Miracle-dz/UniFAR-FTUs huggingface dataset:
Myopic Bike and Say Hi: Games for Empathizing with The Myopic

Myopia is an eye condition that makes it difficult for people to focus on faraway objects. It has become one of the most serious eye conditions worldwide and negatively impacts the quality of life of those who suffer from it. Although myopia is prevalent, many non-myopic people have misconceptions about it and encounter challenges empathizing those who suffer from it. In this research, we developed two virtual reality (VR) games, (1) ”Myopic Bike” and (2) ”Say Hi”, to provide a means for the non-myopic population to experience the difficulties of myopic people. Our two games simulate two inconvenient daily life scenarios (riding a bicycle and greeting friends on the street) that myopic people encounter when not wearing glasses. The goal is to facilitate empathy in people with non-myopia for those who suffer from myopia. We evaluated four participants’ game experiences through questionnaires and semi-structured interviews. Overall, our two VR games can create an engaging and non-judgmental experience for the non-myopic people that has potential to facilitate empathizing with those who suffer from myopia.
On Shallow Packings and Tusnády's Problem

Tusnády’s problem asks to bound the discrepancy of points and axis-parallel boxes in R. Algorithmic bounds on Tusnády’s problem use a canonical decomposition of Matoušek for the system of points and axis-parallel boxes, together with other techniques like partial coloring and / or random-walk based methods. We use the notion of shallow cell complexity and the shallow packing lemma, together with the chaining technique, to obtain an improved decomposition of the set system. Coupled with an algorithmic technique of Bansal and Garg for discrepancy minimization, which we also slightly extend, this yields improved algorithmic bounds on Tusnády’s problem. For d ≥ 5, our bound matches the lower bound of Ω(logd−1 n) given by Matoušek, Nikolov and Talwar [IMRN, 2020] – settling Tusnády’s problem, upto constant factors. For d = 2, 3, 4, we obtain improved algorithmic bounds of O(log n), O(log n) and O(log n) respectively, which match or improve upon the non-constructive bounds of Nikolov for d ≥ 3. Further, we also give improved bounds for the discrepancy of set systems of points and polytopes in R generated via translations of a fixed set of hyperplanes. As an application, we also get a bound for the geometric discrepancy of anchored boxes in R with respect to an arbitrary measure, matching the upper bound for the Lebesgue measure, which improves on a result of Aistleitner, Bilyk, and Nikolov [MC and QMC methods, Springer, Proc. Math. Stat., 2018] for d ≥ 4.
Comparison of Functional Connectivity during Visual-Motor Illusion, Observation, and Motor Execution

Abstract This study investigated the functional connectivity during visual-motor illusion and compared it with observation and motor execution using functional near-infrared spectroscopy (fNIRS). Thirty subjects were randomly assigned to: illusion, observation, and motor execution group. Illusion group watched own finger joint movement video image and induced kinesthetic illusion, while the other group only performed observation or motor execution. Continuous brain activity was measured using fNIRS and functional connectivity was analyzed. The illusion group perceived (using 7-point Likert scale) a higher degree of kinesthetic illusion and sense of body ownership than the observation group. Visual-motor illusion was associated with stronger functional connectivity between the left premotor cortex and the left parietal area compared with observation and motor execution only, suggesting that these areas respond to visual-motor illusion.
Up-regulation of circARF3 reduces blood-brain barrier damage in rat subarachnoid hemorrhage model via miR-31-5p/MyD88/NF-κB axis

Inflammation events have been found to aggravate brain injury and blood-brain barrier (BBB) damage following subarachnoid hemorrhage (SAH). This study probed the role and mechanism of a novel circRNA, circARF3, in regulating the BBB injury in SAH rats and hypoxia-induced vascular endothelial cell (VEC) injury in vitro. Levels of circARF3 and miR-31-5p were monitored by RT-PCR. The expression of inflammatory factors IL-1β and TNF-α was verified by ELISA. In vivo SAH model was constructed in Sprague Dawley (SD) rats. The BBB integrity and cerebral edema, as well as the neurological functions of the rats were evaluated. The apoptotic neurons and microglia in brain lesions were examined by immunohistochemistry (IHC). The MyD88/NF-κB pathway was tested by Western blot. Furthermore, gain-of functional assay were constructed to explore the effects of circARF3 and miR-31-5p in primary cultured brain microvascular endothelial cell (BMEC) injury and microglial inflammation induced by oxygen and glucose deprivation (OGD). circARF3 was significantly down-regulated in plasma and CSF in SAH patients with higher Fisher stages. In the SAH rat model, overexpressing circARF3 improved BBB integrity and neurological score, decreased neuronal apoptosis and microglial activation in ipsilateral basal cortex, with declined miR-31-5p expression and MyD88-NF-κB activation. In vitro, overexpressing circARF3 attenuated OGD-mediated integrity destruction of BMECs and microglial induced neuroinflammation, while overexpressing miR-31-5p had opposite effects. Mechanistically, circARF3 sponged miR-31-5p as an endogenous competitive RNA and dampens its expression, thus inactivating MyD88-NF-κB pathway. CircARF3 attenuates BBB destruction in SAH rats by regulating the miR-31-5p-activated MyD88-NF-κB pathway.
The Effect of Supervision in Vestibular Rehabilitation in Patients with Acute or Chronic Unilateral Vestibular Dysfunction: A Systematic Review

Objective: The investigation of supervised vestibular rehabilitation treatment role for individuals with dizziness and imbalance due to peripheral, unilateral vestibular disorders. Databases Reviewed: Cochrane, PubMed, and Physiotherapy Evidence Database (PEDro) were utilized to identify relevant studies. Methods: The key search terms used were “Vestibular Rehabilitation and Unilateral Vestibular Hypofunction,” “Vestibular Rehabilitation and Unilateral Vestibular Loss,” and “Vestibular Rehabilitation and Supervision. ” A manual search was performed by exploring the references of included articles to identify studies not captured through the computer-based searches. The quality of the studies was assessed according to the PEDro scale. Inclusion criteria were: 1) studies with patients, aged from 18 to 80 years, with acute or chronic dizziness and disequilibrium due to unilateral vestibular dysfunction, 2) randomized control trials (RCTs), 3) studies comparing supervised vestibular rehabilitation program with an unsupervised vestibular rehabilitation program or home-based training or standard care or placebo, and 4) articles written in the English language. Studies reporting cases of vertigo and imbalance due to possible recurrent pathologies, acute benign paroxysmal positional vertigo, or central neurological/orthopedic deficits, were excluded. Results: A total of 448 articles were retrieved from the systematic database search strategy. Five of them were included in the systematic review after full-text analysis, plus one more after manual searching of their references. All studies involved supervised vestibular rehabilitation treatment programs compared with unsupervised home training, the performance of daily activities and, standard care. Based on PEDro's scoring system, one study rated as high-quality RCT, three studies were considered of fair quality and one scored as low-quality RCT. Conclusions: Although most RCTs report better outcomes with a supervised vestibular rehabilitation treatment program regarding the emotional status, dizziness, and balance improvement, this systematic review failed to provide a strong evidence that supervision is superior to unsupervised protocols in patients with UNH. The self-reported subjective measures used by the included RCTs represent a serious limitation of their results.
Second Wave of the COVID-19 Pandemic in Delhi, India: High Seroprevalence Not a Deterrent?

Background We report the findings of a large follow-up community-based serosurvey and correlating it with the COVID-19 test-positivity rate and the case load observed during the peak of the second wave of the Covid-19 pandemic in Delhi, India. Methods Individuals of age more than or equal to 5 years were recruited from 274 wards of the state (population ~ 19.6 million) during January 11 to January 22, 2021. A total of 100 participants each were included from all the wards for a net sample size of ~28,000. A multi-stage sampling technique was applied for selection of participants for the household serosurvey. Anti SARS CoV-2 IgG antibodies were detected by using the VITROS assay (90% Sn, 100% Sp). Results Antibody positivity was observed in 14,298 (50.76%) of the 28,169 samples. The age, sex and district population weighted seroprevalence of the IgG SARS-CoV-2 was 50.52% (95% C.I. 49.94-51.10) and after adjustment for assay characteristics was 56.13% (95% C.I. 55.49- 56.77). On adjusted analysis, participants aged more than or equal to 50 years, of female gender, housewives, having ever lived in containment zones, urban slum dwellers, and diabetes or hypertensive patients had significantly higher odds of SARS-CoV-2 antibody positivity. The peak infection rate and the test positivity rate since October 2020 were initially observed in mid-November 2020 with a subsequent steep declining trend, followed by a period of persistently low case burden lasting until the first week of March 2021. This was followed by a steady increase followed by an exponential surge in infections from April 2021 onwards culminating in the second wave of the pandemic. Conclusions The presence of infection induced immunity from SARS-CoV-2 even in more than one in two people can be ineffective in protecting the population. Keywords: Covid-19; Serosurvey; Delhi; Pandemic wave
Mitochondrial bioenergetics boost macrophage activation, promoting liver regeneration in metabolically compromised animals

Hepatic ischemia–reperfusion injury (IRI) is the leading cause of early posttransplantation organ failure as mitochondrial respiration and ATP production are affected. A shortage of donors has extended liver donor criteria, including aged or steatotic livers, which are more susceptible to IRI. Given the lack of an effective treatment and the extensive transplantation waitlist, we aimed at characterizing the effects of an accelerated mitochondrial activity by silencing methylation‐controlled J protein (MCJ) in three preclinical models of IRI and liver regeneration, focusing on metabolically compromised animal models.
Research on the factors that influence patients with colorectal cancer participating in the prevention and control of surgical site infection: Based on the extended theory of planned behaviour

The most common and severe type of nosocomial infection in patients with colorectal cancer is surgical site infection (SSI). Patient‐related factors are an important components of SSI. So it is necessary to participate in SSI prevention and control. It is important to identify the factors that influence patients' participation behaviour and to explore the mechanism of these effects.
Food group intake of children and adolescents (6–18 years) on a vegetarian, vegan or omnivore diet: results of the VeChi Youth Study

Abstract Plant-based, i.e. vegetarian (without meat and fish) or vegan (exclusively plant-based foods) diets are in trend also among children and adolescents, but data on food intake in this group are lacking. Here, we compare the consumption of food groups of vegetarian (n 145), vegan (n 110) and omnivore (n 135) children and adolescents (6–18 years) in Germany using data of the VeChi Youth Study. Each food item reported in 3 d weighed dietary records was assigned to one of eighteen food groups and individual mean intake per day (g/MJ) was calculated. Group differences were assessed using covariance analyses adjusted for age, sex and other covariates. For food groups with a high number of non-consumers, non-parametric Kruskal–Wallis tests were run. Pairwise comparison of vegetarian and vegan groups indicated significantly higher intakes of legumes, nuts, milk alternatives (all P = 0·0003) and meat alternatives (P = 0·0065) among vegan subjects. Intake of these food groups of omnivore participants was low (Q3:0·0 g/MJ for legumes, milk alternatives and meat alternatives, 0·5 g/MJ for nuts). Dairy intake of vegetarians (11·6 g/MJ) was significantly lower than of omnivore subjects (24·7 g/MJ) (P = 0·0003). Intake of fats/oils and sweet foods was lowest in vegan compared with vegetarian and omnivore participants (P< 0·05). Whole grain intake was higher in vegan participants (14·5 g/MJ) than of vegetarian (9·1 g/MJ) and omnivore (6·5 g/MJ) participants (P = 0·0003). Longitudinal studies are necessary to evaluate the long-term health consequences of vegetarian, vegan and omnivore food patterns, especially in childhood and adolescence.
Exploring Factors Associated With Depressive Symptoms Among Patients With Chronic Pain

Abstract This cross-sectional study examined the factors associated with depression among people with chronic pain (PwCP) attending specialized pain clinics in Muscat, Oman. Two-hundred eighty-seven participants were recruited for the study, and univariate analyses were used to investigate the difference between individuals who scored above/below the cutoff points for depressive symptoms. A multiple regression analysis was used to detect the independent predictors. Twenty-six percent of participants scored above the cutoff point. Further analysis indicated that unstable family relationships pre-existing depressive symptoms (odds ratio [OR], 2.86; p = 0.044), a family history of depression (OR, 4.75; p = 0.019), severe pain (OR, 4.21; p < 0.006), having fibromyalgia (OR, 28.29; p = 0.005), and lumbago/truck (OR, 2.41; p = 0.039) were independent predictors of depressive symptoms. This study indicates that one in four patients with chronic pain also presents with depressive symptoms. However, the role of culture needs to be taken into consideration when interpreting these findings and when building on these data.
College Student Retention Risk Analysis From Educational Database using Multi-Task Multi-Modal Neural Fusion

We develop a Multimodal Spatiotemporal Neural Fusion network for MTL (MSNF-MTCL) to predict 5 important students' retention risks: future dropout, next semester dropout, type of dropout, duration of dropout and cause of dropout. First, we develop a general purpose multi-modal neural fusion network model MSNF for learning students' academic information representation by fusing spatial and temporal unstructured advising notes with spatiotemporal structured data. MSNF combines a Bidirectional Encoder Representations from Transformers (BERT)-based document embedding framework to represent each advising note, Long-Short Term Memory (LSTM) network to model temporal advising note embeddings, LSTM network to model students' temporal performance variables and students' static demographics altogether. The final fused representation from MSNF has been utilized on a Multi-Task Cascade Learning (MTCL) model towards building MSNF-MTCL for predicting 5 student retention risks. We evaluate MSNF-MTCL on a large educational database consists of 36,445 college students over 18 years period of time that provides promising performances comparing with the nearest state-of-art models. Additionally, we test the fairness of such model given the existence of biases.
FASN inhibition sensitizes metastatic OSCC cells to cisplatin and paclitaxel by downregulating cyclin B1.

OBJECTIVES To investigate the potential effect of fatty acid synthase (FASN) inhibitor orlistat to enhance the effectiveness of chemotherapy drugs widely used to treat oral squamous cell carcinomas (OSCC), such as 5-fluorouracil, cisplatin, and paclitaxel. METHODS The OSCC SCC-9 LN-1 metastatic cell line, which expresses high levels of FASN, was used for drug combination experiments. Cell viability was analyzed by crystal violet staining and automatic cell counting. Apoptosis and cell cycle were analysed by flow cytometry with Annexin-V/7-AAD and propidium iodide staining, respectively. Cyclin B1, Cdc25C, Cdk1, FASN, and ERBB2 levels were assessed by western blotting. Finally, cell scratch and transwell assays were performed to assess cell migration and invasion. RESULTS Inhibition of FASN with orlistat sensitized SCC-9 LN-1 cells to the cytotoxic effects of paclitaxel and cisplatin, but not 5-fluorouracil, which was accompanied by a significant reduction in cyclin B1. The suppression of proliferation, migration, and invasion of SCC-9 LN-1 cells induced by orlistat plus cisplatin or paclitaxel was not superior to the effects of chemotherapy drugs alone. CONCLUSION Our results suggest that orlistat enhances the chemosensitivity of SCC-9 LN-1 cells to cisplatin and paclitaxel by downregulating cyclin B1.
Modeling of advanced accelerator concepts

Computer modeling is essential to research on Advanced Accelerator Concepts (AAC), as well as to their design and operation. This paper summarizes the current status and future needs of AAC systems and reports on several key aspects of (i) high-performance computing (including performance, portability, scalability, advanced algorithms, scalable I/Os and In-Situ analysis), (ii) the benefits of ecosystems with integrated workflows based on standardized input and output and with integrated frameworks developed as a community, and (iii) sustainability and reliability (including code robustness and usability).
Autonomous Head-to-Head Racing in the Indy Autonomous Challenge Simulation Race

—This paper describes Ariel Team’s autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race [1]. IAC is the ﬁrst multi-vehicle autonomous head-to-head competition, reaching speeds of 300 km/h along an oval track, modeled after the Indianapolis Motor Speedway (IMS). Our racing controller attempts to maximize progress along the track while avoiding collisions with opponent vehicles and obeying the race rules. To this end, the racing controller ﬁrst computes a race line ofﬂine. Then, it repeatedly computes online a small set of dynamically feasible maneuver candidates, each tested for collision with the opponent vehicles. Finally, it selects the maneuver that maximizes progress along the track, taking into account the race line. The maneuver candidates, as well as the predicted trajectories of the opponent vehicles, are approximated using a point mass model. Despite the simplicity of this racing controller, it managed to drive competitively and with no collision with any of the opponent vehicles in the IAC ﬁnal simulation race.
End-to-End Conversational Search for Online Shopping with Utterance Transfer

Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data. In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone. We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer. With utterance transfer, we introduce a new conversational search dataset for online shopping. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.
Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry

We present a multi-camera visual-inertial odometry system based on factor graph optimization which estimates motion by using all cameras simultaneously while retaining a fixed overall feature budget. We focus on motion tracking in challenging environments, such as narrow corridors, dark spaces with aggressive motions, and abrupt lighting changes. These scenarios cause traditional monocular or stereo odometry to fail. While tracking motion with extra cameras should theoretically prevent failures, it leads to additional complexity and computational burden. To overcome these challenges, we introduce two novel methods to improve multi-camera feature tracking. First, instead of tracking features separately in each camera, we track features continuously as they move from one camera to another. This increases accuracy and achieves a more compact factor graph representation. Second, we select a fixed budget of tracked features across the cameras to reduce back-end optimization time. We have found that using a smaller set of informative features can maintain the same tracking accuracy. Our proposed method was extensively tested using a hardware-synchronized device consisting of an IMU and four cameras (a front stereo pair and two lateral) in scenarios including: an underground mine, large open spaces, and building interiors with narrow stairs and corridors. Compared to stereo-only state-of-the-art visual-inertial odometry methods, our approach reduces the drift rate, relative pose error, by up to 80% in translation and 39% in rotation.
Correcting the User Feedback-Loop Bias for Recommendation Systems

Selection bias is prevalent in the data for training and evaluating recommendation systems with explicit feedback. For example, users tend to rate items they like. However, when rating an item concerning a specific user, most of the recommendation algorithms tend to rely too much on his/her rating (feedback) history. This introduces implicit bias on the recommendation system, which is referred to as user feedback-loop bias in this paper. We propose a systematic and dynamic way to correct such bias and to obtain more diverse and objective recommendations by utilizing temporal rating information. Specifically, our method includes a deep-learning component to learn each user's dynamic rating history embedding for the estimation of the probability distribution of the items that the user rates sequentially. These estimated dynamic exposure probabilities are then used as propensity scores to train an inverse-propensity-scoring (IPS) rating predictor. We empirically validated the existence of such user feedback-loop bias in real world recommendation systems and compared the performance of our method with the baseline models that are either without de-biasing or with propensity scores estimated by other methods. The results show the superiority of our approach.