Miracle-dz/UniFAR-FTUs huggingface dataset:
Electronic width of the 
ψ(3770)
 resonance interfering with the background

Methods for extracting the $\psi(3770)\to e^+e^-$ decay width from the data on the reaction cross section $e^+e^-\to D\bar D$ are discussed. Attention is drawn to the absence of the generally accepted method for determining $\Gamma_{\psi(3770)e^+e^-}$ in the presence of interference between the contributions of the $\psi(3770)$ resonance and background. It is shown that the model for the experimentally measured $D$ meson form factor, which satisfies the requirement of the Watson theorem and takes into account the contribution of the complex of the mixed $\psi(3770)$ and $\psi(2S)$ resonances, allows uniquely determine the value of $\Gamma_{\psi(3770)e^+e^-}$ by fitting. The $\Gamma_{\psi(3770)e^+ e^-}$ values found from the data processing are compared with the estimates in the potential models.
Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

This paper presents a novel unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latency-critical computation-intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). To significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of line-of-sight (LoS) massive multiple-input–multiple-output (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large-scale antennas. A three-dimensional (3-D) geometrical representation of the MEC-enabled network is introduced and an optimization method is proposed that minimizes the computation-based and communication-based weighted total energy consumption (WTEC) of vehicles and ARSU subject to transmit power allocation, task allocation, and time slot scheduling. The results verify the theoretical derivations, emphasize on the effectiveness of the LoS massive MIMO transmission, and provide useful engineering insights.
Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues

Today’s state-of-the-art methods for 3D object detection are based on lidar, stereo, or monocular cameras. Lidar-based methods achieve the best accuracy, but have a large footprint, high cost, and mechanically-limited angular sampling rates, resulting in low spatial resolution at long ranges. Recent approaches using low-cost monocular or stereo cameras promise to overcome these limitations but struggle in low-light or low-contrast regions as they rely on passive CMOS sensors. We propose a novel 3D object detection modality that exploits temporal illumination cues from a low-cost monocular gated imager. We introduce a novel deep detection architecture, Gated3D, that is tailored to temporal illumination cues in gated images. This modality allows us to exploit mature 2D object feature extractors that guide the 3D predictions through a frustum segment estimation. We assess the proposed method experimentally on a 3D detection dataset that includes gated images captured over 10,000 km of driving data. We validate that our method outperforms state-of-the-art monocular and stereo methods, opening up a new sensor modality as an avenue to replace lidar in autonomous driving. https://light.princeton. edu/gated3d
Object Removal Attacks on LiDAR-based 3D Object Detectors

LiDARs play a critical role in Autonomous Vehicles' (AVs) perception and their safe operations. Recent works have demonstrated that it is possible to spoof LiDAR return signals to elicit fake objects. In this work we demonstrate how the same physical capabilities can be used to mount a new, even more dangerous class of attacks, namely Object Removal Attacks (ORAs). ORAs aim to force 3D object detectors to fail. We leverage the default setting of LiDARs that record a single return signal per direction to perturb point clouds in the region of interest (RoI) of 3D objects. By injecting illegitimate points behind the target object, we effectively shift points away from the target objects' RoIs. Our initial results using a simple random point selection strategy show that the attack is effective in degrading the performance of commonly used 3D object detection models.
In-Order Chart-Based Constituent Parsing

We propose a novel in-order chart-based model for constituent parsing. Compared with previous CKY-style and top-down models, our model gains advantages from in-order traversal of a tree (rich features, lookahead information and high efficiency) and makes a better use of structural knowledge by encoding the history of decisions. Experiments on the Penn Treebank show that our model outperforms previous chart-based models and achieves competitive performance compared with other discriminative single models.
HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data

Clinical trials are crucial for drug development but are time consuming, expensive, and often burdensome on patients. More importantly, clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. If we were better at predicting the results of clinical trials, we could avoid having to run trials that will inevitably fail — more resources could be devoted to trials that are likely to succeed. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions for all diseases based on a comprehensive and diverse set of web data including molecule information of the drugs, target disease information, trial protocol and biomedical knowledge. HINT first encode these multi-modal data into latent embeddings, where an imputation module is designed to handle missing data. Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web. Then the interaction graph module will connect all the embeddings via domain knowledge to fully capture various trial components and their complex relations as well as their influences on trial outcomes. Finally, HINT learns a dynamic attentive graph neural network to predict trial outcome. Comprehensive experimental results show that HINT achieves strong predictive performance, obtaining 0.772, 0.607, 0.623, 0.703 on PR-AUC for Phase I, II, III, and indication outcome prediction, respectively. It also consistently outperforms the best baseline method by up to 12.4% on PR-AUC.
A Deep Study of an Intermediate-age Open Cluster SAI 35 (Juchert 20) Using Ground-based Imaging and Gaia EDR3 Astrometry

We present a CCD UBVI photometric study of poorly studied intermediate-age open cluster SAI 35 (Juchert 20) for the first time. To accomplish this study, we also used LAMOST DR5, Two Micron All Sky Survey, and Gaia EDR3 databases. We identified 214 most probable cluster members with membership probability higher than 50%. The mean proper motion of the cluster is found as and μ δ = −1.66 ± 0.01 mas yr−1. We find the normal interstellar extinction law using the various two-color diagrams. The age, distance, reddening, and radial velocity of the cluster are estimated to be 360 ± 40 Myr, 2.9 ± 0.15 kpc, 0.72 ± 0.05 mag, and −91.62 ± 6.39 km s−1, respectively. The overall mass function slope for main-sequence stars is found to be 1.49 ± 0.16 within the mass range 1.1–3.1 M ⊙, which is in agreement with Salpeter’s value within uncertainty. The present study demonstrates that SAI 35 is dynamically relaxed. Galactic orbital parameters are determined using Galactic potential models. We found that this object follows a circular path around the Galactic center.
Condensation of Cooper triples

The condensation of Cooper pairs, originating from the Fermi-surface instability due to a weakly attractive interaction between two fermions, opened a new frontier for exploring many-body physics in interdisciplinary contexts. In this work, we discuss the possible condensation of Cooper triples, which are three-body counterparts of Cooper pairs for three-component fermions with a three-body attraction. Although each composite trimer-like state obeys the Fermi-Dirac statistics, its aggregate can form a condensate at zero center-of-mass momentum in the presence of the internal degrees of freedom associated with the relative momenta of constituent particles of momenta close to the Fermi surface. Such condensation can be regarded as bosonization in infinite-component fermions. We propose a variational wave function for the condensate of Cooper triples in analogy with the Bardeen-Cooper-Schrieffer ground state and obtain the ground-state energy.
Understanding the Interaction of Adversarial Training with Noisy Labels

Noisy labels (NL) and adversarial examples both undermine trained models, but interestingly they have hitherto been studied independently. A recent adversarial training (AT) study showed that the number of projected gradient descent (PGD) steps to successfully attack a point (i.e., find an adversarial example in its proximity) is an effective measure of the robustness of this point. Given that natural data are clean, this measure reveals an intrinsic geometric property -- how far a point is from its class boundary. Based on this breakthrough, in this paper, we figure out how AT would interact with NL. Firstly, we find if a point is too close to its noisy-class boundary (e.g., one step is enough to attack it), this point is likely to be mislabeled, which suggests to adopt the number of PGD steps as a new criterion for sample selection for correcting NL. Secondly, we confirm AT with strong smoothing effects suffers less from NL (without NL corrections) than standard training (ST), which suggests AT itself is an NL correction. Hence, AT with NL is helpful for improving even the natural accuracy, which again illustrates the superiority of AT as a general-purpose robust learning criterion.
Long-term evolution of three light bridges developed on the same sunspot

One important feature of sunspots is the presence of light bridges. These structures are elongated and bright (as compared to the umbra) features that seem to be related to the formation and evolution of sunspots. In this work, we studied the long-term evolution and the stratification of different atmospheric parameters of three light bridges formed in the same host sunspot by different mechanisms. To accomplish this, we used data taken with the GREGOR Infrared Spectrograph installed at the GREGOR telescope. These data were inverted to infer the physical parameters of the atmosphere where the observed spectral profiles were formed of the three light bridges. We find that, in general, the behaviour of the three light bridges is typical of this kind of structure with the magnetic field strength, inclination, and temperature values between the values at the umbra and the penumbra. We also find that they are of a significantly non-magnetic character (particularly at the axis of the light bridges) as it is deduced from the filling factor. In addition, within the common behaviour of the physical properties of light bridges, we observe that each one exhibits a particular behaviour. Another interesting result is that the light bridge cools down, the magnetic field decreases, and the magnetic field lines get more inclined higher in the atmosphere. Finally, we studied the magnetic and non-magnetic line-of-sight velocities of the light bridges. The former shows that the magnetic component is at rest and, interestingly, its variation with optical depth shows a bi-modal behaviour. For the line-of-sight velocity of the non-magnetic component, we see that the core of the light bridge is at rest or with shallow upflows and clear downflows sinking through the edges.
A 4% Efficient Organic Solar Cell Using a Fluorinated Fused Subphthalocyanine Dimer as an Electron Acceptor

the bulk heterojunction, [ 3 ] and the tandem structure. [ 4 ] Further effi ciency enhancements have primarily been related to the development of improved donor materials [ 5 ] that absorb a wider range of the solar irradiance spectrum with energy levels that enable a high open-circuit voltage ( V oc ). In contrast to the wide variety of donor materials, the preferred acceptor material in these cells is almost invariably among the family of fullerene molecules. [ 6 ] Their success is related to their high electron mobility and high exciton diffusion length, as well as to the fact that exciton dissociation at donor:fullerene interfaces is highly effi cient and ultra-fast. However, owing to the fact that fullerenes absorb strongly only at wavelengths below λ = 560 nm, their overall contribution to the photocurrent is usually limited. Furthermore, only rarely do nonfullerene acceptors show improved performance [ 6 , 7 ] since alternatives such as perylenes, [ 2 ] CdSe, [ 8 ] ZnO, [ 9 ] and TiO 2 , [ 10 ]
The slope of fibred surfaces: Unitary rank and Clifford index

We prove new slope inequalities for relatively minimal fibred surfaces, showing an influence of the relative irregularity qf$q_f$ , of the unitary rank uf$u_f$ and of the Clifford index cf$c_f$ on the slope. The argument uses Xiao's method and a new Clifford‐type inequality for subcanonical systems on non‐hyperelliptic curves.
Self-heating and drying in two-dimensional bagasse piles

This paper describes a two-dimensional model for self-heating and changes in water levels in bagasse piles of constant rectangular or triangular cross section. (Bagasse is the residue, mainly cellulose, that remains after sugar has been extracted from sugar-cane.) After milling, the bagasse has almost 50% water by weight, as hot water is used to remove the last of the sugar. The bagasse can be used as fuel in electrical power stations, but needs to be dried out before use. This paper discusses the way in which the drying out of a pile depends on the ambient conditions, and the shape and size of the pile. Accordingly, the energy equation, and equations for liquid water, water vapour and oxygen are solved numerically using the method of lines. The equations include terms describing heat conduction, diffusion of water vapour and oxygen, condensation and evaporation and an Arrhenius self-heating term. In addition, recent measurements show that there is also self-heating due to the presence of water in the bagasse, with a maximum effect near 60 °C, which is modelled by a modified Arrhenius expression. The local maximum in the heat release curve for the problem leads to approximate steady-state behaviour on short time scales that eventually is lost as the pile dries out. This interesting physical behaviour motivates an approximate analytical model for the rate at which liquid water is reduced in the pile. Analytical and numerical results are presented for a variety of pile configurations and some fairly general conclusions are drawn.
Computational Fluid Dynamics Study of Taylor Flow in Microreactors:Investigating the Effect of Surface Tension and Contact Angle on the Heatand Mass Transfer

Microfluidics technology offers high efficiency of heat and mass transfer and low safety hazards compared to conventional multiphase processes. The multiphase flow in the microchannels is usually characterized as Taylor flow that includes elongated microbubbles , separated by liquid slugs. In the current study, we employ OpenFOAM CFD package to investigate the effect of the surface tension and the contact angle on the interfacial surface area and transport yield in microscale systems. The results show that contact angle can significantly affect the surface area and the bubble size while the surface tension does not change these parameters in the system. Moreover, in lower contact angle, the flow may turn into bubbly flow, affecting KL, mass transfer coefficient in the liquid phase. Findings of the current study can improve the mass transfer coefficient in microreactors while avoiding thermal runaways, hot spots and other safety issues of conventional reactors.
Transverse thermoelectric generation using magnetic materials

The transverse thermoelectric effect refers to the conversion of a temperature gradient into a transverse charge current, or vice versa, which appears in a conductor under a magnetic field or in a magnetic material with spontaneous magnetization. Among such phenomena, the anomalous Nernst effect in magnetic materials has been receiving increasing attention from the viewpoints of fundamental physics and thermoelectric applications owing to the rapid development of spin caloritronics and topological materials science. In this research trend, a conceptually different transverse thermoelectric conversion phenomenon appearing in thermoelectric/magnetic hybrid materials has been demonstrated, enabling the generation of a large transverse thermopower. Here, we review the recent progress in fundamental and applied studies on the transverse thermoelectric generation using magnetic materials. We anticipate that this perspective will further stimulate research activities on the transverse thermoelectric generation and lead to the development of next-generation thermal energy harvesting and heat-flux sensing technologies.
CMS-LSTM: Context-Embedding and Multi-Scale Spatiotemporal-Expression LSTM for Video Prediction

Extracting variation and spatiotemporal features via limited frames remains as an unsolved and challenging problem in video prediction. Inherent uncertainty among consecutive frames exacerbates the difﬁculty in long-term prediction. To tackle the problem, we focus on capturing context correlations and multi-scale spatiotemporal ﬂows, then propose CMS-LSTM by integrating two effective and lightweight blocks, namely Context-Embedding (CE) and Spatiotemporal-Expression (SE) block, into ConvLSTM backbone. CE block is designed for abundant context interactions, while SE block focuses on multi-scale spatiotemporal expression in hidden states. The newly introduced blocks also facilitate other spatiotemporal models (e.g., PredRNN, SA-ConvLSTM) to produce representative implicit features for video prediction. Qualitative and quantitative experiments demonstrate the effectiveness and ﬂexibil-ity of our proposed method. We use fewer parameters to reach markedly state-of-the-art results on Moving MNIST and TaxiBJ datasets in numbers of metrics. All source code is available at https://github.com/czh-98/CMS-LSTM.
Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise

The empirical success of deep learning is often attributed to SGD's mysterious ability to avoid sharp local minima in the loss landscape, as sharp minima are known to lead to poor generalization. Recently, empirical evidence of heavy-tailed gradient noise was reported in many deep learning tasks, and it was shown in \c{S}im\c{s}ekli (2019a,b) that SGD can escape sharp local minima under the presence of such heavy-tailed gradient noise, providing a partial solution to the mystery. In this work, we analyze a popular variant of SGD where gradients are truncated above a fixed threshold. We show that it achieves a stronger notion of avoiding sharp minima: it can effectively eliminate sharp local minima entirely from its training trajectory. We characterize the dynamics of truncated SGD driven by heavy-tailed noises. First, we show that the truncation threshold and width of the attraction field dictate the order of the first exit time from the associated local minimum. Moreover, when the objective function satisfies appropriate structural conditions, we prove that as the learning rate decreases, the dynamics of heavy-tailed truncated SGD closely resemble those of a continuous-time Markov chain that never visits any sharp minima. Real data experiments on deep learning confirm our theoretical prediction that heavy-tailed SGD with gradient clipping finds a"flatter"local minima and achieves better generalization.
Localizing Unsynchronized Sensors With Unknown Sources

We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known. Unlike previous work, our method directly recovers the array geometry, instead of first estimating the timing information. The key component is a new loss function which is insensitive to the unknown timings. We cast the problem as a minimization of a non-convex functional of the Euclidean distance matrix of microphones and sources subject to certain non-convex constraints. After convexification, we obtain a semidefinite relaxation which gives an approximate solution; subsequent refinement on the proposed loss via the Levenberg-Marquardt scheme gives the final locations. Our method achieves state-of-the-art performance in terms of reconstruction accuracy, speed, and the ability to work with a small number of sources and receivers. It can also handle missing measurements and exploit prior geometric and temporal knowledge, for example if either the receiver offsets or the emission times are known, or if the array contains compact subarrays with known geometry.
Improving Artificial Teachers by Considering How People Learn and Forget

The paper presents a novel model-based method for intelligent tutoring, with particular emphasis on the problem of selecting teaching interventions in interaction with humans. Whereas previous work has focused on either personalization of teaching or optimization of teaching intervention sequences, the proposed individualized model-based planning approach represents convergence of these two lines of research. Model-based planning picks the best interventions via interactive learning of a user memory model’s parameters. The approach is novel in its use of a cognitive model that can account for several key individual- and material-specific characteristics related to recall/forgetting, along with a planning technique that considers users’ practice schedules. Taking a rule-based approach as a baseline, the authors evaluated the method’s benefits in a controlled study of artificial teaching in second-language vocabulary learning (N = 53).
Electrochemical Kinetics of CuCl(aq)/HCl(aq) Electrolyzer for Hydrogen Production via a Cu-Cl Thermochemical Cycle

AnelectrochemicalkineticsinvestigationoftheCuCl(aq)/HCl(aq)electrolyzeridentiﬁedmethodstosigniﬁcantlyreducetheplatinumloadingsrequiredtoachieveahighcellcurrentdensityof0.5A/cm 2 at0.7V. AstheCuCl(aq)/HCl(aq)electrolyzerisakeycomponent oftheCu-Clthermochemicalcycle,theeconomicviabilityoftheCu-Clthermochemicalcyclewassigniﬁcantlyimprovedbyreducingtheloadingrequiredtoachieve0.5A/cm 2 . Electrochemical impedance spectroscopy (EIS) and linear sweep voltammetry (LSV) with a rotating disc electrode were employed to investigate the kinetics of the aqueous Cu II /Cu I chloride complexes reaction on platinum and glassy carbon using a three-electrode cell. It was found that the standard exchange current density of the anodic Cu II /Cu I electrochemical reaction on platinum, 4–12 A/cm 2 , was signiﬁcantly larger than the values reported for the HER cathodic reaction thus far. In addition, SEM was used to observe the effectiveness of different catalyst application techniques. Through SEM observations, and electrochemical data analysis, the amount of platinum used in a laboratory scale CuCl(aq)/HCl(aq) electrolyzer was reduced from 0.8 mg/cm 2 applied to both electrodes to 0.4 mg/cm 2 on the cathode and zero at the anode while still maintaining a current density of 0.5 A/cm