Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Labeling of Human Action For Visually Impaired And Blind People Scene Interaction

The aim of this work is to contribute to the development of a tactile device for visually impaired and blind persons in order to let them to understand actions of the surrounding people and to interact with them. First, based on the state-of-the-art methods of human action recognition from RGB-D sequences, we use the skeleton information provided by Kinect, with the disentangled and unified multi-scale Graph Convolutional (MS-G3D) model to recognize the performed actions. We tested this model on real scenes and found some of constraints and limitations. Next, we apply a fusion between skeleton modality with MS-G3D and depth modality with CNN in order to bypass the discussed limitations. Third, the recognized actions are labeled semantically and will be mapped into an output device perceivable by the touch sense.
Compressing Word Embeddings Using Syllables

This work examines the possibility of using syllable embeddings, instead of the often used $n$-gram embeddings, as subword embeddings. We investigate this for two languages: English and Dutch. To this end, we also translated two standard English word embedding evaluation datasets, WordSim353 and SemEval-2017, to Dutch. Furthermore, we provide the research community with data sets of syllabic decompositions for both languages. We compare our approach to full word and $n$-gram embeddings. Compared to full word embeddings, we obtain English models that are 20 to 30 times smaller while retaining 80% of the performance. For Dutch, models are 15 times smaller for 70% performance retention. Although less accurate than the $n$-gram baseline we used, our models can be trained in a matter of minutes, as opposed to hours for the $n$-gram approach. We identify a path toward upgrading performance in future work. All code is made publicly available, as well as our collected English and Dutch syllabic decompositions and Dutch evaluation set translations.
Lepton Number vs Coulomb Super-Criticality

The non-perturbative effects of QED-vacuum reconstruction under influence of a supercritical EM-source (Z > Zcr,1) are considered in view of lepton number conservation. The question is here that if the vacuum shells formation and spontaneous positron emission, associated with discrete levels diving into the lower continuum, exist in reality, then it by no means should be a signal of new physics. The reason is that the emitted positrons carry away the lepton number equal to (−1)×their total number, and so the corresponding amount of positive lepton number should be shared by VP-density, concentrated in vacuum shells. In this case instead of integer lepton number of real particles there should appear the lepton number VP-density. Otherwise either the lepton number conservation in such processes must be broken, or the spontaneous positron emission prohibited. The conditions, under which the emission of vacuum positrons can be unambiguously detected on the nuclear conversion pairs background, are also discussed.
CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation

Recently, there is growing attention on one-stage panoptic segmentation methods which aim to segment instances and stuff jointly within a fully convolutional pipeline efficiently. However, most of the existing works directly feed the backbone features to various segmentation heads ignoring the demands for semantic and instance segmentation are different: The former needs semantic-level discriminative features, while the latter requires features to be distinguishable across instances. To alleviate this, we propose to first predict semantic-level and instance-level correlations among different locations that are utilized to enhance the backbone features, and then feed the improved discriminative features into the corresponding segmentation heads, respectively. Specifically, we organize the correlations between a given location and all locations as a continuous sequence and predict it as a whole. Considering that such a sequence can be extremely complicated, we adopt Discrete Fourier Transform (DFT), a tool that can approximate an arbitrary sequence parameterized by amplitudes and phrases. For different tasks, we generate these parameters from the backbone features in a fully convolutional way which is optimized implicitly by corresponding tasks. As a result, these accurate and consistent correlations contribute to producing plausible discriminative features which meet the requirements of the complicated panoptic segmentation task. To verify the effectiveness of our methods, we conduct experiments on several challenging panoptic segmentation datasets and achieve state-of-the-art performance on MS COCO with $45.1$\% PQ and ADE20k with $32.6$\% PQ.
FRAMEx. II. Simultaneous X-Ray and Radio Variability in Active Galactic Nuclei—The Case of NGC 2992

Using simultaneous Very Long Baseline Array and Neil Gehrels Swift Observatory X-ray Telescope observations of the active galactic nucleus (AGN) in NGC 2992 over a six-month observing campaign, we observed a large drop in core 5 cm radio luminosity, by a factor of >3, in tandem with a factor of >5 increase in 2–10 keV X-ray luminosity. While NGC 2992 has long been an important object for studies of X-ray variability, our study is the first simultaneous X-ray and radio variability campaign for this object. We observe that the X-ray spectral index does not change over the course of the flare, consistent with a change in the bulk amount of Comptonizing plasma, potentially due to a magnetic reconnection event in the accretion disk. The drop in apparent radio luminosity can be explained by a change in free–free absorption, which we calculate to correspond to an ionized region with a physical extent and electron density consistent with the broad-line region (BLR). Our results are consistent with magnetic reconnection events in the dynamic accretion disk creating outbursts of ionizing material, increasing Compton up-scattering of UV accretion disk photons and feeding material into the BLR. These findings present an important physical picture for the dynamical relationship between X-ray and radio emission in AGNs.
$p$-group Galois covers of curves in characteristic $p$

We study cohomologies of a curve with an action of a finite $p$-group over a field of characteristic $p$. Assuming the existence of a certain 'magical element' in the function field of the curve, we compute the equivariant structure of the module of holomorphic differentials and the de Rham cohomology, up to certain local terms. We show that a generic $p$-group cover has a 'magical element'. As an application we compute the de Rham cohomology of a curve with an action of a finite cyclic group of prime order.
Towards a dark sector model from string theory

An embedding of a unified dark sector model into string theory with the following features is proposed: the model-independent axion descending from the Kalb-Ramond 2-form field is identified with the dark-matter field, and the real part of a Kähler modulus field — the “radius” of one of the extra spatial dimensions — accounts for dark energy. The expectation value of the dilaton field is stabilized by a gaugino condensation mechanism. A dark-energy potential with an amplitude corresponding to a realistic low energy scale results from some gentle tuning of the stabilized expectation value of the dilaton. The resulting potential reproduces the one in a previous dark-sector model proposed by two of us. Challenges to obtain a sufficiently flat potential are discussed.
Finding (s, d)-Hypernetworks in F-Hypergraphs is NP-Hard

We consider the problem of computing an $(s,d)$-hypernetwork in an acyclic F-hypergraph. This is a fundamental computational problem arising in directed hypergraphs, and is a foundational step in tackling problems of reachability and redundancy. This problem was previously explored in the context of general directed hypergraphs (containing cycles), where it is NP-hard, and acyclic B-hypergraphs, where a linear time algorithm can be achieved. In a surprising contrast, we find that for acyclic F-hypergraphs the problem is NP-hard, which also implies the problem is hard in BF-hypergraphs. This is a striking complexity boundary given that F-hypergraphs and B-hypergraphs would at first seem to be symmetrical to one another. We provide the proof of complexity and explain why there is a fundamental asymmetry between the two classes of directed hypergraphs.
Document-Level Relation Extraction With Context Guided Mention Integration and Inter-Pair Reasoning

Document-level Relation Extraction (DRE) aims to recognize the relations between two entities. The entity may correspond to multiple mentions that span beyond the sentence boundary. Few previous studies have investigated the mention integration, which may be problematic because coreferential mentions do not equally contribute to a specific relation. Moreover, prior efforts mainly focus on reasoning at entity-level rather than capturing the global interactions between the entity pairs. In this article, we propose two novel techniques, Context Guided Mention Integration and Inter-pair Reasoning (CGM2IR), to improve DRE. Instead of simply applying average pooling, the contexts are utilized to guide the integration of coreferential mentions in a weighted sum manner. Additionally, inter-pair reasoning executes an iterative algorithm on the entity pair graph, so as to model the interdependency of relations. We evaluate our CGM2IR model on three widely used benchmark datasets, namely DocRED, CDR, and GDA. Experimental results show that our model outperforms previous strong baselines and achieves remarkable effectiveness.
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents

At the heart of improving conversational AI is the open problem of how to evaluate conversations. Issues with automatic metrics are well known (Liu et al., 2016), with human evaluations still considered the gold standard. Unfortunately, how to perform human evaluations is also an open problem: differing data collection methods have varying levels of human agreement and statistical sensitivity, resulting in differing amounts of human annotation hours and labor costs. In this work we compare five different crowdworker-based human evaluation methods and find that different methods are best depending on the types of models compared, with no clear winner across the board. While this highlights the open problems in the area, our analysis leads to advice of when to use which one, and possible future directions.
Hand-Object Interaction Reasoning

This paper proposes an interaction reasoning network for modelling spatio-temporal relationships between hands and objects in egocentric video. The proposed interaction unit utilises a Transformer-style module to reason about each acting hand, and its spatio-temporal relations to the other hand as well as objects being interacted with. We show that modelling two-handed interactions are critical for action recognition in egocentric video, and demonstrate that by using positionally-encoded trajectories, the network can better recognise observed interactions. We train and evaluate our proposed network on large-scale egocentric EPIC-KITCHENS-100 and crowd-sourced Something-Else datasets, with an ablation study to showcase our proposal.
Diabetes duration and the risk of dementia: a cohort study based on German health claims data

Abstract Objective Diabetes is a risk factor for dementia but little is known about the impact of diabetes duration on the risk of dementia. We investigated the effect of type 2 diabetes duration on the risk of dementia. Design Prospective cohort study using health claims data representative for the older German population. The data contain information about diagnoses and medical prescriptions from the in- and outpatient sector. Methods We performed piecewise exponential models with a linear and a quadratic term for time since first type 2 diabetes diagnosis to predict the dementia risk in a sample of 13,761 subjects (2,558 dementia cases) older than 65 years. We controlled for severity of diabetes using the Adopted Diabetes Complications Severity Index. Results We found a U-shaped dementia risk over time. After type 2 diabetes diagnosis the dementia risk decreased (26% after 1 year) and reached a minimum at 4.75 years, followed by an increase through the end of follow-up. The pattern was consistent over different treatment groups, with the strongest U-shape for insulin treatment and for those with diabetes complications at the time of diabetes diagnosis. Conclusions We identified a non-linear association of type 2 diabetes duration and the risk of dementia. Physicians should closely monitor cognitive function in diabetic patients beyond the first few years after diagnosis, because the later increase in dementia occurred in all treatment groups.
Incidence of common infectious diseases in Japan during the COVID-19 pandemic

Recent reports indicate that respiratory infectious diseases were suppressed during the novel coronavirus disease-2019 (COVID-19) pandemic. COVID-19 led to behavioral changes aimed to control droplet transmission or contact transmission. In this study, we examined the incidence of common infectious diseases in Japan during the COVID-19 pandemic. COVID-19 data were extracted from the national data based on the National Epidemiological Surveillance of Infectious Diseases (NESID). Common infectious diseases were selected from notifiable infectious diseases under the NESID. The epidemic activity of the diseases during 2015–2020 was evaluated based on the Infectious Disease Weekly Reports published by the National Institute of Infectious Diseases. Each disease was then categorized according to the route of transmission. Many Japanese people had adopted hygienic activities, such as wearing masks and hand washing, even before the COVID-19 pandemic. We examined the correlation between the time-series of disease counts of common infectious diseases and COVID-19 over time using cross-correlation analysis. The weekly number of cases of measles, rotavirus, and several infections transmitted by droplet spread, was negatively correlated with the weekly number of cases of COVID-19 for up to 20 weeks in the past. According to the difference-in-differences analysis, the activity of influenza and rubella was significantly lower starting from the second week in 2020 than that in 2015–2019. Only legionellosis was more frequent throughout the year than in 2015–2019. Lower activity was also observed in some contact transmitted, airborne-transmitted, and fecal-oral transmitted diseases. However, carbapenem-resistant Enterobacteriaceae, exanthema subitum, showed the same trend as that over the previous 5 years. In conclusion, our study shows that public health interventions for the COVID-19 pandemic may have effectively prevented the transmission of most droplet-transmitted diseases and those transmitted through other routes.
Systemic and adipose tissue inflammation in NASH: correlations with histopathological aspects

Introduction: Non-alcoholic steatohepatitis (NASH) is a progressive form of liver steatosis that involves a risk of progression towards fibrosis, cirrhosis, and end-stage liver disease. Low-grade inflammation is recognized to be involved in non-alcoholic fatty liver disease (NAFLD) pathogeny. Additionally, adipose tissue dysfunction plays an important role in the development of metabolic diseases. Patients, Materials and Methods: We conducted a study on 68 patients with liver steatosis confirmed through liver biopsy during the surgery. In all the patients, we recorded anthropometric parameters and we performed blood tests for systemic inflammation [high-sensitivity C-reactive protein (hs-CRP), fibrinogen] and serum adipokines related to adipose tissue inflammation (leptin, adiponectin). Additional to histopathological examination, we also performed the immunohistochemical study of inflammatory mononuclear cells. Results: The 68 patients had a mean age of 56.57±4.94 years old, had a mean value of hs-CRP of 2.30±0.91 mg/L, a mean value of leptin of 14.02±17.02 ng/mL and a mean value of adiponectin of 7.54±0.38 mg/L. In all the cases studied by liver biopsy, the steatosis exceeded 5% of hepatocytes, but the frequency of NASH was 26.47%. Cluster of differentiation (CD)45-positive, CD4-positive, and CD8-positive T-lymphocytes predominated in the studied cases. We obtained a statistically significant high association between definite NASH and the values of hs-CRP, serum adiponectin and leptin/adiponectin ratio (p<0.0001). Conclusions: Systemic and adipose tissue inflammation was statistically significant associated with histological lesions of steatosis and NASH, suggesting that the determination of hs-CRP and serum adipokines in dynamics in patients with NAFLD is predictive for the progression of the disease.
Targeting disialoganglioside GD2 with chimeric antigen receptor-redirected T cells in lung cancer

Background We explored whether the disialoganglioside GD2 (GD2) is expressed in small cell lung cancer (SCLC) and non-SCLC (NSCLC) and can be targeted by GD2-specific chimeric antigen receptor (CAR) T cells. Methods GD2 expression was evaluated in tumor cell lines and tumor biopsies by flow cytometry and immunohistochemistry. We used a GD2.CAR that coexpress the IL-15 to promote T-cell proliferation and persistence, and the inducible caspase 9 gene safety switch to ablate GD2.CAR-T cells in case of unforeseen toxicity. The antitumor activity of GD2.CAR-T cells was evaluated using in vitro cocultures and in xenograft models of orthotopic and metastatic tumors. The modulation of the GD2 expression in tumor cell lines in response to an epigenetic drug was also evaluated. Results GD2 was expressed on the cell surface of four of fifteen SCLC and NSCLC cell lines (26.7%) tested by flow cytometry, and in 39% of SCLC, 72% of lung adenocarcinoma and 56% of squamous cell carcinoma analyzed by immunohistochemistry. GD2 expression by flow cytometry was also found on the cell surface of tumor cells freshly isolated from tumor biopsies. GD2.CAR-T cells exhibited antigen-dependent cytotoxicity in vitro and in vivo in xenograft models of GD2-expressing lung tumors. Finally, to explore the applicability of this approach to antigen low expressing tumors, we showed that pretreatment of GD2low/neg lung cancer cell lines with the Enhancer of zeste homolog 2 inhibitor tazemetostat upregulated GD2 expression at sufficient levels to trigger GD2.CAR-T cell cytotoxic activity. Conclusions GD2 is a promising target for CAR-T cell therapy in lung cancer. Tazemetostat treatment could be used to upregulate GD2 expression in tumor cells, enhancing their susceptibility to CAR-T cell targeting.
Research on the Formation Mechanism of MgO and Al2O3 on Composite Calcium Ferrite Based on DA-RBF Neural Network

Calcium complex ferrate is an ideal binder phase in the sintered ore phase, and a detailed study of the whole process of calcium complex ferrate generation is of great significance to improve the quality of sintered ore. In this paper, we first investigated calcium ferrate containing aluminum (CFA), which is an important precursor compound for the generation of complex calcium ferrate (SFCA), followed by a series of composite calcium ferrate generation process phase XRD detections and data preprocessing of data. Data correlation and data fitting analysis were combined with composite calcium ferrite phase diagram energy spectrum analysis to obtain the effect of MgO and Al2O3 on the formation of composite calcium ferrite. Then a modified RBF neural network model using the resource allocation network algorithm (RAN) was used to predict the generation trend of complex calcium ferrate. The parameters of the neural network are optimized with the Dragonfly algorithm, compared with the traditional RBF neural network. The prediction accuracy of the improved algorithm was found to be higher, with a prediction result of 97.6%. Finally, the predicted results were based on comparative metallurgical experimental results and data analysis. The validity and accuracy of the findings in this paper were verified.
Sulpirid induzierte Dystonie bei einem Patienten mit Tourette-Syndrom - eine Schlaganfall ähnliche Präsentation

Zusammenfassung Wir berichten über den Fall einer Sulpirid-induzierten Dystonie bei einem Patienten mit einem bekannten Tourette Syndrom. Aufgrund des plötzlichen Auftretens einer schlaganfallähnlichen Symptomatik während einer körperlichen Belastung war die eigentliche Dystoniediagnose erschwert. Nach Ausschluss einer vaskulären Ursache (Blutung, Gefäßdissektion, etc.) durch geeigneter Bildgebung wurde Biperiden infundiert, welche zum sofortigen Verschwinden der Dystonie führte.
Frequency and estimated costs of ten low-value practices in the Spanish primary care: a retrospective study

ABSTRACT Background The frequency of low-value practices (LVPs) in the healthcare system is a worldwide challenge. This study aimed to evaluate the LVPs trend in Spanish primary care (PC), its frequency in both sexes, and estimate its related extra cost. Methods A multicentric, retrospective, and national research project was conducted. Ten LVPs highly frequent and potentially harmful for patients were analyzed (majority of them related to prescription). Algorithms were applied to collect the data from 28,872,851 episodes registered into national databases (2015–2017). Results LVPs registered a total of 7,160,952 (26.5%) episodes plus a total of 259,326 avoidable PSA screening tests. In adults, a high frequency was found for inadequate prescription of paracetamol antibiotics, and benzodiazepines . Women received more jeopardizing practices (p ≤ 0.001). Pediatrics presented a downward of antibiotic and paracetamol-ibuprofen prescription combination. The estimated extra cost was close to €292 million (2.8% of the total cost in PC). Conclusion LVPs reduction during the analyzed period was moderate compared to studies following ‘Choosing Wisely list’ of LVPs and must improve to reduce patient risk and the extra related costs.
Less common synaptic input between muscles from the same group allows for more flexible coordination strategies during a fatiguing task.

This study aimed to determine whether neural drive is redistributed between muscles during a fatiguing isometric contraction, and if so, whether the initial level of common synaptic input between these muscles constrains this redistribution. We studied two muscle groups: triceps surae (14 participants) and quadriceps (15 participants). Participants performed a series of submaximal isometric contractions and a torque-matched contraction maintained until task failure. We used high-density surface electromyography to identify the behavior of 1874 motor units from the soleus, gastrocnemius medialis (GM), gastrocnemius lateralis(GL), rectus femoris, vastus lateralis (VL), and vastus medialis(VM). We assessed the level of common drive between muscles in absence of fatigue using a coherence analysis. We also assessed the redistribution of neural drive between muscles during the fatiguing contraction through the correlation between their cumulative spike trains (index of neural drive). The level of common drive between VL and VM was significantly higher than that observed for the other muscle pairs, including GL-GM. The level of common drive increased during the fatiguing contraction, but the differences between muscle pairs persisted. We also observed a strong positive correlation of neural drive between VL and VM during the fatiguing contraction (r=0.82). This was not observed for the other muscle pairs, including GL-GM, which exhibited differential changes in neural drive. These results suggest that less common synaptic input between muscles allows for more flexible coordination strategies during a fatiguing task, i.e., differential changes in neural drive across muscles. The role of this flexibility on performance remains to be elucidated.