Virtual to Real Reinforcement Learning for Autonomous Driving

Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. Experiments show that our proposed virtual to real (VR) reinforcement learning (RL) works pretty well. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data

MerTK inhibition for the treatment of organ fibrosis

Background: The evolution of multi-chronic diseases is largely influenced by fibrosis, which acts as a key route. However, effective and low side-effect antifibrotic therapies remains limited. In organ fibrosis, scarring mainly driven by transforming growth factor-β (TGF-β) is a pivotal contributor to disease progression. However, ubiquitously expressed TGF-β is hard to be applied in fibrosis therapy, and identification of refined alternative targets to inhibit TGF-β signalling is needed.In recent studies, researchers have uncovered new genetic risk variants of myeloid-epithelial reproductive tyrosine kinase (MerTK). Previous studies have shown a significant correlation between these polymorphisms and liver fibrosis progress. This study investigates the potential of MerTK as a new therapeutic target for multiorgan fibrosis, including liver, kidney, and lung, as well as the underlying processes involved in its therapeutic activities.Methods: MerTK and TGF-β interaction was investigated in vitro. Fibrosis models of liver (bile duct ligation), kidney (unilateral ureteral obstruction) and lung (bleomycin injection) were established by Mertk knockout mice to explore their role in the fibrosis process of each organ. The mechanism of MerTK was explored through RNA-seq, ATAC-seq and Cut&Tag-seq.Results: I identified MerTK as a TGF-β-inducible nodal effector of organ fibrosis that is upregulated in multiple fibrotic organs in mice. TGF-β elicits a rapid upregulation of MerTK expression in fibroblast. MerTK, on the other hand, can stimulate further production of TGF-β and facilitate the activation of profibrotic TGF-β/SMAD and non-SMAD signaling pathways. Conclusion: The findings combining in vitro, and in vivo investigations have provided evidence that MerTK functions as a novel regulator of fibrosis across several organs by involving shared core pathway. This research proposes that MerTK may serve as a promising therapeutic target for the treatment of multiorgan fibrosis

Optimal human labelling for anomaly detection in industrial inspection

Anomaly detection with machine learning in industrial inspection systems for manufactured products relies on labelleddata. This rises the question how the labelling by humans should be conducted. We consider the case where we want to optimise the cost of the combined inspection process done by humans and an algorithm. This also influences the combined performance of the trained model as well as the knowledge of the performance of this model. We focus on so called one-class classification problem models which produce a continuous outlier score. We establish some cost model for human and machine combined inspection of samples. We then discuss in this cost model how to select two optimal boundaries of the outlier score where in between these two boundaries human inspection takes place. We also frame this established knowledge into an applicable algorithm

HubbardNet: Efficient Predictions of the Bose-Hubbard Model Spectrum with Deep Neural Networks

We present a deep neural network (DNN)-based model (HubbardNet) to variationally find the ground state and excited state wavefunctions of the one-dimensional and two-dimensional Bose-Hubbard model. Using this model for a square lattice with $M$ sites, we obtain the energy spectrum as an analytical function of the on-site Coulomb repulsion, $U$, and the total number of particles, $N$, from a single training. This approach bypasses the need to solve a new hamiltonian for each different set of values $(U,N)$. Using \texttt{HubbardNet}, we identify the two ground state phases of the Bose-Hubbard model (Mott insulator and superfluid). We show that the DNN-parametrized solutions are in excellent agreement with results from the exact diagonalization of the hamiltonian, and it outperforms exact diagonalization in terms of computational scaling. These advantages suggest that our model is promising for efficient and accurate computation of exact phase diagrams of many-body lattice hamiltonians

The role of electrochemical properties of biochar to promote methane production in anaerobic digestion

The electrochemical properties of biochar may be the key factor to promote anaerobic digestion, which has attracted extensive attention. However, the mechanism and the role of the electrochemical properties of biochar are remaining unclear. In this study, biochar with different electrochemical properties was prepared by pyrolysis at different temperatures (BC300/600/900) and oxidation or reduction modification (O/RBC300/600/900). The biochar was added as an additive to promote methanogenic performance of anaerobic digesters of glucose and food waste. In both anaerobic digestion systems, the cumulative methane production of food waste increased by 42.07% and the maximum methane production rate of glucose enhanced by 17.80% after BC900 treatment. RBC600 was inferior to BC900, but superior to BC600. Microbiological analysis suggests that biochar enriched the relative abundant Synergistia and Methanoculleus. This is conducive to the establishment of the direct interspecies electrons transfer (DIET). Results from correlation analysis, principal component analysis and machine learning confirmed that both of the electron donating capacities (EDC) and electrical conductivity (EC) are dominated factors affecting the cumulative methane yield. Through the analysis of electrochemical properties and preparation process of biochar, the results showed that the pyrolysis temperature increases and the content of phenolic hydroxyl decreases under medium temperature of biochar, which was beneficial to the methane production. This study found the key factors of the electrochemical properties of biochar in anaerobic digestion, provided new insights for the mechanism of biochar promoting anaerobic digestion and proposed novel directions for the preparation of biochar.acceptedVersio

Exceptional Performance of Hierarchical Ni-Fe (hydr)oxide@NiCu Electrocatalysts for Water Splitting

Developing low‐cost bifunctional electrocatalysts with superior activity for both the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is of great importance for the widespread application of the water splitting technique. In this work, using earth‐abundant transition metals (i.e., nickel, iron, and copper), 3D hierarchical nanoarchitectures, consisting of ultrathin Ni–Fe layered‐double‐hydroxide (Ni–Fe LDH) nanosheets or porous Ni–Fe oxides (NiFeOx) assembled to a metallic NiCu alloy, are delicately constructed. In alkaline solution, the as‐prepared Ni–Fe LDH@NiCu possesses outstanding OER activity, achieving a current density of 10 mA cm−2 at an overpotential of 218 mV, which is smaller than that of RuO2 catalyst (249 mV). In contrast, the resulting NiFeOx@NiCu exhibits better HER activity, yielding a current density of 10 mA cm−2 at an overpotential of 66 mV, which is slightly higher than that of Pt catalyst (53 mV) but superior to all other transition metal (hydr)oxide‐based electrocatalysts. The remarkable activity of the Ni–Fe LDH@NiCu and NiFeOx@NiCu is further demonstrated by a 1.5 V solar‐panel‐powered electrolyzer, resulting in current densities of 10 and 50 mA cm−2 at overpotentials of 293 and 506 mV, respectively. Such performance renders the as‐prepared materials as the best bifunctional electrocatalysts so far

Potential distributions of seven sympatric sclerophyllous oak species in Southwest China depend on climatic, non-climatic, and independent spatial drivers

Key message An ensemble modelling approach was performed to predict the distributions of seven sympatric sclerophyllous oak species in the Hengduan Mountains of Southwest China. Spatial eigenvector filters revealed missing factors in addition to commonly used environmental variables, thus effectively improved predictive accuracy for the montane oak species. This study identified a richness center of sclerophyllous oaks, which provides a reference for proper conservation and utilization of oak resources. Context As key species and important trees for construction- and fuel-wood, montane sclerophyllous oaks (Quercus sect. Heterobalanus) in the Hengduan Mountains of Southwest China are threatened by climate change, habitat fragmentation, and human activities. Aims This study aims to simulate the potential distributions of seven sympatric sclerophyllous oak species with an emphasis on exploring the relative importance of climatic, non-climatic, and additional spatial factors. Methods We performed an ensemble modelling approach of six ecological niche models in combination with spatial eigenvector filters to predict the potential distributions of seven oak species. Results The results elucidated that temperature seasonality, followed by land use/cover and the human influence index were the most critical variables controlling oak species distributions. Regardless of the selected algorithm, the best performing models for most oaks combined climatic and non-climatic factors as well as additional spatial filters. Conclusion It is necessary to strengthen the conservation of oak species at the junction of Sichuan and Yunnan Province where we found the richness center of the studied oaks. Our research provides essential insights for the rational conservation and management of sclerophyllous oak species, suggesting that spatial constraints might reflect limited ability of migration under future climate change.Peer reviewe

Comparison of Two Methods for Osmolality Determination of Foods for Special Dietary Uses

A total of 46 samples from nine types of commercially available foods for special dietary uses were collected for osmolality measurement by a freezing point and a dew point osmometer, and the differences between the two methods were analysed. The results showed that the detection range of the freezing point osmometer was 195–763 mOsmol/kg with relative standard deviation (RSD) of 0.20%–4.08%, and the detection range of the dew point osmometer was 197–649 mmol/kg with RSD of 0.00%–3.66%. It was found that different reconstitution methods had a significant effect on the determination results, and the temperature of the solution also affected the parallelism of the determination results. Statistical analysis using t-test showed that there were significant differences between the results of the two methods for each of 31 samples. It was inferred from the experiments that whether the sample solution reached an ideal dilute solution state was the major factor affecting the significant difference between both methods. This study provides a theoretical basis for further research on the detection of osmolality in foods for special dietary uses, and highlights some key issues that need urgent attention in the design and production of foods for special dietary uses

Stability of osteotomy in minimally invasive hallux valgus surgery with “8” shaped bandage during gait: a finite element analysis

IntroductionHallux valgus, a common foot deformity, often necessitates surgical intervention. This study evaluates the biomechanical alterations in patients post-surgery, focusing on the efficacy of an “8” bandage fixation system to promote optimal recovery.MethodsA three-dimensional (3D) model was constructed using CT data from a patient with hallux valgus. A quasi-static finite element analysis (FEA) was conducted in conjunction with gait analysis to evaluate the biomechanical changes at the osteotomy site under “8” shaped bandage fixation following hallux valgus surgery. The effects of the “8” shaped bandage on the stability of the osteotomy site and bone healing were investigated at three load points during the gait cycle.ResultsDuring the Loading Response (LR), Midstance (MSt), and Terminal stance TSt phases, the osteotomy end experienced maximum Von Mises stresses of 0.118, 1.349, and 1.485 MPa, respectively. Correspondingly, the maximum principal stresses, all of which were compressive along the Z-axis, were 0.11662 N, 1.39266 N, and 1.46762 N, respectively. Additionally, these phases showed a maximum relative total displacement of 0.848 mm and a maximum relative shear displacement of 0.872 mm.ConclusionDuring the stance phase, the osteotomy end of the first metatarsal is predominantly subjected to compressive stress, with the relative displacement within the safe range to promote healing. The application of an “8” bandage for external fixation after surgery can maintain the dynamic stability of osteotomy sites post-minimally invasive hallux valgus correction during the gait cycle, thereby promoting the healing of the osteotomy ends

DDC-PIM: Efficient Algorithm/Architecture Co-design for Doubling Data Capacity of SRAM-based Processing-In-Memory

Processing-in-memory (PIM), as a novel computing paradigm, provides significant performance benefits from the aspect of effective data movement reduction. SRAM-based PIM has been demonstrated as one of the most promising candidates due to its endurance and compatibility. However, the integration density of SRAM-based PIM is much lower than other non-volatile memory-based ones, due to its inherent 6T structure for storing a single bit. Within comparable area constraints, SRAM-based PIM exhibits notably lower capacity. Thus, aiming to unleash its capacity potential, we propose DDC-PIM, an efficient algorithm/architecture co-design methodology that effectively doubles the equivalent data capacity. At the algorithmic level, we propose a filter-wise complementary correlation (FCC) algorithm to obtain a bitwise complementary pair. At the architecture level, we exploit the intrinsic cross-coupled structure of 6T SRAM to store the bitwise complementary pair in their complementary states ($Q/\overline{Q}$), thereby maximizing the data capacity of each SRAM cell. The dual-broadcast input structure and reconfigurable unit support both depthwise and pointwise convolution, adhering to the requirements of various neural networks. Evaluation results show that DDC-PIM yields about $2.84\times$ speedup on MobileNetV2 and $2.69\times$ on EfficientNet-B0 with negligible accuracy loss compared with PIM baseline implementation. Compared with state-of-the-art SRAM-based PIM macros, DDC-PIM achieves up to $8.41\times$ and $2.75\times$ improvement in weight density and area efficiency, respectively.Comment: 14 pages, to be published in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD