Miracle-dz/UniFAR-FTUs huggingface dataset:
Herding on Tricky Questions

We explore a simple twist of the canonical binary-state observational learning model wherein signal precision is common to all players but is unknown. We characterize the condition when an information cascade occurs and show that it is less likely to occur in our model compared with Bikhchandani et al.’s (1992) canonical model. Furthermore, the probability of correct cascades may be non-monotonic in the signal’s precision.
Cross DQN: Cross Deep Q Network for Ads Allocation in Feed

E-commerce platforms usually display a mixed list of ads and organic items in feed. One key problem is to allocate the limited slots in the feed to maximize the overall revenue as well as improve user experience, which requires a good model for user preference. Instead of modeling the influence of individual items on user behaviors, the arrangement signal models the influence of the arrangement of items and may lead to a better allocation strategy. However, most of previous strategies fail to model such a signal and therefore result in suboptimal performance. In addition, the percentage of ads exposed (PAE) is an important indicator in ads allocation. Excessive PAE hurts user experience while too low PAE reduces platform revenue. Therefore, how to constrain the PAE within a certain range while keeping personalized recommendation under the PAE constraint is a challenge. In this paper, we propose Cross Deep Q Network (Cross DQN) to extract the crucial arrangement signal by crossing the embeddings of different items and modeling the crossed sequence by multi-channel attention. Besides, we propose an auxiliary loss for batch-level constraint on PAE to tackle the above-mentioned challenge. Our model results in higher revenue and better user experience than state-of-the-art baselines in offline experiments. Moreover, our model demonstrates a significant improvement in the online A/B test and has been fully deployed on Meituan feed to serve more than 300 millions of customers.
Physics‐informed neural network applied to surface‐tension‐driven liquid film flows

A physics‐informed neural network (PINN), which has been recently proposed by Raissi et al. [J Comput Phys. 2019;378:686–707], is applied to the partial differential equation (PDE) of liquid film flows. The PDE considered is the time evolution of the thickness distribution h(x,t)$$ h\left(x,t\right) $$ owing to the Laplace pressure, which involves fourth‐order spatial derivative and fourth‐order nonlinear term. Even for such a PDE, it is confirmed that the PINN can predict the solutions with sufficient accuracy. Nevertheless, some improvements are needed in training convergence and accuracy of the solutions. The precision of floating‐point numbers is a critical issue for the present PDE. When the calculation is executed with a single precision floating‐point number, the optimization is terminated due to the loss of significant digits. Calculation of the automatic differentiation dominates the computational time required for training and becomes exponentially longer with increasing order of derivatives. By splitting the original fourth‐order single PDE into lower‐order coupled PDEs, the computational time for each training iteration is greatly reduced. The sampling density of training data also significantly affects training convergence. For the problem considered in this study, improved convergence was obtained by allowing the sampling density of training data to be greater in earlier time ranges, where the rapid flattening of the thickness occurs.
Exponentially Enhanced Quantum Non-Hermitian Sensing via Optimized Coherent Drive

Liying Bao, Bo Qi, Daoyi Dong Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China School of Engineering and Information Technology, University of New South Wales, Canberra ACT 2600, Australia qibo@amss.ac. cn (Dated: December 30, 2021)
Incident‐Polarization‐Independent Spin Hall Effect of Light Reaching Half Beam Waist

The spin Hall effect of light (SHEL), a spin‐dependent transverse splitting of light at an optical interface, is intrinsically an incident‐polarization‐sensitive phenomenon. Recently, an approach to eliminate the polarization dependence by equalizing the reflection coefficients of two linear polarizations has been proposed, but is only valid when the beam waist is sufficiently larger than the wavelength. Here, it is demonstrated that an interface, at which the reflection coefficients of the two linear polarizations are the same and so are their derivatives with respect to the incident angle, supports the polarization‐independent spin Hall shift, even when the beam waist is comparable to the wavelength. In addition, an isotropic–anisotropic interface that exhibits the polarization‐independent spin Hall shift over the entire range of incident angles is presented. Monte‐Carlo simulations prove that spin Hall shifts are degenerate under any polarization and reach a half of beam waist under unpolarized incidence. An application of the beam‐waist‐scale SHEL as a tunable beam‐splitting device that is responsive to the incident polarization is suggested. The spin Hall shift that is independent of the incident polarization at any incident angle will facilitate a wide range of applications including practical spin‐dependent devices and active beam splitters.
NeuralFMU: Towards Structural Integration of FMUs into Neural Networks

This paper covers two major subjects: First, the presentation of a new open-source library called FMI.jl for integrating FMI into the Julia programming environment by providing the possibility to load, parameterize and simulate FMUs. Further, an extension to this library called FMIFlux.jl is introduced, that allows the integration of FMUs into a neural network topology to obtain a NeuralFMU. This structural combination of an industry typical black-box model and a data-driven machine learning model combines the different advantages of both modeling approaches in one single development environment. This allows for the usage of advanced data driven modeling techniques for physical effects that are difficult to model based on first principles.
Suppression of circ_0008932 inhibits tumor growth and metastasis in osteosarcoma by targeting miR-145-5p

Osteosarcoma (OS) is a common type of primary malignant tumor. Although the pathogenesis of OS has been extensively studied, the underlying molecular mechanisms have remained to be fully elucidated. Accumulating evidence has revealed that dysregulation of various circular RNAs (circRNAs) is associated with tumorigenesis and recent studies have indicated that circRNA circ_0008932 is aberrantly expressed in tumors. In the present study, the expression and detailed function of circ_0008932 in OS were elucidated. The levels of circ_0008932 in OS samples and cell lines were examined using reverse transcription-quantitative PCR. A cell model with circ_0008932 knockdown was generated using specific small interfering RNA (si-circ_0008932). Cell viability was determined by a Cell Counting Kit-8 assay, the cell migratory/invasive capacity was evaluated using Transwell assays and cell apoptosis was assessed by flow cytometry. The results suggested that circ_0008932 was upregulated in most primary OS tumors, suggesting that circ_0008932 is associated with the development of OS. In the in vitro assays, si-circ_0008932 inhibited the proliferation, migration and invasion of OS cells, while apoptosis was promoted. A luciferase reporter assay revealed that circ_0008932 may downregulate microRNA (miR)-145-5p through direct binding. Furthermore, the expression of miR-145-5p was negatively correlated with circ_0008932 levels in OS specimens. In addition, further functional studies indicated that miR-145-5p inhibitors eliminated the effects caused by si-circ_0008932 in OS cells. In comparison, the changes in the biological behavior of OS cells transfected with si-circ_0008932 were enhanced by miR-145-5p. In summary, circ_0008932 may be a novel oncogenic factor during the progression and development of OS by targeting miR-145-5p; more importantly, circ_0008932 may be a potential therapeutic target for OS.
Sema4C modulates the migration of primary tumor-associated lymphatic endothelial cells via an ERK-mediated pathway

Although lymphatic endothelial cells (LECs) serve a positive role in tumor lymphatic metastasis, the regulation of LECs undergoing migration similar to that of tumor cells remains poorly understood. A previous study revealed that semaphorin 4C (Sema4C) could be a marker of LECs in cervical cancer. Thus, the present study aimed to understand the mechanism via which Sema4C could promote the development of tumor-associated characteristics in LECs in cervical cancer. Primary tumor-associated LECs (TLECs) were distinguished from cervical cancer by flow cytometry. The promigratory ability was assessed using the Transwell assay. Lentivirus infection was used to alter the expression of Sema4C in TLECs. Confocal laser scanning was used to determine the infection efficiency of lentivirus infection. Sema4C/ERK/E-cadherin pathway was measured by reverse transcription-quantitative PCR and western blotting. The co-localization of Sema4C and the lymphatic marker lymphatic vessel endothelial hyaluronan receptor 1 was verified. Primary tumor-associated LECs (TLECs) were isolated from a mouse xenograft cervical tumor model. It was revealed that overexpressing Sema4C stimulated the migratory ability of TLECs, downregulated E-cadherin expression and stimulated ERK phosphorylation, whereas knocking down Sema4C had the opposite effects. The treatment of PD98059 (ERK inhibitor) blocked the pro-migratory ability of TLECs, which indicated a dependence on the ERK signaling pathway. It was identified that the Sema4C/ERK/E-cadherin pathway may be critical for the migration of TLECs, which may promote lymph node metastasis. Therefore, Sema4C could be a promising target for the treatment of cervical cancer with lymphatic metastasis.
Dynamic Locomotion Teleoperation of a Wheeled Humanoid Robot Reduced Model with a Whole-Body Human-Machine Interface

Bilateral teleoperation provides humanoid robots with human planning intelligence while enabling the human to feel what the robot feels. It has the potential to transform physically capable humanoid robots into dynamically intelligent ones. However, dynamic bilateral locomotion teleoperation remains as a challenge due to the complex dynamics it involves. This work presents our initial step to tackle this challenge via the concept of wheeled humanoid robot locomotion teleoperation by body tilt. Specifically, we developed a force-feedback-capable whole-body human-machine interface (HMI), and designed a force feedback mapping and two teleoperation mappings that map the human's body tilt to the robot's velocity or acceleration. We compared the two mappings and studied the force feedback's effect via an experiment, where seven human subjects teleoperated a simulated robot with the HMI to perform dynamic target tracking tasks. The experimental results suggest that all subjects accomplished the tasks with both mappings after practice, and the force feedback improved their performances. However, the subjects exhibited two distinct teleoperation styles, which benefited from the force feedback differently. Moreover, the force feedback affected the subjects' preferences on the teleoperation mappings, though most subjects performed better with the velocity mapping.
Ramsey simplicity of random graphs

A graph G is q-Ramsey for another graph H if in any q-edge-colouring of G there is a monochromatic copy of H, and the classic Ramsey problem asks for the minimum number of vertices in such a graph. This was broadened in the seminal work of Burr, Erdős, and Lovász to the investigation of other extremal parameters of Ramsey graphs, including the minimum degree. It is not hard to see that if G is minimally q-Ramsey forH we must have δ(G) ≥ q(δ(H)−1)+1, and we say that a graph H is q-Ramsey simple if this bound can be attained. Grinshpun showed that this is typical of rather sparse graphs, proving that the random graph G(n, p) is almost surely 2-Ramsey simple when log n n p n−2/3. In this paper, we explore this question further, asking for which pairs p = p(n) and q = q(n, p) we can expect G(n, p) to be q-Ramsey simple. We resolve the problem for a wide range of values of p and q; in particular, we uncover some interesting behaviour when n−2/3 p n−1/2.
Endoscopic submucosal dissection of a lateral spreading tumor involving the appendiceal orifice using a multi-traction device.

Endoscopic submucosal dissection (ESD) is the technique of choice for the resection of large superficial colorectal lesions [1], but it can be technically challenging in some situations. For this reason, lesions involving the appendiceal orifice have long been removed surgically. However, recent findings have found that ESD is effective and safe in that situation [2, 3], even for lesions deeply invading the appendiceal orifice (Toyonaga 3 lesions) with the help of traction device [4, 5]. Here we report the case of a 70-year-old woman with a history of appendectomy who underwent a colonoscopy that revealed a granular lateral spreading tumor (LST-G) of 3 cm invading the previous site of the appendectomy (Toyonaga 3a) (▶Fig. 1). An ESD using a multi-intertwined loop device was chosen (▶Video 1). After circumferential mucosal incision around the lesion, the first loop of the device was attached to one edge of the lesion with a hemostatic clip. Then a second loop was attached to the opposite edge of the lesion. Then the entire device was attached to the opposite colonic wall. The submucosal space was well exposed at the two edges of the LST-G, allowing a safe dissection during the whole procedure with an en-bloc resection (▶Fig. 2, ▶Fig. 3). The patient was discharged the day after without E-Videos
Influenza virus genotype to phenotype predictions through machine learning: a systematic review

ABSTRACT Background: There is great interest in understanding the viral genomic predictors of phenotypic traits that allow influenza A viruses to adapt to or become more virulent in different hosts. Machine learning techniques have demonstrated promise in addressing this critical need for other pathogens because the underlying algorithms are especially well equipped to uncover complex patterns in large datasets and produce generalizable predictions for new data. As the body of research where these techniques are applied for influenza A virus phenotype prediction continues to grow, it is useful to consider the strengths and weaknesses of these approaches to understand what has prevented these models from seeing widespread use by surveillance laboratories and to identify gaps that are underexplored with this technology. Methods and Results: We present a systematic review of English literature published through 15 April 2021 of studies employing machine learning methods to generate predictions of influenza A virus phenotypes from genomic or proteomic input. Forty-nine studies were included in this review, spanning the topics of host discrimination, human adaptability, subtype and clade assignment, pandemic lineage assignment, characteristics of infection, and antiviral drug resistance. Conclusions: Our findings suggest that biases in model design and a dearth of wet laboratory follow-up may explain why these models often go underused. We, therefore, offer guidance to overcome these limitations, aid in improving predictive models of previously studied influenza A virus phenotypes, and extend those models to unexplored phenotypes in the ultimate pursuit of tools to enable the characterization of virus isolates across surveillance laboratories.
Protective Role of Caregiver Preparedness on the Relationship Between Depression and Quality of Life in Stroke Dyads

Supplemental Digital Content is available in the text. Background and Purpose: Depression and quality of life (QOL) have an interdependent and transactional nature in stroke survivor-caregiver dyads. While the strong relationship between depression and physical and emotional QOL in stroke survivor-caregiver dyads is well known, it is less clear if this relationship is moderated by caregiver preparedness, which could easily be targeted with interventions. In this study, we examined the moderating role of caregiver preparedness on the association between depression and QOL in stroke survivor-caregiver dyads. Methods: We used a longitudinal design with follow-ups every 3 months over a 1-year period. Considering the nonindependent nature of the data (survivors and their caregivers), we used multilevel modeling to analyze data at the dyad level. We implemented 4 longitudinal dyadic moderation models (one for each QOL domain: physical, psychological, social, and environmental) using hierarchical linear modeling. Results: A sample of 222 stroke survivor-caregiver dyads was analyzed. Stroke survivors were older (M=70.8, SD=11.9) than their caregivers (M=52.4, SD=13.1). Stroke survivors predominantly had an ischemic stroke, equally distributed by site. Caregivers were primarily female (66%), with a medium to high educational level (57%). Caregiver preparedness significantly moderated the association between survivor depression and survivor psychological (B=0.56, P<0.01) and environmental (B=0.58, P<0.01) QOL at baseline and social QOL over time (B=0.24, P<0.05). Similarly, caregiver preparedness significantly moderated the association between caregiver depression and caregiver physical (B=0.25, P<0.01) and environmental (B=0.18, P<0.05) QOL over time. Conclusions: Caregiver preparedness has a positive influence on both members of the dyad. Assessment of stroke-caregiver preparedness could be helpful to motivate clinicians to develop and implement interventions for stroke survivor-caregiver dyads.
INTERCAAT: identifying interface residues between macromolecules

SUMMARY The Interface Contact definition with Adaptable Atom Types (INTERCAAT) was developed to determine the atomic interactions between molecules that form a known three dimensional structure. First, INTERCAAT creates a Voronoi tessellation where each atom acts as a seed. Interactions are defined by atoms that share a hyperplane and whose distance is less than the sum of each atoms' Van der Waals radii plus the diameter of a solvent molecule. Interacting atoms are then classified and interactions are filtered based on compatibility. INTERCAAT implements an adaptive atom classification method; therefore, it can explore interfaces between a variety macromolecules. AVAILABILITY Source code is freely available at (https://gitlab.com/fiserlab.org/intercaat). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Covid-19 Vaccine Effectiveness and the Test-Negative Design

Observational studies are emerging as fundamental sources of information about vaccine effectiveness outside the controlled environment of randomized trials, and they are being used to generate evidence of effectiveness against outcomes that are underpowered in trials, such as hospitalization or intensive care unit (ICU) admission, or for narrow subgroups.1 These studies can monitor the waning of vaccine effectiveness or measure the performance of vaccines against novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants when large randomized, controlled trials are not feasible.2 Thompson et al.3 now describe in the Journal the application of a retrospective test-negative design to estimate coronavirus disease 2019 (Covid-19) vaccine effectiveness in adults 50 years of age or older. A multisite network contributed data on 41,552 admissions to 187 hospitals and 21,522 visits to 221 emergency departments or urgent care clinics. These data were derived from patients who had accessed medical care for Covid-19–like illness and had had molecular testing for SARS-CoV-2. In the test-negative design, the case patients were those who tested positive for SARS-CoV-2, and the control patients were those who tested negative. Vaccine effectiveness was estimated by comparing the odds of vaccination between cases and controls. Table 1 shows how data from such studies may be used to calculate vaccine effectiveness. The test-negative design may be used to estimate vaccine effectiveness against medically attended, laboratory-confirmed SARS-CoV-2 infection among patients who would seek — and have access to — medical care.4 Thompson et al. estimated effectiveness with respect to three distinct outcomes: an emergency department or urgent care visit, hospitalization, and admission to an ICU. Each effectiveness measure reflected the
Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations

Learning from Demonstration (LfD) provides an intuitive and fast approach to program robotic manipulators. Task parameterized representations allow easy adaptation to new scenes and online observations. However, this approach has been limited to pose-only demonstrations and thus only skills with spatial and temporal features. In this work, we extend the LfD framework to address forceful manipulation skills, which are of great importance for industrial processes such as assembly. For such skills, multi-modal demonstrations including robot end-effector poses, force and torque readings, and operation scene are essential. Our objective is to reproduce such skills reliably according to the demonstrated pose and force profiles within different scenes. The proposed method combines our previous work on task-parameterized optimization and attractor-based impedance control. The learned skill model consists of (i) the attractor model that unifies the pose and force features, and (ii) the stiffness model that optimizes the stiffness for different stages of the skill. Furthermore, an online execution algorithm is proposed to adapt the skill execution to real-time observations of robot poses, measured forces, and changed scenes. We validate this method rigorously on a 7-DoF robot arm over several steps of an E-bike motor assembly process, which require different types of forceful interaction such as insertion, sliding and twisting.
Mean-Square Analysis with An Application to Optimal Dimension Dependence of Langevin Monte Carlo

Sampling algorithms based on discretizations of Stochastic Differential Equations (SDEs) compose a rich and popular subset of MCMC methods. This work provides a general framework for the non-asymptotic analysis of sampling error in 2-Wasserstein distance, which also leads to a bound of mixing time. The method applies to any consistent discretization of contractive SDEs. When applied to Langevin Monte Carlo algorithm, it establishes (cid:101) O (cid:16) √ d / (cid:15) (cid:17) mixing time, without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures at inﬁnity. This bound improves the best previously known (cid:101) O (cid:0) d / (cid:15) (cid:1) result and is optimal (in terms of order) in both dimension d and accuracy tolerance (cid:15) for target measures satisfying the aforementioned assumptions. Our theoretical analysis is further validated by numerical experiments.
DIAGNOSIS AND MANAGEMENT OF BACTERIAL SIALADENITIS

Introduction: Various diseases ranging from obstruction, infection, to benign and malignant tumors occur in the salivary glands. The most common problem is the blockage of the salivary gland ducts which prevents salivary drainage. Method: This article was created using the literature review method, involving 30 references in both national and international books and journals. Discussion: Sialadenitis is inflammation or infection of the salivary glands which can affect the parotid, submandibular and minor salivary glands. Sialadenitis can be caused by infectious or non-communicable factors. Bacterial or viral infections are the most frequent etiologies. Staphylococcus is a common cause of bacteria, while paramyxovirus is a Tinjauan