Miracle-dz/UniFAR-FTUs huggingface dataset:
Atmosphere-Dependent Electron Relaxation of the Ag−Decorated TiO2 and the Relations with Photocatalytic Properties

In the current research, the atmosphere effects on the photoinduced electron relaxations of the undecorated TiO2 and Ag-decorated TiO2 (Ag/TiO2) were carefully studied by means of the in situ photoconductance and diffuse reflection measurements. In pure N2 atmosphere, the results showed that the electron relaxation mainly occurs through the transfer to the residual O2, and the Ag nanoparticles form a fast electron transfer pathway. It was seen that the apparent activation energy of the electron transfer to O2 was greatly reduced by the Ag decoration. In the methanol-containing N2 atmosphere, the electron relaxation can still occur via the transfer to residual O2 in the case of the undecorated TiO2, while the relaxation mechanism changes for the Ag/TiO2 as the relaxations are decreased with the temperatures. It is possible that the methanol molecule adsorbed on the Ag/TiO2 perimeters could act as the bridge for the recombination of the holes and the electrons stored in the Ag nanoparticles. Reducing the Ag nanoparticle size from 15 nm to 3 nm can greatly increase the electron relaxations due to the increase in Ag dispersion and Ag/TiO2 interconnection. Although the electron transfer to O2 was increased, both the photocatalytic oxidations of acetone and isopropanol showed a decrease after the Ag decoration. The results indicated that the photocatalytic oxidation was not limited by the electron transfer to O2. The increased electron transfer to O2 contributed to the recombination around the Ag/TiO2 perimeters, and the photocatalytic activities were decreased.
Defects in liquid crystals: homotopy theory and experimental studies

The fundamental concepts of the homotopy theory of defects in liquid crystals and the results of experimental studies in this field are presented. The concepts of degeneracy space, homotopy groups, and topological charge, which are used for classifying the topologically stable inhomogeneous distributions in different liquid-crystalline phases are examined (uni and biaxial nematics, cholesterics, smectics, and columnar phases). Experimental data are given for the different mesophases on the structure and properties of dislocations, disclinations, point defects in the volume (hedgehogs) and on the surface of the medium (boojums), monopoles, domain formations, and solitons. Special attention is paid to the results of studies of defects in closed volumes (spherical drops, cylindrical capillaries), and to the connection between the topological charges of these defects and the character of the orientation of the molecules of the liquid crystal at the surface. A set of fundamentally new effects that can occur in studying the topological properties of defects in liquid crystals is discussed.
New Stability Analysis for Systems with Interval Time-varying Delay Based on Lyapunov Functional Method

This paper considers the stability of linear systems with interval time-varying delay. We are interested in a new Lyapunov-Krasovskii Functional (LKF) including multiple-integral quadratic terms. Based on this LKF, some new analysis method are used to estimate its derivative, and as a result, some less conservative stability results are obtained in terms of LMIs. In the last, well-known examples are used to illustrate that our results reduce the conservatism of the previous results.
Using natural element mesh-free numerical method in solving shallow water equations

This article attempts to solve shallow water equations based on the natural element method (NEM). The foundation of the NEM method is based on the natural neighbour interpolation, and, in this article, the Sibson interpolation is used. Moreover, the nodal integration is applied for discretising the governing equations. The proposed method is validated by three 2D shallow water test cases, including (1) supercritical flow in converged channel, (2) subcritical flow in sudden expansion and (3) inflow jet into the main channel, and then compared with the experimental data. In addition, in order to evaluate the obtained results, the outcomes of the models are compared with the results of the conventional finite volume method (FVM). The results are evaluated by NRMSE and relative error criteria. In general, results indicate that the proposed NEM method is slightly superior to the FVM.
Smart Scarf: An IOT-based Solution for Emotion Recognition

This paper presents the design rationale of a color-changing scarf depending on the emotional state it detects. The goal of the design is to associate people to show up their emotional state especially those who have difficulties in expressing their feelings, such as elderly people, children, people with special needs, and people who have to wear a face mask, which covers their facial expression, as it is common during the COVID-19 pandemic period. Wearing a smart emotional scarf provides a context of the individual's emotional state that might help surrounding people in dealing with them. Our design uses a heart rate sensor and a skin sensor to detect and recognize emotional information. The scarf will change its color based on the emotional state it detects (neutral, angry, happy, and sad). An interface allows the provision of emotional data and displays emotional statistics and daily history. We went through a user-centered design process adopting wearable technology guidelines. A proposed prototype of the smart scarf is presented called Scarfy. A user evaluation is conducted showing that wearing the scarf with changing colors based on emotional status is a good solution to express feelings with comfort and wearing it might enhance social engagement.
The Effects of 5 km Interval Running on the Anterior Cruciate Ligament Strain and Biomechanical Characteristic of the Knee Joint: Simulation and Principal Component Analysis

Interval running methodologies simulate competition and training conditions, with the aim of enhancing an athletes’ ability to cope with constant deceleration, acceleration, and sudden changes in direction, as associated athletic and performance challenges. Fifteen male athletes were recruited in this study, in which the anterior cruciate ligament was modeled as a nonlinear elastic passive soft tissue in OpenSim 4.2. Participants completed 5 km interval running training on a treadmill. Before and after the interval running, kinematics, kinetics, and electromyography activity of the lower leg during the cutting maneuvers were collected simultaneously. After running training, the anterior cruciate ligament strain demonstrated a decreasing trend when performing unexpected cutting maneuvers. Principal component analysis showed significant differences in knee moments during abduction-adduction; knee angles in flexion-extension, external-internal rotation, and abduction-adduction, as well as knee contact forces in the sagittal and coronal planes. The findings of the study highlight that athletes generate greater adduction moment at the onset of the cut, followed by greater abduction moment towards the end of the cut, which may have a substantial impact on the anterior cruciate ligament loading. Furthermore, athletes need to be mindful of changes in coronal plane contact forces.
An undiagnosed eventration of diaphragm in a pregnant lady–out of the blue problem

We report a rare case of 34-year-old female with left diaphragmatic eventration presenting during adulthood. The primigravida patient came for emergency cesarean section who presented with breathlessness during perioperative period. Patient had no previous history of similar complaints, or any history of trauma. Physical examination showed normal findings in the patient during preanesthesia checkup. With best of our knowledge this is the first in pregnant patient in Maharashtra in whom this problem was identified.
Relationship Between Cleft Lip and Palate Followed by Osteogenesis Imperfecta Accompanied by Tetralogy of Fallot in Children with a Maternal History of Catatonic Schizophrenia with Valproic Acid Therapy and Diabetes Mellitus

Researchers believe that most cases of cleft lip and cleft palate are caused by genetic and environmental factors. Nevertheless, the exact cause was not found. Nevertheless there are allegations that the condition of the cleft lip and palate has something to do with osteogenesis imperfecta with the heterozygous mutation COL1A2 and the formation of triple helix DNA accompanied by Tetralogy of Fallot in children with a maternal history of catatonic schizophrenia followed by the use of valproic acid and type II diabetes mellitus. Then, it is also found in babies with cleft lip and sky conditions usually accompanied by other congenital anomaly, but not always. This then further gives rise to speculation that there are links between the above variables and the cleft lip and palate. Therefore. Knowing the Relationship Between Cleft Lip and Palate Events with osteogenesis imperfecta with heterozygous mutation COL1A2 and triple helix DNA formation accompanied by Tetralogy of Fallot in children with a maternal history of catatonic schizophrenia followed by the use of valproic acid and type II diabetes mellitus. This research is a Systematic Review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses method or commonly called PRISMA, this method is carried out systematically by following the correct research stages or protocols. The source was taken from the Google Scholar site with journals published in 2017-2023 and then screened the results of 1,870 journals. Journal clustering was carried out and the number of Q1 indexed journals was obtained in 4 journals, Q3 in 1 journal, and Q4 in 1 journal so that there were 6 journals extracted. The entire journal does not directly discuss the relationship between the research variables, but only provides an overview of the increased risk of celestial cleft conditions in babies if the expectant mother has type 2 diabetes mellitus conditions, osteogenesis imperfect, and the relationship of the two health conditions to the possibility of birth of the baby with the Tetralogy of Fallot condition, as well as the relationship of the baby to the condition of the cleft lip and palate and tetralogy of fallot
BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, based on either crowdsourcing or text mining, are often limited to a small predefined set of relations due to manual cost or restrictions in text corpus. Recent research proposed to use pretrained language models (LMs) as implicit knowledge bases that accept knowledge queries with prompts. Yet, the implicit knowledge lacks many desirable properties of a full-scale symbolic KG, such as easy access, navigation, editing, and quality assurance. In this paper, we propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition (a prompt and a few shot of example entity pairs), the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge of the desired relation. We develop an effective search-and-rescore mechanism for improved efficiency and accuracy. We deploy the approach to harvest KGs of over 400 new relations from different LMs. Extensive human and automatic evaluations show our approach manages to extract diverse accurate knowledge, including tuples of complex relations (e.g.,"A is capable of but not good at B"). The resulting KGs as a symbolic interpretation of the source LMs also reveal new insights into the LMs' knowledge capacities.
Efficient Quantum State Synthesis with One Query

We present a polynomial-time quantum algorithm making a single query (in superposition) to a classical oracle, such that for every state $|\psi\rangle$ there exists a choice of oracle that makes the algorithm construct an exponentially close approximation of $|\psi\rangle$. Previous algorithms for this problem either used a linear number of queries and polynomial time, or a constant number of queries and polynomially many ancillae but no nontrivial bound on the runtime. As corollaries we do the following: - We simplify the proof that statePSPACE $\subseteq$ stateQIP (a quantum state analogue of PSPACE $\subseteq$ IP) and show that a constant number of rounds of interaction suffices. - We show that QAC$\mathsf{_f^0}$ lower bounds for constructing explicit states would imply breakthrough circuit lower bounds for computing explicit boolean functions. - We prove that every $n$-qubit state can be constructed to within 0.01 error by an $O(2^n/n)$-size circuit over an appropriate finite gate set. More generally we give a size-error tradeoff which, by a counting argument, is optimal for any finite gate set.
Full deautonomisation by singularity confinement as an integrability test

Since its introduction, the method of full deautonomisation by singularity confinement has proved a strikingly effective way of detecting the dynamical degrees of birational mappings of the plane. This method is based on an observed link between two a priori unrelated notions: firstly the dynamical degree of the mapping and secondly the evolution of parameters required for its singularity structure to remain unchanged under a sufficiently general deautonomisation. We give a proof of this conjectured correspondence for a large class of birational mappings of the plane via the spaces of initial conditions for their deautonomised versions. We show that even for non-integrable mappings in this class, the surfaces forming these spaces have effective anticanonical divisors and one can define a kind of period map similar to that in the theory of rational surfaces associated with discrete Painlev\'e equations. This provides a bridge between the evolution of coefficients in the deautonomised mapping and the induced dynamics on the Picard lattice which encode the dynamical degree.
Hierarchical Attention Encoder Decoder

Recent advances in large language models have shown that autoregressive modeling can generate complex and novel sequences that have many real-world applications. However, these models must generate outputs autoregressively, which becomes time-consuming when dealing with long sequences. Hierarchical autoregressive approaches that compress data have been proposed as a solution, but these methods still generate outputs at the original data frequency, resulting in slow and memory-intensive models. In this paper, we propose a model based on the Hierarchical Recurrent Encoder Decoder (HRED) architecture. This model independently encodes input sub-sequences without global context, processes these sequences using a lower-frequency model, and decodes outputs at the original data frequency. By interpreting the encoder as an implicitly defined embedding matrix and using sampled softmax estimation, we develop a training algorithm that can train the entire model without a high-frequency decoder, which is the most memory and compute-intensive part of hierarchical approaches. In a final, brief phase, we train the decoder to generate data at the original granularity. Our algorithm significantly reduces memory requirements for training autoregressive models and it also improves the total training wall-clock time.
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model

Counterfactual inference aims to answer retrospective"what if"questions and thus belongs to the most fine-grained type of inference in Pearl's causality ladder. Existing methods for counterfactual inference with continuous outcomes aim at point identification and thus make strong and unnatural assumptions about the underlying structural causal model. In this paper, we relax these assumptions and aim at partial counterfactual identification of continuous outcomes, i.e., when the counterfactual query resides in an ignorance interval with informative bounds. We prove that, in general, the ignorance interval of the counterfactual queries has non-informative bounds, already when functions of structural causal models are continuously differentiable. As a remedy, we propose a novel sensitivity model called Curvature Sensitivity Model. This allows us to obtain informative bounds by bounding the curvature of level sets of the functions. We further show that existing point counterfactual identification methods are special cases of our Curvature Sensitivity Model when the bound of the curvature is set to zero. We then propose an implementation of our Curvature Sensitivity Model in the form of a novel deep generative model, which we call Augmented Pseudo-Invertible Decoder. Our implementation employs (i) residual normalizing flows with (ii) variational augmentations. We empirically demonstrate the effectiveness of our Augmented Pseudo-Invertible Decoder. To the best of our knowledge, ours is the first partial identification model for Markovian structural causal models with continuous outcomes.
Video Colorization with Pre-trained Text-to-Image Diffusion Models

Video colorization is a challenging task that involves inferring plausible and temporally consistent colors for grayscale frames. In this paper, we present ColorDiffuser, an adaptation of a pre-trained text-to-image latent diffusion model for video colorization. With the proposed adapter-based approach, we repropose the pre-trained text-to-image model to accept input grayscale video frames, with the optional text description, for video colorization. To enhance the temporal coherence and maintain the vividness of colorization across frames, we propose two novel techniques: the Color Propagation Attention and Alternated Sampling Strategy. Color Propagation Attention enables the model to refine its colorization decision based on a reference latent frame, while Alternated Sampling Strategy captures spatiotemporal dependencies by using the next and previous adjacent latent frames alternatively as reference during the generative diffusion sampling steps. This encourages bidirectional color information propagation between adjacent video frames, leading to improved color consistency across frames. We conduct extensive experiments on benchmark datasets, and the results demonstrate the effectiveness of our proposed framework. The evaluations show that ColorDiffuser achieves state-of-the-art performance in video colorization, surpassing existing methods in terms of color fidelity, temporal consistency, and visual quality.
Packet Reception Probability: Packets That You Can't Decode Can Help Keep You Safe

This paper provides a robust, scalable Bluetooth Low-Energy (BLE) based indoor localization solution using commodity hardware. While WiFi-based indoor localization has been widely studied, BLE has emerged a key technology for contact-tracing in the current pandemic. To accurately estimate distance using BLE on commercial devices, systems today rely on Receiver Signal Strength Indicator(RSSI) which suffers from sampling bias and multipath effects. We propose a new metric: Packet Reception Probability (PRP) that builds on a counter-intuitive idea that we can exploit packet loss to estimate distance. We localize using a Bayesian-PRP formulation that also incorporates an explicit model of the multipath. To make deployment easy, we do not require any hardware, firmware, or driver-level changes to off-the-shelf devices, and require minimal training. PRP can achieve meter level accuracy with just 6 devices with known locations and 12 training locations. We show that fusing PRP with RSSI is beneficial at short distances<2m. Beyond 2m, fusion is worse than PRP, as RSSI becomes effectively de-correlated with distance. Robust location accuracy at all distances and ease of deployment with PRP can help enable wide range indoor localization solutions using BLE.
Adaptive Message Quantization and Parallelization for Distributed Full-graph GNN Training

Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is bandwidth-demanding and time-consuming. Frequent exchanges of node features, embeddings and embedding gradients (all referred to as messages) across devices bring significant communication overhead for nodes with remote neighbors on other devices (marginal nodes) and unnecessary waiting time for nodes without remote neighbors (central nodes) in the training graph. This paper proposes an efficient GNN training system, AdaQP, to expedite distributed full-graph GNN training. We stochastically quantize messages transferred across devices to lower-precision integers for communication traffic reduction and advocate communication-computation parallelization between marginal nodes and central nodes. We provide theoretical analysis to prove fast training convergence (at the rate of O(T^{-1}) with T being the total number of training epochs) and design an adaptive quantization bit-width assignment scheme for each message based on the analysis, targeting a good trade-off between training convergence and efficiency. Extensive experiments on mainstream graph datasets show that AdaQP substantially improves distributed full-graph training's throughput (up to 3.01 X) with negligible accuracy drop (at most 0.30%) or even accuracy improvement (up to 0.19%) in most cases, showing significant advantages over the state-of-the-art works.
Reducing Popularity Bias in Recommender Systems through AUC-Optimal Negative Sampling

Popularity bias is a persistent issue associated with recommendation systems, posing challenges to both fairness and efficiency. Existing literature widely acknowledges that reducing popularity bias often requires sacrificing recommendation accuracy. In this paper, we challenge this commonly held belief. Our analysis under general bias-variance decomposition framework shows that reducing bias can actually lead to improved model performance under certain conditions. To achieve this win-win situation, we propose to intervene in model training through negative sampling thereby modifying model predictions. Specifically, we provide an optimal negative sampling rule that maximizes partial AUC to preserve the accuracy of any given model, while correcting sample information and prior information to reduce popularity bias in a flexible and principled way. Our experimental results on real-world datasets demonstrate the superiority of our approach in improving recommendation performance and reducing popularity bias.
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?

Recent research on dialog state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. However, performance gains heavily depend on aggressive data augmentation and fine-tuning of ever larger language model based architectures. In contrast, general purpose language models, trained on large amounts of diverse data, hold the promise of solving any kind of task without task-specific training. We present preliminary experimental results on the ChatGPT research preview, showing that ChatGPT achieves state-of-the-art performance in zero-shot DST. Despite our findings, we argue that properties inherent to general purpose models limit their ability to replace specialized systems. We further theorize that the in-context learning capabilities of such models will likely become powerful tools to support the development of dedicated dialog state trackers and enable dynamic methods.