Miracle-dz/UniFAR-FTUs huggingface dataset:
Charge trapping and conduction in pure and iodine-doped biaxially-oriented polypropylene

Study of charge trapping and conduction in pure and iodine doped biaxially oriented polypropylene (BOPP) is presented. Structural and chemical modifications induced by iodine were investigated using X-ray, optical and infrared methods. Optical spectra of doped BOPP show absorption at 290 nm from charge transfer complexes. X-ray examination revealed a decrease in crystallinity and crystallite size after doping. The effect of iodine on charge trapping was determined by thermally stimulated current technique. Deep traps (120/spl deg/C peak) at crystalline-amorphous interfaces are destroyed by iodine, which provides new traps (68/spl deg/C peak) with activation energy 0.9 eV. Pressure dependence of conductivity indicates ionic conduction in pure samples and electronic conduction in doped samples. Steady state currents in 0.5%wt iodine doped BOPP were measured for fields 1 to 5/spl times/10/sup 5/ V cm/sup /spl minus/1/ and at elevated temperatures 22 to 50/spl deg/C. Iodine enhances conductivity by /spl sim/700/spl times/ in pure BOPP and the steady state conductivity shows a good fit of the 3-D Poole-Frenkel theory to the experiment. It is proposed that trapped electrons (arising due to donor-acceptor action) thermally released through PF lowering, predominantly contribute to the conduction. >
Novel Anti-Caking Materials in Compound Fertiliezer Based on Waste Animal/Plant Oil

A novel anti-caking material (E-Oil) was prepared from waste animal/plant oil and triethanolamine by catalytic synthesis in this study. The E-Oil was analyzed by FTIR and the anti-caking effect of anti-caking materials was described by caking ratio. The FTIR results showed that the reaction product of Waste Animal/Plant Oil and Hydramine was surfactant with anti-caking effect. The anti-caking test results indicated that E-Oil made the caking ratio of compound fertilizer decrease from 39.75% to 16.33%. Taking the cost into consideration, the optimal fraction of E Oil was 5‰ and the caking ratio could reach 16.33%, which could approximately meet the application requirements.
Effect of metabolizable protein intake on rates of plasma leucine turnover and protein synthesis in heifers

SUMMARY An isotope dilution method using [1-13C]leucine (Leu) infusion together with open-circuit calorimetry was applied to determine the effect of metabolizable protein (MP) intake on rates of plasma Leu turnover and whole body protein synthesis (WBPS) in six heifers. WBPS rate was estimated from rate of plasma Leu turnover and Leu oxidation to carbon dioxide. The experiment consisted of three levels of MP intake and was conducted in a two 3×3 Latin square designs of three 21-day periods. The experimental diet consisted of mixed hay, maize and soybean meal. Dietary MP intake of each dietary treatment was 4·3, 4·5 and 4·9 g/kg BW0·75/day by changing maize and soybean meal weights. Metabolizable energy (ME) intake was similar for all dietary treatments. When plasma α-[1-13C]keto-isocaproic acid enrichments were used as markers indicating intracellular Leu enrichments, plasma Leu turnover rate (LeuTR) increased (P=0·012) and WBPS tended to increase (P=0·091) as MP intake increased. In contrast, plasma LeuTR and WBPS were not influenced if plasma [1-13C]Leu was taken to indicate intracellular Leu enrichments. Total and plasma Leu oxidation rates did not change but intracellular Leu oxidation increased (P=0·044) with increasing MP intake. In heifers, it is suggested that rates of plasma Leu turnover and WBPS are influenced by dietary MP intake, independent of ME intake, although the change in MP intake was relatively small.
Magnetic interactions and phase transformations in FeM, M = (Pt, Rh) ordered alloys

We present results of the first-principles calculations for selected non-collinear magnetic configurations in L10 FePt and B2 FeRh. These results have been interpreted within the unified model of magnetic interactions which treats the Fe atomic moments as sufficiently localized degrees of freedom. The exchange fields of Fe atomic moment determine both the orientation and the value of the induced Rh/Pt atomic moments. The Pt moment fluctuations in FePt follow a relatively simple form while for the FeRh significant contributions of the non-linear susceptibility have to be taken into account. The latter, along with the remarkably strong Rh mediated exchange lead to a dominating contribution from the bi-quadratic non-Heisenberg terms. The effect of magnetic interactions mediated by the induced Rh/Pt moment on the order-order (FeRh) and order disorder (FePt) magnetic phase transition is discussed.
Robust High-Speed Running for Quadruped Robots via Deep Reinforcement Learning

Deep reinforcement learning has emerged as a popular and powerful way to develop locomotion controllers for quadruped robots. Common approaches have largely focused on learning actions directly in joint space, or learning to modify and offset foot positions produced by trajectory generators. Both approaches typically require careful reward shaping and training for millions of time steps, and with trajectory generators introduce human bias into the resulting control policies. In this paper, we present a learning framework that leads to the natural emergence of fast and robust bounding policies for quadruped robots. The agent both selects and controls actions directly in task space to track desired velocity commands subject to environmental noise including model uncertainty and rough terrain. We observe that this framework improves sample efficiency, necessitates little reward shaping, leads to the emergence of natural gaits such as galloping and bounding, and eases the sim-to-real transfer at running speeds. Policies can be learned in only a few million time steps, even for challenging tasks of running over rough terrain with loads of over 100% of the nominal quadruped mass. Training occurs in PyBullet, and we perform a sim-to-sim transfer to Gazebo and sim-to-real transfer to the Unitree A1 hardware. For sim-to-sim, our results show the quadruped is able to run at over 4 m/s without a load, and 3.5 m/s with a 10 kg load, which is over 83% of the nominal quadruped mass. For sim-to-real, the Unitree A1 is able to bound at 2 m/s with a 5 kg load, representing 42% of the nominal quadruped mass.
Privacy-preserving Object Detection

Privacy considerations and bias in datasets are quickly becoming high-priority issues that the computer vision community needs to face. So far, little attention has been given to practical solutions that do not involve collection of new datasets. In this work, we show that for object detection on COCO, both anonymizing the dataset by blurring faces, as well as swapping faces in a balanced manner along the gender and skin tone dimension, can retain object detection performances while preserving privacy and partially balancing bias.
SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver CT segmentation

Background and objective : Liver segmentation is an essential prerequisite for liver cancer diagnosis and surgical planning. Traditionally, liver contour is delineated manually by radiologist in a slice-by-slice fashion. However, this process is time-consuming and prone to errors depending on radiologist’s experience. In this paper, a modified U-Net based framework is presented, which leverages techniques from Squeeze-and-Excitation (SE) block, Atrous Spatial Pyramid Pooling (ASPP) and residual learning for accurate and robust liver CT segmentation, and the effectiveness of the proposed method was tested on two public datasets LiTS17 and SLiver07. Methods : A new network architecture called SAR-U-Net was designed, which grounded in the classic U-Net. Firstly, the SE block is introduced to adaptively extract image features after each convolution in the U-Net encoder, while suppressing irrelevant regions, and highlighting features of specific segmentation task; Secondly, ASPP was employed to replace the transition layer and the output layer, and acquire multi-scale image information via different receptive fields. Thirdly, to alleviate the degradation problem, the traditional convolution block was replaced with the residual block and thus prompt the network to gain accuracy from considerably increased depth. Results : In the LiTS17 database experiment, the mean values of Dice , VOE , RVD , ASD and MSD were 95.71±0.55, 9.52±1.11, -0.84±3.86, 1.54±0.30 and 29.14±2.63, respectively.
Generalized Darmois–Israel Junction Conditions

We present a general method to derive the appropriate Darmois–Israel junction conditions for gravitational theories with higher-order derivative terms by integrating the bulk equations of motion across the singular hypersurface. In higher-derivative theories, the field equations can contain terms which are more singular than the Dirac delta distribution. To handle them appropriately, we formulate a regularization procedure based on representing the delta function as the limit of a sequence of classical functions. This procedure involves imposing suitable constraints on the extrinsic curvature such that the field equations are compatible with the singular source being a delta distribution. As explicit examples of our approach, we demonstrate in detail how to obtain the generalized junction conditions for quadratic gravity, F(R) theories, a 4D low-energy effective action in string theory, and action terms that are Euler densities. Our results are novel, and refine the accuracy of previously claimed results in F(R) theories and quadratic gravity. In particular, when the coupling constants of quadratic gravity are those for the Gauss–Bonnet case, our junction conditions reduce to the known ones for the latter obtained independently by boundary variation of a surface term in the action. Finally, we briefly discuss a couple of applications to thin-shell wormholes and stellar models.
Advancing Trajectory Optimization with Approximate Inference: Exploration, Covariance Control and Adaptive Risk

Discrete-time stochastic optimal control remains a challenging problem for general, nonlinear systems under significant uncertainty, with practical solvers typically relying on the certainty equivalence assumption, replanning and/or extensive regularization. Control-as-inference is an approach that frames stochastic control as an equivalent inference problem, and has demonstrated desirable qualities over existing methods, namely in exploration and regularization. We look specifically at the input inference for control (I2C) algorithm, and derive three key characteristics that enable advanced trajectory optimization: An ‘expert’ linear Gaussian controller that combines the benefits of open-loop optima and closed-loop variance reduction when optimizing for nonlinear systems, adaptive risk sensitivity for regularized exploration, and performing covariance control through specifying the terminal state distribution.
Intraclass clustering: an implicit learning ability that regularizes DNNs

Several works have shown that the regularization mechanisms underlying deep neural networks' generalization performances are still poorly understood. In this paper, we hypothesize that deep neural networks are regularized through their ability to extract meaningful clusters among the samples of a class. This constitutes an implicit form of regularization, as no explicit training mechanisms or supervision target such behaviour. To support our hypothesis, we design four different measures of intraclass clustering, based on the neuron- and layer-level representations of the training data. We then show that these measures constitute accurate predictors of generalization performance across variations of a large set of hyperparameters (learning rate, batch size, optimizer, weight decay, dropout rate, data augmentation, network depth and width).
Electronic gas-phase spectrum of the pentaacetylene cation

The origin band of the A(2)Pi(u) - X(2)Pi(g) electronic transition of the linear pentaacetelyne cation, HC10H+, and isotopic derivatives, HC10D+ and DC10D+, has been recorded in the gas phase. The absorption spectrum was observed by cavity ring down spectroscopy through a supersonic planar plasma expansion. The spectrum comprises both spin-orbit components with resolved P- and R-branches. Contour fits allow the determination of ground and excited state spectroscopic constants.
Where is your place, Visual Place Recognition?

Visual Place Recognition (VPR) is often characterized as being able to recognize the same place despite significant changes in appearance and viewpoint. VPR is a key component of Spatial Artificial Intelligence, enabling robotic platforms and intelligent augmentation platforms such as augmented reality devices to perceive and understand the physical world. In this paper, we observe that there are three "drivers" that impose requirements on spatially intelligent agents and thus VPR systems: 1) the particular agent including its sensors and computational resources, 2) the operating environment of this agent, and 3) the specific task that the artificial agent carries out. In this paper, we characterize and survey key works in the VPR area considering those drivers, including their place representation and place matching choices. We also provide a new definition of VPR based on the visual overlap - akin to spatial view cells in the brain - that enables us to find similarities and differences to other research areas in the robotics and computer vision fields. We identify several open challenges and suggest areas that require more in-depth attention in future works.
Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering

Question answering (QA) is an important use case on voice assistants. A popular approach to QA is extractive reading comprehension (RC) which finds an answer span in a text passage. However, extractive answers are often unnatural in a conversational context which results in suboptimal user experience. In this work, we investigate conversational answer generation for QA. We propose AnswerBART, an end-to-end generative RC model which combines answer generation from multiple passages with passage ranking and answerability. Moreover, a hurdle in applying generative RC are hallucinations where the answer is factually inconsistent with the passage text. We leverage recent work from summarization to evaluate factuality. Experiments show that AnswerBART significantly improves over previous best published results on MS MARCO 2.1 NLGEN by 2.5 ROUGE-L and NarrativeQA by 9.4 ROUGE-L.
Carbon-chain molecule survey toward four low-mass molecular outflow sources

We performed a carbon-chain molecule (CCM) survey toward four low-mass outflow sources, IRAS 04181+2655 (I04181), HH211, L1524, and L1598, using the 13.7 m telescope at the Purple Mountain Observatory (PMO) and the 65 m Tian Ma Radio telescope at the Shanghai Observatory. We observed the following hydrocarbons (C2H, C4H, c–C3H2), HC2n+1N (n = 1, 2), CnS (n = 2, 3), and SO, HNC, N2H+. Hydrocarbons and HC3N were detected in all the sources, except for L1598, which had a marginal detection of C4H and a non-detection of HC3N (J = 2–1). HC5N and CCCS were only detected in I04181 and L1524, whereas SO was only detected in HH211. L1598 exhibits the lowest detection rate of CCMs and is generally regarded to be lacking in CCMs source. The ratio of N(HC3N/N(N2H+)) increases with evolution in low-mass star-forming cores. I04181 and L1524 are carbon-chain-rich star-forming cores that may possibly be characterized by warm carbon-chain chemistry. In I04181 and L1524, the abundant CCCS can be explained by shocked carbon-chain chemistry. In HH211, the abundant SO suggests that SO is formed by sublimated S+. In this study, we also mapped HNC, C4H, c–C3H2, and HC3N with data from the PMO. We also find that HNC and NH3 are concentrated in L1524S and L1524N, respectively. Furthermore, we discuss the chemical differences between I04181SE and I04181W. The co-evolution between linear hydrocarbon and cyanopolyynes can be seen in I04181SE.
On Synthetic Absorption Line Profiles of Thermally Driven Winds from Active Galactic Nuclei

The warm absorbers observed in more than half of all nearby active galactic nuclei are tracers of ionized outflows located at parsec-scale distances from the central engine. If the smallest inferred ionization parameters correspond to plasma at a few 104 K, then the gas undergoes a transition from being bound to unbound, provided it is further heated to ∼106 K at larger radii. Dannen et al. recently discovered that, under these circumstances, thermally driven wind solutions are unsteady and even show very dense clumps due to thermal instability. To explore the observational consequences of these new wind solutions, we compute line profiles based on the one-dimensional simulations of Dannen et al. We show how the line profiles from even a simple steady-state wind solution depend on the ionization energy (IE) of absorbing ions, which is a reflection of the wind ionization stratification. To organize the diversity of the line shapes, we group them into four categories: weak Gaussians, saturated boxy profiles with and without an extended blue wing, and broad weak profiles. The lines with profiles in the last two categories are produced by ions with the highest IE that probe the fastest regions. Their maximum blueshifts agree with the highest flow velocities in thermally unstable models, both steady-state and clumpy versions. In contrast, the maximum blueshifts of the highest-IE lines in thermally stable models can be less than half of the actual solution velocities. Clumpy solutions can additionally imprint distinguishable absorption troughs at widely separated velocities.
Dynamic Grasping with a "Soft" Drone: From Theory to Practice

Rigid grippers used in existing aerial manipulators require precise positioning to achieve successful grasps and transmit large contact forces that may destabilize the drone. This limits the speed during grasping and prevents "dynamic grasping", where the drone attempts to grasp an object while moving. On the other hand, biological systems (e.g., birds) rely on compliant and soft parts to dampen contact forces and compensate for grasping inaccuracy, enabling impressive feats.This paper presents the first prototype of a soft drone — a quadrotor where traditional (i.e., rigid) landing gears are replaced with a soft tendon-actuated gripper to enable aggressive grasping. We provide three key contributions. First, we describe our soft drone prototype, including electro-mechanical design, software infrastructure, and fabrication. Second, we review the set of algorithms we use for trajectory optimization and control of the drone and the soft gripper; the algorithms combine state-of-the-art techniques for quadrotor control (i.e., an adaptive geometric controller) with advanced soft robotics models (i.e., a quasi-static finite element model). Finally, we evaluate our soft drone in physics simulations (using SOFA and Unity) and in real tests in a motion-capture room. Our drone is able to dynamically grasp objects of unknown shape where baseline approaches fail. Our physical prototype ensures consistent performance, achieving 91.7% successful grasps across 23 trials. We showcase dynamic grasping results in the video attachment.
Robust 2D/3D Vehicle Parsing in CVIS

We present a novel approach to robustly detect and perceive vehicles in different camera views as part of a cooperative vehicle-infrastructure system (CVIS). Our formulation is designed for arbitrary camera views and makes no assumptions about intrinsic or extrinsic parameters. First, to deal with multi-view data scarcity, we propose a part-assisted novel view synthesis algorithm for data augmentation. We train a part-based texture inpainting network in a self-supervised manner. Then we render the textured model into the background image with the target 6-DoF pose. Second, to handle various camera parameters, we present a new method that produces dense mappings between image pixels and 3D points to perform robust 2D/3D vehicle parsing. Third, we build the first CVIS dataset for benchmarking, which annotates more than 1540 images (14017 instances) from real-world traffic scenarios. We combine these novel algorithms and datasets to develop a robust approach for 2D/3D vehicle parsing for CVIS. In practice, our approach outperforms SOTA methods on 2D detection, instance segmentation, and 6-DoF pose estimation, by 4.5%, 4.3%, and 2.9%, respectively. More details and results are included in the supplement. To facilitate future research, we will release the source code and the dataset on GitHub.
Customized Quantum Annealing Schedules

In a typical quantum annealing protocol, the system starts with a transverse ﬁeld Hamiltonian which is gradually turned oﬀ and replaced by a longitudinal Ising Hamiltonian. The ground state of the Ising Hamiltonian encodes the solution to the computational problem of interest, and the state overlap with this ground state gives the success probability of the annealing protocol. The form of the annealing schedule can have a signiﬁcant impact on the ground state overlap at the end of the anneal, so precise control over these annealing schedules can be a powerful tool for increasing success probabilities of annealing protocols. Here we show how superconducting circuits, in particular capacitively shunted ﬂux qubits (CSFQs), can be used to construct quantum annealing systems by providing tools for mapping circuit ﬂux biases to Pauli coeﬃcients. We use this mapping to ﬁnd customized annealing schedules: appropriate circuit control biases that yield a desired annealing schedule, while accounting for the physical limitations of the circuitry. We then provide examples and proposals that utilize this capability to improve quantum annealing performance.
On hydrodynamic limits of the Vlasov-Navier-Stokes system

We introduce a framework to justify hydrodynamic limits of the Vlasov-Navier-Stokes system. We specifically study high friction regimes, which take into account the fact that particles of the dispersed phase are light (resp. small) compared to the fluid part, and lead to the derivation of Transport-Navier-Stokes (resp. Inhomogeneous Navier-Stokes) systems.
A learning rule balancing energy consumption and information maximization in a feed-forward neuronal network.

Information measures are often used to assess the efficacy of neural networks, and learning rules can be derived through optimization procedures on such measures. In biological neural networks, computation is restricted by the amount of available resources. Considering energy restrictions, it is thus reasonable to balance information processing efficacy with energy consumption. Here, we studied networks of non-linear Hawkes neurons and assessed the information flow through these networks using mutual information. We then applied gradient descent for a combination of mutual information and energetic costs to obtain a learning rule. Through this procedure, we obtained a rule containing a sliding threshold, similar to the Bienenstock-Cooper-Munro rule. The rule contains terms local in time and in space plus one global variable common to the whole network. The rule thus belongs to so-called three-factor rules and the global variable could be related to a number of biological processes. In neural networks using this learning rule, frequent inputs get mapped onto low energy orbits of the network while rare inputs aren't learned.