Miracle-dz/UniFAR-FTUs huggingface dataset:
V2CNet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation

We propose V2CNet, a new deep learning framework to automatically translate the demonstration videos to commands that can be directly used in robotic applications. Our V2CNet has two branches and aims at understanding the demonstration video in a fine-grained manner. The first branch has the encoder-decoder architecture to encode the visual features and sequentially generate the output words as a command, while the second branch uses a Temporal Convolutional Network (TCN) to learn the fine-grained actions. By jointly training both branches, the network is able to model the sequential information of the command, while effectively encodes the fine-grained actions. The experimental results on our new large-scale dataset show that V2CNet outperforms recent state-of-the-art methods by a substantial margin, while its output can be applied in real robotic applications. The source code and trained models will be made available.
Deep residual network framework for structural health monitoring

Convolutional neural networks have been widely employed for structural health monitoring and damage identification. The convolutional neural network is currently considered as the state-of-the-art method for structural damage identification due to its capabilities of efficient and robust feature learning in a hierarchical manner. It is a tendency to develop a convolutional neural network with a deeper architecture to gain a better performance. However, when the depth of the network increases to a certain level, the performance will degrade due to the gradient vanishing issue. Residual neural networks can avoid the problem of vanishing gradients by utilizing skip connections, which allows the information flowing to the next layer through identity mappings. In this article, a deep residual network framework is proposed for structural health monitoring of civil engineering structures. This framework is composed of purely residual blocks which operate as feature extractors and a fully connected layer as a regressor. It learns the damage-related features from the vibration characteristics such as mode shapes and maps them into the damage index labels, for example, stiffness reductions of structures. To evaluate the efficacy and robustness of the proposed framework, an intensive evaluation is conducted with both numerical and experimental studies. The comparison between the proposed approach and the state-of-the-art models, including a sparse autoencoder neural network, a shallow convolutional neural network and a convolutional neural network with the same structure but without skip connections, is conducted. In the numerical studies, a 7-storey steel frame is investigated. Four scenarios with considering measurement noise and finite element modelling errors in the data sets are studied. The proposed framework consistently outperforms the state-of-the-art models in all the scenarios, especially for the most challenging scenario, which includes both measurement noise and uncertainties. Experimental studies on a prestressed concrete bridge in the laboratory are conducted. The proposed framework demonstrates consistent damage prediction results on this beam with the state-of-the-art models.
Effects of Dipeptidyl Peptidase-4 Inhibitors on Renal Outcomes in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis

Background To investigate the effects of dipeptidyl peptidase-4 (DPP-4) inhibitors on renal outcomes in patients with type 2 diabetes. Methods MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were searched to identify randomized controlled trials (RCTs) of DPP-4 inhibitors from inception to September 2017. We selected eligible RCTs comparing DPP-4 inhibitors with placebo or other antidiabetic agents and reporting at least one renal outcome. A meta-analysis was conducted to calculate standardized mean differences, weighted mean differences (WMDs), relative risks (RRs), and 95% confidence intervals (CIs) for each renal outcome. Results We included 23 RCTs with 19 publications involving 41,359 patients. Overall changes in urine albumin-to-creatinine ratio were comparable between DPP-4 inhibitors and controls (P=0.150). However, DPP-4 inhibitors were associated with significantly lower risk of incident microalbuminuria (RR, 0.89; 95% CI, 0.80 to 0.98; P=0.022) and macroalbuminuria (RR, 0.77; 95% CI, 0.61 to 0.97; P=0.027), as well as higher rates of regression of albuminuria (RR, 1.22; 95% CI, 1.10 to 1.35; P<0.001) compared with controls. Although DPP-4 inhibitors were associated with small but significantly lower estimated glomerular filtration rate (WMD, −1.11 mL/min/1.73 m2; 95% CI, −1.78 to −0.44; P=0.001), there was no difference in the risk of end-stage renal disease between two groups (RR, 0.93; 95% CI, 0.76 to 1.14; P=0.475). Conclusion DPP-4 inhibitors had beneficial renal effects mainly by reducing the risk of development or progression of albuminuria compared with placebo or other antidiabetic agents.
“The pained and silent song of a branch”: Ecological precarity in the poetry of Taufiq Ismail and Khairani Barokka

ABSTRACT This article aims to illuminate how Indonesian poets Taufiq Ismail and Khairani Barokka narrativize late-20th- and early-21st-century ecological precarity, including urgent issues of deforestation, pollution, biodiversity loss, and Indigenous dispossession. Ismail set the ecocritical agenda in Indonesian poetry early on with “I Want to Write Poetry” (1975), “Reading the Signs” (1982), and “The Pained and Silent Song of a Branch” (1982). Barokka’s long poem Indigenous Species (2016) similarly warns against neocolonial appropriation of land and advocates ecojustice for Indigenous communities of Kalimantan, centre of Indonesia’s palm oil industry. Their poetry links ecological change and catastrophe in Indonesia to the “imperial debris” of globalization. Focused on preserving the value of traditional Indonesian relationships to the more-than-human world, their work gives prominence to the affective dimensions of ecological precarity. The article adopts a postcolonial environmental humanities framework integrating Laura Stoler’s “imperial debris” and Akhil Gupta’s theorization of environmental precarity as “immiserization”.
Health Care Use and Costs Among Patients With Nonalcoholic Steatohepatitis With Advanced Fibrosis Using the Fibrosis‐4 Score

Limited evidence exists on the clinical and economic burden of advanced fibrosis in patients with nonalcoholic fatty liver disease/nonalcoholic steatohepatitis (NAFLD/NASH) due to the invasiveness of liver biopsies for accurately staging liver disease. The fibrosis‐4 (FIB‐4) score allows for noninvasive assessment of liver fibrosis by using clinical and laboratory data alone. This study aimed to characterize the comorbidity burden, health care resource use (HCRU), and costs among patients with NAFLD/NASH with FIB‐4‐defined F3 (bridging fibrosis) and F4 (compensated cirrhosis) fibrosis. Using the Optum Research Database, a retrospective cohort study was conducted among 251,725 commercially insured adult patients with ≥1 NAFLD/NASH diagnosis from January 1, 2008, to August 31, 2016, and laboratory data required to calculate FIB‐4 scores. Five criteria using varying FIB‐4 score cutoffs were identified based on expert clinical opinion and published literature. Date of the first valid FIB‐4 score marked the index date. Mean annual HCRU and costs were calculated during the pre‐index and post‐index periods. The prevalence of FIB‐4‐based F3 and F4 fibrosis was 0.40%‐2.72% and 1.03%‐1.61%, respectively. Almost 50% of patients identified with FIB‐4‐based F3 or F4 had type 2 diabetes, cardiovascular disease, or renal impairment. Total all‐cause health care costs increased significantly from pre‐index to post‐index for patients with FIB‐4‐based F3 fibrosis across most criteria (17%‐29% increase) and patients with FIB‐4‐based F4 fibrosis across all criteria (47%‐48% increase). Inpatient costs were the primary drivers of this increment. Conclusion: Significant increases in HCRU and costs were observed following FIB‐4‐based identification of F3 and F4 fibrosis among U.S. adults with NAFLD/NASH. These data suggest the importance of early identification and management of NAFLD/NASH that may halt or reduce the risk of disease progression and limit the underlying burden.
The carbon-to-oxygen ratio: implications for the spectra of hydrogen-dominated exoplanet atmospheres

We present results from one-dimensional atmospheric simulations investigating the effect of varying the carbon-to-oxygen (C/O) ratio on the thermal structure, chemical composition and transmission and emission spectra, for irradiated hydrogen-dominated atmospheres. We find that each of these properties of the atmosphere are strongly dependent on the individual abundances (relative to hydrogen) of carbon and oxygen. We confirm previous findings that different chemical equilibrium compositions result from different sets of element abundances but with the same C/O ratio. We investigate the effect of this difference in composition on the thermal structure and simulated spectra. We also simulate observations using the PandExo tool and show that these differences are observationally significant with current (i.e. Hubble Space Telescope) and future (i.e. James Webb Space Telescope) instruments. We conclude that it is important to consider the full set of individual element abundances, with respect to hydrogen, rather than the ratios of only two elements, such as the C/O ratio, particularly when comparing model predictions with observed transmission and emission spectra.
Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution

Spectral super-resolution (SSR) aims at generating a hyperspectral image (HSI) from a given RGB image. Recently, a promising direction is to learn a complicated mapping function from the RGB image to the HSI counterpart using a deep convolutional neural network. This essentially involves mapping the RGB context within a size-specific receptive field centered at each pixel to its spectrum in the HSI. The focus thereon is to appropriately determine the receptive field size and establish the mapping function from RGB context to the corresponding spectrum. Due to their differences in category or spatial position, pixels in HSIs often require different-sized receptive fields and distinct mapping functions. However, few efforts have been invested to explicitly exploit this prior.To address this problem, we propose a pixel-aware deep function-mixture network for SSR, which is composed of a new class of modules, termed function-mixture (FM) blocks. Each FM block is equipped with some basis functions, i.e., parallel subnets of different-sized receptive fields. Besides, it incorporates an extra subnet as a mixing function to generate pixel-wise weights, and then linearly mixes the outputs of all basis functions with those generated weights. This enables us to pixel-wisely determine the receptive field size and the mapping function. Moreover, we stack several such FM blocks to further increase the flexibility of the network in learning the pixel-wise mapping. To encourage feature reuse, intermediate features generated by the FM blocks are fused in late stage, which proves to be effective for boosting the SSR performance. Experimental results on three benchmark HSI datasets demonstrate the superiority of the proposed method.
Properties of the Bare Nucleus of Comet 96P/Machholz 1

We observed comet 96P/Machholz 1 on a total of nine nights before and after perihelion during its 2017/2018 apparition. Both its unusually small perihelion distance and the observed fragmentation during multiple apparitions make 96P an object of great interest. Our observations show no evidence of a detectable dust coma, implying that we are observing a bare nucleus at distances ranging from 2.3 to 3.8 au. Based on this assumption, we calculated its color and found average values of g′–r′ = 0.50 ± 0.04, r′–i′ = 0.17 ± 0.03, and i′–z′ = 0.06 ± 0.04. These are notably more blue than those of the nuclei of other Jupiter-family and long-period comets. Furthermore, assuming a bare nucleus, we found an equivalent nuclear radius of 3.4 ± 0.2 km with an axial ratio of at least 1.6 ± 0.1. The lightcurve clearly displays one large peak, one broad flat peak, and two distinct troughs, with a clear asymmetry that suggests that the shape of the nucleus deviates from that of a simple triaxial ellipsoid. This asymmetry in the lightcurve allowed us to constrain the nuclear rotation period to 4.10 ± 0.03 hr and 4.096 ± 0.002 hr before and after perihelion, respectively. Within the uncertainties, 96P’s rotation period does not appear to have changed throughout the apparition, and we conclude a maximum possible change in rotation period of 130 s. The observed properties were compared to those of comet 322P and interstellar object 1I/‘Oumuamua in an attempt to study the effects of close perihelion passages on cometary surfaces and their internal structure and the potential interstellar origin of 96P.
Gene expression, protein profiling, and chemotactic activity of infrapatellar fat pad mesenchymal stem cells in pathologies of the knee joint

The infrapatellar fat pad (IPFP) is a periarticular adipose knee tissue. This tissue contains a large number of mesenchymal stem cells (MSCs). In the present work, we wanted to study the IPFP MSCs and their relationship and differences in two groups, anterior cruciate ligament (ACL) ruptures knees and ostheoarthrosis (OA). The IPFP of 42 patients with OA or ACL rupture were analyzed. Isolation, primary culture, and a genetic and proteomic study of MSCs from IPFP were performed. Gene expression of IL‐6, tumor necrosis factor (TNF), IL‐8, HSPA1A (Hsp70), CXCL10, RANTES, MMP1, MMP3, TIMP1, and BMP7 was analyzed by real‐time quantitative polymerase chain reaction (RT‐qPCR). We analyzed MSCs from from 12 diferents patients in two cellular pools (6 from AO disease and 6 from ALC rupture to form two cell pool), for the iTRAQ Proteomic Assay. The conditional media were used in quantitative analysis of MSC soluble factors by Luminex and for de migration assay. A higher gene expression of IL‐6, TNF, CXCL10, RANTES, and MMP1 and OPG in MSCs from OA versus ACL (p < 0.05) was observed. Conversely HSPA1A, TIMP1, and RANKL showed a significant lower expression in OA‐MSCs (p < 0.05). In the secretome analysis, adipsin and visfantin levels in the supernatants from OA‐MSCs were lower (p < 0.05) respect to ACL‐MSCs. Also, the monocytic cells migrated two‐folds in the presence of conditioned media from OA‐MSCs patients versus patients with ACL‐MSC. The infrapatellar pad should be considered as an adipose tissue capable of producing and excreting inflammatory mediators directly in the knee joint, influencing the development and progression of knee joint pathologies.
Novel therapeutic approaches in utilizing platelet lysate in regenerative medicine: Are we ready for clinical use?

Hemoderivative materials are used to treat different diseases. These derivatives include platelet‐rich plasma, serum, platelet gel, and platelet lysate (PL). Among them, PL contains more growth factors than the others and its production is inexpensive and easy. PL is one of the proper sources of platelet release factors. It is used in cells growth and proliferation and is a good alternative to fetal bovine serum. In recent years, the clinical use of PL has gained more appeal by scientists. PL is a solution saturated by growth factors, proteins, cytokines, and chemokines and is administered to treat different diseases such as wound healing, bone regeneration, alopecia, oral mucositis, radicular pain, osteoarthritis, and ocular diseases. In addition, it can be used in cell culture for cell therapy and tissue transplantation purposes. Platelet‐derived growth factor, fibroblast growth factor, insulin‐like growth factor, transforming growth factor β, and vascular endothelial growth factor are key PL growth factors playing a major role in cell proliferation, wound healing, and angiogenesis. In this paper, we scrutinized recent advances in using PL and PL‐derived growth factors to treat diseases and in regenerative medicine, and the ability to replace PL with other hemoderivative materials.
Spin-polarized correlated insulator and superconductor in twisted double bilayer graphene.

Ferromagnetism and superconductivity typically compete with each other 1 since the internal magnetic field generated in a magnet suppresses the formation of spin-singlet Cooper pairs in conventional superconductors 2. Only a handful of ferromagnetic superconductors are known in heavy fermion systems 3–5, where many-body electron interactions promoted by the narrow energy bands play a key role in stabilizing these emergent states. Recently, interaction-driven superconductivity and ferromagnetism have been demonstrated as separate phenomena in different density regimes of flat bands enabled by graphene moiré superlattices 6–9. Combining superconductivity and magnetism in a single ground state may lead to more exotic quantum phases. Here, employing van der Waals heterostructures of twisted double bilayer graphene (TDBG), we realize a flat electron band that is tunable by perpendicular electric fields. Similar to the magic angle twisted bilayer graphene, TDBG exhibits energy gaps at the half and quarter filled flat bands, indicating the emergence of correlated insulating states. We find that the gaps of these insulating states increase with in-plane magnetic field, suggesting a ferromagnetic order. Upon doping the ferromagnetic half-filled insulator, superconductivity emerges with a critical temperature controlled by both density and electric fields. We observe that the in-plane magnetic field enhances the superconductivity in the low field regime, suggesting spin-polarized electron pairing. Spin-polarized superconducting states discovered in TDBG provide a new route to engineering interaction-driven topological superconductivity 10.
Observational constraints on sign-changeable interaction models and alleviation of the 
H0
 tension

We investigate various scenarios which include interaction forms between dark matter and dark energy that exhibit sign reverse, namely where the transfer of energy between the dark fluids changes sign during evolution. We study the large-scale inhomogeneities in such interacting scenarios and we confront them with the latest astronomical data. Our analysis shows that the sign-changeable interaction models are able to produce stable perturbations. Additionally, the data seem to slightly favor a non-zero interaction, however, within $1\sigma$ confidence level (CL) the scenarios cannot be distinguished from non-interacting cosmologies. We find that the best-fit value of the dark-energy equation-of-state parameter lies in the phantom regime, while the quintessence region is also allowed nevertheless at more than 2$\sigma$ CL. Examining the effect of the interaction on the CMB TT and matter power spectra we show that while from the simple spectra it is hard to distinguish the interacting case from $\Lambda$CDM scenario, in the residual graphs the interaction is indeed traceable. Moreover, we find that sign-changeable interaction models can reconcile the $H_0$ tension, however the $\sigma_8$ tension is still persisting. Finally, we examine the validity of the laws of thermodynamics and we show that the generalized second law is always satisfied, while the second derivative of the total entropy becomes negative at late times which implies that the universe tends towards thermodynamic equilibrium.
International Effects of Corporate Tax Cuts on Income Distribution

This paper evaluates the domestic and cross‐country effects of corporate income tax (CIT) cuts on income inequality using a tractable multi‐country DSGE model calibrated to U.S. and Canadian data. We illustrate that the cross‐border propagation of differentiated equity holdings can help explain the increase in income inequality, both within‐ and cross‐country, in response to a CIT cut. A lower barrier to capital mobility alleviates this divergence in income, while lower trade costs exert little effects on income distribution. The model is also consistent with recent evidence that middle‐income workers experience lower income growth than their counterparts in either extreme of the skill spectrum.
Skew selection for factor stochastic volatility models

ABSTRACT This paper proposes factor stochastic volatility models with skew error distributions. The generalized hyperbolic skew t-distribution is employed for common-factor processes and idiosyncratic shocks. Using a Bayesian sparsity modeling strategy for the skewness parameter provides a parsimonious skew structure for possibly high-dimensional stochastic volatility models. Analyses of daily stock returns are provided. Empirical results show that the skewness is important for common-factor processes but less for idiosyncratic shocks. The sparse skew structure improves prediction and portfolio performance.
Entanglement 25 Years after Quantum Teleportation: Testing Joint Measurements in Quantum Networks

Twenty-five years after the invention of quantum teleportation, the concept of entanglement gained enormous popularity. This is especially nice to those who remember that entanglement was not even taught at universities until the 1990s. Today, entanglement is often presented as a resource, the resource of quantum information science and technology. However, entanglement is exploited twice in quantum teleportation. Firstly, entanglement is the “quantum teleportation channel”, i.e., entanglement between distant systems. Second, entanglement appears in the eigenvectors of the joint measurement that Alice, the sender, has to perform jointly on the quantum state to be teleported and her half of the “quantum teleportation channel”, i.e., entanglement enabling entirely new kinds of quantum measurements. I emphasize how poorly this second kind of entanglement is understood. In particular, I use quantum networks in which each party connected to several nodes performs a joint measurement to illustrate that the quantumness of such joint measurements remains elusive, escaping today’s available tools to detect and quantify it.
Failure-Scenario Maker for Rule-Based Agent using Multi-agent Adversarial Reinforcement Learning and its Application to Autonomous Driving

We examine the problem of adversarial reinforcement learning for multi-agent domains including a rule-based agent. Rule-based algorithms are required in safety-critical applications for them to work properly in a wide range of situations. Hence, every effort is made to find failure scenarios during the development phase. However, as the software becomes complicated, finding failure cases becomes difficult. Especially in multi-agent domains, such as autonomous driving environments, it is much harder to find useful failure scenarios that help us improve the algorithm. We propose a method for efficiently finding failure scenarios; this method trains the adversarial agents using multi-agent reinforcement learning such that the tested rule-based agent fails. We demonstrate the effectiveness of our proposed method using a simple environment and autonomous driving simulator.