and more generally of the Ice Pile Model IPM by adding a further dimension to the system. By drawing a parallel between these unidimensional and bidimensional models we will ﬁnd some common feautures and some diﬀerences. We will show that, like for SPM ( n ), not all plane partitions are accessible in BSPM ( n ) starting from the initial state. However, it appears to be much more diﬃcult to characterize the partitions that are accessible in BSPM ( n ): we will be able to give some necessary but not suﬃcient conditions for a partition to be accessible. On the other hand, we will show how several properties of the Ice Pile Model in one dimension can be generalized when one adds a second dimension.
Global Well-Posedness of a Class of Hyperbolic Cauchy Problems with Coefficients Sublogarithmic in Time

The goal of this paper is to study global well-posedness, cone of dependence and loss of regularity of the solutions to a class of strictly hyperbolic equations with coeﬃcients displaying “mild” blow-up of sublogarithmic order -| ln t | γ , γ ∈ (0 , 1) . The problems we study are of strictly hyperbolic type with respect to a generic weight and a metric on the phase space. The coeﬃcients are polynomially bound in x with their x -derivatives and t -derivative of order O( t − δ ) , δ ∈ [0 , 1) , and O ( t − 1 | ln t | γ − 1 ) , γ ∈ (0 , 1) , respectively. We employ the Planck function associated with the metric to subdivide the extended phase space and deﬁne appropriate generalized parameter dependent symbol classes. To arrive at an energy estimate, we perform a conjugation by a pseudodiﬀerential operator. This operator explains the loss of regularity by linking it to the metric on the phase space and the singular behavior. We call the conjugating operator as loss operator . We report that the solution experiences an arbitrarily small loss in relation to the initial datum deﬁned in the Sobolev space tailored to the loss operator.
Delirium diagnosis without a gold standard: Evaluating diagnostic accuracy of combined delirium assessment tools

Background Fluctuating course of delirium and complexities of ICU care mean delirium symptoms are hard to identify or commonly confused with other disorders. Delirium is difficult to diagnose, and clinicians and researchers may combine assessments from multiple tools. We evaluated diagnostic accuracy of different combinations of delirium assessments performed in each enrolled patient. Methods Data were obtained from a previously conducted cross-sectional study. Eligible adult patients who remained admitted to ICU for >24 hours with at least one family member present were consecutively enrolled as patient-family dyads. Clinical delirium assessments (Intensive Care Delirium Screening Checklist [ICSDC] and Confusion Assessment Method-ICU [CAM-ICU]) were completed twice daily by bedside nurse or trained research assistant, respectively. Family delirium assessments (Family Confusion Assessment Method and Sour Seven) were completed once daily by family members. We pooled all delirium assessment tools in a single two-class latent model and pairwise (i.e., combined, clinical or family assessments) Bayesian analyses. Results Seventy-three patient-family dyads were included. Among clinical delirium assessments, the ICDSC had lower sensitivity (0.72; 95% Bayesian Credible [BC] interval 0.54–0.92) and higher specificity (0.90; 95%BC, 0.82–0.97) using Bayesian analyses compared to pooled latent class analysis and CAM-ICU had higher sensitivity (0.90; 95%BC, 0.70–1.00) and higher specificity (0.94; 95%BC, 0.80–1.00). Among family delirium assessments, the Family Confusion Assessment Method had higher sensitivity (0.83; 95%BC, 0.71–0.92) and higher specificity (0.93; 95%BC, 0.84–0.98) using Bayesian analyses compared to pooled latent class analysis and the Sour Seven had higher specificity (0.85; 95%BC, 0.67–0.99) but lower sensitivity (0.64; 95%BC 0.47–0.82). Conclusions Results from delirium assessment tools are often combined owing to imperfect reference standards for delirium measurement. Pairwise Bayesian analyses that explicitly accounted for each tool’s (performed within same patient) prior sensitivity and specificity indicate that two combined clinical or two combined family delirium assessment tools have fair diagnostic accuracy.
Patient-based benefit-risk assessment of medicines: development, refinement, and validation of a content search strategy to retrieve relevant studies

Introduction: Poor indexing and inconsistent use of terms and keywords may prevent efficient retrieval of studies on the patient-based benefit-risk assessment (BRA) of medicines. We aimed to develop and validate an objectively derived content search strategy containing generic search terms that can be adapted for any search for evidence on patient-based BRA of medicines for any therapeutic area. Methods: We used a robust multistep process to develop and validate the content search strategy: (1) we developed a bank of search terms derived from screening studies on patient-based BRA of medicines in various therapeutic areas, (2) we refined the proposed content search strategy through an iterative process of testing sensitivity and precision of search terms, and (3) we validated the final search strategy in PubMed by firstly using multiple sclerosis as a case condition and secondly computing its relative performance versus a published systematic review on patient-based BRA of medicines in rheumatoid arthritis. Results: We conceptualized a final search strategy to retrieve studies on patient-based BRA containing generic search terms grouped into two domains, namely the patient and the BRA of medicines (sensitivity 84%, specificity 99.4%, precision 20.7%). The relative performance of the content search strategy was 85.7% compared with a search from a published systematic review of patient preferences in the treatment of rheumatoid arthritis. We also developed a more extended filter, with a relative performance of 93.3% when compared with a search from a published systematic review of patient preferences in lung cancer.
COVID-19 precautionary practices and associated factors among clients visiting a tertiary hospital, Addis Ababa, Ethiopia

Background Despite efforts to contain the spread of COVID-19, Addis Ababa, the country’s COVID-19 epicenter, is experiencing a sharp increase in the number of cases and death rate. While poor public adherence to COVID-19 precautionary practices is evident, factors associated with it are not well studied. We aimed to assess the level of practice of COVID-19 precaution and associated factors. Methods This was a hospital-based cross-sectional study conducted from February 1st to 15th, 2021 at Saint Paul’s Hospital Millennium Medical College, a tertiary teaching hospital in Addis Ababa, Ethiopia. We used a structured questionnaire to conduct a face-to-face exit interview with clients visiting the hospital. Systematic random sampling was employed to recruit study participants. Binary and multivariable logistic regressions were implemented to examine factors associated with precautionary practices. Statistical significance was declared at p-value <0.05. The Crude odds ratio (COR) and Adjusted odds ratio (AOR) were reported with a 95% confidence interval. Results We analyzed data obtained from 262 participants. The mean age of participants was 36 (SD+12) years. The majority (207, 79%) of the study participants had a favorable attitude towards prevention and control measures of COVID-19. A little over half (116, 55.7%) of the respondents had a satisfactory level of practice of COVID-19 precautions. Living in an area with strict enforcement of COVID-19 precautionary measures [AOR: 2.25, 95% CI (1.22–4.15)], and having a favorable attitude of COVID-19 prevention [AOR: 4.88, 95% CI (2.08–11.68)] were significantly associated with satisfactory COVID-19 precautionary practices. Conclusions The level of practice of COVID-19 precaution was unsatisfactory. Favorable attitude and stricter enforcement of COVID-19 preventive measures might have contributed more to adherence to precautionary practices. The findings highlight the need for a public health education strategy targeted at improving attitudes of the community on COVID-19 focusing on the effectiveness of preventive measures.