Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition

In 3D shape recognition, multi-view based methods leverage human's perspective to analyze 3D shapes and have achieved significant outcomes. Most existing research works in deep learning adopt handcrafted networks as backbones due to their high capacity of feature extraction, and also benefit from ImageNet pretraining. However, whether these network architectures are suitable for 3D analysis or not remains unclear. In this paper, we propose a neural architecture search method named Auto-MVCNN which is particularly designed for optimizing architecture in multi-view 3D shape recognition. Auto-MVCNN extends gradient-based frameworks to process multi-view images, by automatically searching the fusion cell to explore intrinsic correlation among view features. Moreover, we develop an end-to-end scheme to enhance retrieval performance through the trade-off parameter search. Extensive experimental results show that the searched architectures significantly outperform manually designed counterparts in various aspects, and our method achieves state-of-the-art performance at the same time.
PARRoT: Predictive Ad-hoc Routing Fueled by Reinforcement Learning and Trajectory Knowledge

Swarms of collaborating Unmanned Aerial Vehicles (UAVs) that utilize ad-hoc networking technologies for coordinating their actions offer the potential to catalyze emerging research fields such as autonomous exploration of disaster areas, demand-driven network provisioning, and near field packet delivery in Intelligent Transportation Systems (ITSs). As these mobile robotic networks are characterized by high grades of relative mobility, existing routing protocols often fail to adopt their decision making to the implied network topology dynamics. For addressing these challenges, we present Predictive Ad-hoc Routing fueled by Reinforcement learning and Trajectory knowledge (PARRoT) as a novel machine learning-enabled routing protocol which exploits mobility control information for integrating knowledge about the future motion of the mobile agents into the routing process. The performance of the proposed routing approach is evaluated using comprehensive network simulation. In comparison to established routing protocols, PARRoT achieves a massively higher robustness and a significantly lower end-to-end latency.
Empirical Analysis of Unlabeled Entity Problem in Named Entity Recognition

In many scenarios, named entity recognition (NER) models severely suffer from unlabeled entity problem, where the entities of a sentence may not be fully annotated. Through empirical studies performed on synthetic datasets, we find two causes of the performance degradation. One is the reduction of annotated entities and the other is treating unlabeled entities as negative instances. The first cause has less impact than the second one and can be mitigated by adopting pretraining language models. The second cause seriously misguides a model in training and greatly affects its performances. Based on the above observations, we propose a general approach that is capable of eliminating the misguidance brought by unlabeled entities. The core idea is using negative sampling to keep the probability of training with unlabeled entities at a very low level. Experiments on synthetic datasets and real-world datasets show that our model is robust to unlabeled entity problem and surpasses prior baselines. On well-annotated datasets, our model is competitive with state-of-the-art method.
Spectral analysis of some random matrices based schemes for stable and robust nonparametric and functional regression estimators

In the first part of this work, we develop and study a random pseudo-inverse based scheme for a stable and robust solution of nonparametric regression problems. For the interval $I=[-1,1],$ we use a random projection over a special orthonormal family of a weighted $L^2(I)-$space, given by the Jacobi normalized polynomials. Then, the pseudo-inverse of this random matrix is used to compute the different expansion coefficients of the nonparametric regression estimator with respect to this orthonormal system. We show that this estimator is stable. Then, we combine the RANdom SAmpling Consensus (RANSAC) iterative algorithm with the previous scheme to get a robust and stable nonparametric regression estimator. This estimator has also the advantage to provide fairly accurate approximations to the true regression functions. In the second part of this work, we extend the random pseudo-inverse scheme technique to build a stable and accurate estimator for solving linear functional regression (LFR) problems. A dyadic decomposition approach is used to construct this last stable estimator for the LFR problem. The performance of the two proposed estimators are illustrated by various numerical simulations.
A 4-Month Whole-Systems Ayurvedic Medicine Nutrition and Lifestyle Intervention Is Feasible and Acceptable for Breast Cancer Survivors: Results of a Single-Arm Pilot Clinical Trial

Purpose Ongoing symptoms and impairments in quality of life (QOL) among breast cancer survivors remain a significant problem. We tested the feasibility and acceptability of a manualized Ayurvedic nutrition and lifestyle intervention for breast cancer survivors. Methods Eligible participants had Stage I–III breast cancer, underwent treatment within the past year that included chemotherapy, and were without active disease. The 4-month individualized Ayurvedic intervention included counseling on nutrition, lifestyle, yoga, and marma (like acupressure) during 8 one-on-one visits with an Ayurvedic practitioner. Feasibility and acceptability were the primary outcomes. QOL (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire [EORTC QLQ C30]) and symptoms—sleep disturbance (General Sleep Disturbance Scale [GSDS]), fatigue (Lee Fatigue Scale [LFS]), depressive symptoms (Center for Epidemiological Studies—Depression Scale [CES-D]), anxiety (Spielberger State-Trait Anxiety Inventory [STAI-S, STAI-T]), and stress (Perceived Stress Scale [PSS])—were measured prior to, at midpoint, and at the end of the 4-month intervention. Effect sizes (Cohen’s d) were calculated along with paired t tests comparing baseline to end of month 4 time points. Mixed effects models were used for repeated measures analyses. Results Participants (n = 32) had a mean age of 48 years (SD = 10). Retention at the end of the intervention was 84%. Among those who completed the intervention (n = 27), adherence was high (99.5% of visits with practitioners attended). Large improvements were seen in QLQ-C30 emotional functioning (d = 0.84, P < 0.001), QLQ-C30 cognitive functioning (d = 0.86, P < 0.001), GSDS (d = –1.23, P < 0.001), and CES-D (d = –1.21, P < 0.001). Moderate improvements were seen in QLQ-C30 global health (d = 0.65, p = 0.003), LFS (d = –0.68, P = 0.002), and PSS (d = –0.75, P < 0.001). No adverse events were observed due to the intervention. Conclusion This 4-month Ayurvedic whole-systems multimodal nutrition and lifestyle intervention was feasible and acceptable for breast cancer survivors. Promise of clinical benefit was seen in terms of improvements in symptoms and QOL that warrants further investigation.
Generative Deep Learning Techniques for Password Generation

Password guessing approaches via deep learning have recently been investigated with significant breakthroughs in their ability to generate novel, realistic password candidates. In the present work we study a broad collection of deep learning and probabilistic based models in the light of password guessing: attention-based deep neural networks, autoencoding mechanisms and generative adversarial networks. We provide novel generative deep-learning models in terms of variational autoencoders exhibiting state-of-art sampling performance, yielding additional latent-space features such as interpolations and targeted sampling. Lastly, we perform a thorough empirical analysis in a unified controlled framework over well-known datasets (RockYou, LinkedIn, Youku, Zomato, Pwnd). Our results not only identify the most promising schemes driven by deep neural networks, but also illustrate the strengths of each approach in terms of generation variability and sample uniqueness.
Formation of rutile nuclei at anatase (112) twin interfaces and the phase transformation mechanism in nanocrystalline titania

In nanocrystalline anatase coarsened under hydrothermal conditions (250 C, P{sub sat}), the anatase-to-rutile phase transformation is nucleated at anatase {l_brace}112{r_brace} twin boundaries formed by oriented attachment. The anatase twin boundary is constructed from structural elements common to rutile. Specifically, rutile nucleation involves displacement of only one half the titanium cations within the twin slab. Subsequent transformation of bulk anatase involves rupture of 7 of the 24 Ti-O bonds per unit cell and cooperative displacement of Ti and O. As the transformation advances into the bulk material, adjacent slabs of anatase octahedra are destabilized, resulting in rapid progression of the transformation of bulk anatase to rutile. The implied chain reaction, scarcity of partly reacted crystals, absence of multiple twinned rutile, and the importance of nucleation at anatase twins indicate a rate law based on slow nucleation and rapid growth. The displacements are comparable to those proposed previously for macroscopic anatase at much higher temperatures, indicating the atomic mechanism is not modified by particle size or temperature, despite the rapid kinetics in finely crystalline aggregates. In addition to the formation of twins, clusters with rutile-like character may occur at some fraction of random anatase-anatase particle contacts. Such interfaces should result in decreasedmore » activation barriers for rutile nucleation and, thus, contribute significantly to the observed faster transformation rates in nanocrystalline compared to coarsely crystalline materials. « less
Extracellular vesicles derived from human dental pulp stem cells promote osteogenesis of adipose-derived stem cells via the MAPK pathway

Recent studies have shown that co-culture systems play an important role in bone tissue engineering. In this study, human dental pulp stem cells (hDPSCs) were co-cultured with human adipose-derived stem cells (hADSCs), and osteoblastic phenotypes were found to be enhanced in co-cultures compared with monocultures of hDPSCs or hADSCs. Furthermore, GW4869, an inhibitor of extracellular vesicle (EV) formation, suppressed the mineralization of co-cultured cells. Studies indicate that the therapeutic potential of DPSCs is realized through paracrine action, in which EVs play an important role. To study their role, we successfully obtained and identified hDPSC-derived extracellular vesicles (hDPSC-EVs), and further investigated their effects on hADSCs and the underlying mechanism. hADSCs were stimulated with hDPSC-EVs, which were found to promote the migration and mineralization of hADSCs. Moreover, hDPSC-EVs promoted osteogenic differentiation by enhancing the phosphorylation of ERK 1/2 and JNK in hADSCs. To investigate the specific proteins in EVs that might play a role in hADSC osteogenic differentiation, we performed proteomic analysis of hDPSC-EVs. We determined the top 30 enriched pathways, which notably included the insulin signaling pathway. The number of genes enriched in the insulin signaling pathway was the largest, in addition to the “protein processing in endoplasmic reticulum” term. The MAPK cascade is a typical downstream pathway mediating insulin signaling. To further study the effects of hDPSC-EVs on maxillofacial bone regeneration, we used hDPSC-EVs as a cell-free biomaterial in a model of mandibular defects in rats. To assess the therapeutic potential of EVs, we analyzed their proteome. Animal experiments demonstrated that hDPSC-EVs promoted the regeneration of bone defects. Overall, these results highlight the potential of hDPSC-EVs to induce lineage specific differentiation of hADSCs. The results also indicated the importance of considering hDPSC-EVs as biomimetic materials for clinical translation of treatments for oral maxillofacial defects.
Effectiveness of specific stabilization exercise compared with traditional trunk exercise in women with non-specific low back pain: a pilot randomized controlled trial

Background Non-specific low back pain (LBP) is the leading cause of disability worldwide. The primary physiotherapeutic treatment for LBP is physical exercise, but evidence suggesting a specific exercise as most appropriate for any given case is limited. Objective To determine if specific stabilization exercise (SSE) is more effective than traditional trunk exercise (TTE) in reducing levels of pain, disability and inflammation in women with non-specific low back pain (LBP). Design A pilot randomized controlled trial was conducted in Rovira i Virgili University, Catalonia. Methods Thirty-nine females experiencing non-specific LBP were included in two groups: the TTE program and SSE program, both were conducted by a physiotherapist during twenty sessions. The primary outcome was pain intensity (10-cm Visual Analogue Scale). Secondary outcomes were disability (Roland Morris Disability Questionnaire), and inflammation (IL-6 and TNF-α plasma levels). Measurements were taken at baseline, at half intervention, at post-intervention, and a month later. Results Mean group differences in change from baseline to post-intervention for TTE were: −4.5 points (CI 3.3 to 5.6) for pain, −5.1 points (CI 3.0 to 7.3) for disability, 0.19 pg/mL (95% CI [−1.6–1.2]) for IL-6 levels, and 46.2 pg/mL (CI 13.0 to 85.3) for TNF-α levels. For SSE, differences were: −4.3 points (CI 3.1 to 5.6) for pain, −6.1 points (CI 3.7 to 8.6) for disability, 1.1 pg/mL (CI 0.0 to 2.1) for IL-6 levels , and 12.8 pg/mL (95% CI [−42.3–16.7]) for TNF-α levels. There were an insignificant effect size and no statistically significant overall mean differences between both groups. Conclusion This study suggests that both interventions (traditional trunk and specific stabilization exercises) are effective in reducing pain and disability in non-specific LBP patients, but the two programs produce different degrees of inflammation change. Clinical trial registration number NCT02103036.
Clinico-Biochemical Correlation in Birth Asphyxia and Its Effects on Outcome

Background Birth asphyxia is a major cause of early neonatal death and leads to severe consequences such as epilepsy, cerebral palsy, and developmental delay. This study aims to determine the correlation between dyselectrolytemia and the degree of hypoxic-ischemic encephalopathy (HIE) and to find out major risk factors contributing to the severity of HIE and neonatal death. Methods In this prospective cohort study (n=150), term babies weighing ≥ 2.5 kg at birth, with the diagnosis of birth asphyxia, admitted in a medical college in Odisha state from September 2014 to August 2016 were included. Clinical findings, biochemical parameters, treatment, and outcome of HIE babies were recorded. Result The majority of the asphyxiated babies were having moderate HIE (HIE II) (57.33%), whereas mild and severe stages were seen in 15.33%, and 27.34% of babies, respectively. Factors like prolonged labor (87.8%) and meconium-stained liquor (63.4%) were mostly attributed to the severe degree of birth asphyxia (p < 0.001). Apnea, lethargy, and hypothermia were the most remarkable feature of HIE III. The degree of hyponatremia, hypocalcemia, and hyperkalemia (124.4±4.4 mmol/l, 0.83±0.08 mmol/l, and 6.17± 0.89 mmol/l, respectively) were more severely affected in HIE III as compared to HIE l (137.5±3.8 mmol/l, 1.06±0.17 mmol/l, and 5.0±0.79 mmol/l, respectively). Serum urea and creatinine increased proportionately with an increase in the severity of HIE grade. The mildly asphyxiated neonates recovered completely, whereas all the cases who died (n=29,19.3%) belonged to the moderate or severe degree of birth asphyxia. Conclusion The asphyxiated neonates had hyponatremia, hypocalcemia, hyperkalemia, raised serum urea, and creatinine and correlated with the severity of birth asphyxia. Prolonged labor and meconium-stained liquor were the most attributable factor for the severe degree of birth asphyxia. Effective neonatal resuscitation and quick correction of electrolyte imbalances will help in the reduction of neonatal mortality and long-term neurological sequelae.
What Makes a Humanitarian Crisis Severe and Can Severity be Quantified? Assessment of the Global Severity Crisis Index

Background: Humanitarian response actors evaluate crisis severity with the INFORM Severity Index, a publicly available metric. This index, however, has not undergone critical statistical review. If imprecise or incorrect, humanitarian response may be negatively impacted. Methods: The INFROM Severity Index is calculated from 35 publicly available indicators, which conceptually reflect the severity of each crisis We used 172 unique global crises from the INFORM SEVERITY Index database that occurred January 1 to November 30, 2019, or were ongoing by this date. We applied exploratory factor analysis (EFA) to determine common factors within the dataset. We then applied a second-order confirmatory factor analysis (CFA) to predict crisis severity as a latent construct. Model fit was assessed via chi-square goodness-of-fit statistic, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA). Results: The EFA models suggested a 3- or 4- factor solution, with 46% and 53% variance explained in each model, respectively. The final CFA was parsimonious, containing three factors comprised of 11 indicators, with reasonable model fit (Chi-squared=107, with 40 degrees of freedom, CFI=0.94, TLI=0.92, RMSEA=0.10). In the second-order CFA, the magnitude of standardized factor-loading on the societal governance latent construct had the strongest association with the latent construct of crisis severity (0.73), followed by the humanitarian access/safety construct (0.56). Conclusions: A metric of crisis-severity is a critical step towards improving humanitarian response, but only when it reflects real-life conditions. Our work is a first step in refining an existing framework to quantify crisis severity.
Keggin-type polyoxometalate-based supramolecular complex for selective oxidation of styrene to benzaldehyde

Abstract A Keggin-type polyoxometalate (POM)-based supramolecular complex with the formula of [Co(H2O)3]2(ttb)2[HBW12O40]·2H2O (1) (ttb = 1,3,5-tri(1,2,4-triazol-1-ylmethyl)-2,4,6-trimethylbenzene) was synthesized via hydrothermal method and characterized by single-crystal X-ray diffraction, IR and powder X-ray diffraction analyses. In 1, [Co(H2O)3]2+ cations are connected with the adjacent ttb ligands to form a porous two-dimensional (2-D) cationic layered network, and the [HBW12O40]4− polyoxoanions are encapsulated between the layers through intermolecular interactions, generating a POM-capsulated 3-D supramolecular network. Integrating the advantage of Co2+ with [HBW12O40]4−, 1 as a heterogeneous catalyst displays a synergistically improved catalytic performance for oxidation of styrene. It can achieve almost 98% conversion of styrene in 4 h with about 99% selectivity of benzaldehyde product, which is about five times more than the catalytic activities of the single components (Co(OAc)2·4H2O or K5BW12O40). A series of comparative analyses showed that the high catalytic performance of 1 is mainly attributed to the synergistic effect of unsaturated metal sites and polyoxoanions. This work provides an approach for developing highly efficient POM-based heterogeneous catalysts toward the selective oxidation of styrene. Graphical Abstract
Artificial Intelligence-Assisted Surgery: Potential and Challenges

Background: Artificial intelligence (AI) has recently achieved considerable success in different domains including medical applications. Although current advances are expected to impact surgery, up until now AI has not been able to leverage its full potential due to several challenges that are specific to that field. Summary: This review summarizes data-driven methods and technologies needed as a prerequisite for different AI-based assistance functions in the operating room. Potential effects of AI usage in surgery will be highlighted, concluding with ongoing challenges to enabling AI for surgery. Key Messages: AI-assisted surgery will enable data-driven decision-making via decision support systems and cognitive robotic assistance. The use of AI for workflow analysis will help provide appropriate assistance in the right context. The requirements for such assistance must be defined by surgeons in close cooperation with computer scientists and engineers. Once the existing challenges will have been solved, AI assistance has the potential to improve patient care by supporting the surgeon without replacing him or her.
Feedback Control of Brownian Motion for Single-Particle Fluorescence Spectroscopy

The stochastic Brownian motion of individual particles in solution constrains the utility of single-particle fluorescence microscopy both by limiting the dwell time of particles in the observation volume and by convolving their internal degrees of freedom with their random spatial trajectories. This thesis describes the use of active feedback control to eliminate these undesirable effects. We designed and implemented a feedback tracking system capable of locking the position of a fluorescent particle to the optic axis of our microscope, i.e., capable of tracking the two-dimensional, planar Brownian motion of a free particle in solution. A full theoretical description of the experiment is given in the language of linear stochastic control theory. The model describes both the statistics of the tracking system and provides a generalization of the theory of open-loop Fluorescence Correlation Spectroscopy (FCS) that accounts for fluctuations in fluorescence arising from competition between diffusion and damping. We find excellent agreement between theory and experiment. Using fluorescent polymer microspheres as test particles, we find that the observation time for these particles can be increased by 2-3 orders of magnitude over the open-loop scenario. The system achieves nearly optimal performance for moderately fast-moving particles at very low fluorescent count rates, comparable to those of a single fluorescent protein molecule. The system can classify particles in a binary mixture based on a real-time estimate of their diffusion coefficients (differing by a factor of ~4), achieving 90% success using fewer than 600 photons detected over 120 ms. Future directions for both the experimental and theoretical techniques are briefly discussed.