Miracle-dz/UniFAR-FTUs huggingface dataset:
Dependence of polytetrafluoroethylene reflectance on thickness at visible and ultraviolet wavelengths in air

Polytetrafluoroethylene (PTFE) is an excellent diffuse reflector widely used in light collection systems for particle physics experiments. However, the reflectance of PTFE is a function of its thickness. In this work, we investigate this dependence in air for light of wavelengths 260 nm and 450 nm using two complementary methods. We find that PTFE reflectance for thicknesses from 5 mm to 10 mm ranges from 92.5% to 94.5% at 450 nm, and from 90.0% to 92.0% at 260 nm. We also see that the reflectance of PTFE of a given thickness can vary by as much as 2.7% within the same piece of material. Finally, we show that placing a specular reflector behind the PTFE can recover the loss of reflectance in the visible without introducing a specular component in the reflectance.
Water Deficit Induced Pollen Sterility Associated with a Programmed Cell Death and Oxidative Stress in Rice Anthers

Water Deficit Induced Pollen Sterility Associated with a Programmed Cell Death and Oxidative Stress in Rice Anthers Nguyen Ngoc Giao , Deborah Hailstones, Meredith Wilkes, Bruce Sutton Faculty of Agriculture, Food and Natural Resources, the University of Sydney, NSW 2006, Australia Elizabeth Macarthur Agricultural Institute, NSW Department of Primary Industries, NSW 2570, Australia Corresponding author; email: g.nguyen@usyd.edu. au
BDTF: A Blockchain-Based Data Trading Framework with Trusted Execution Environment

The need for data trading promotes the emergence of data market. However, in conventional data markets, both data buyers and data sellers have to use a centralized trading platform which might be dishonest. A dishonest centralized trading platform may steal and resell the data seller’s data, or may refuse to send data after receiving payment from the data buyer. It seriously affects the fair data transaction and harm the interests of both parties to the transaction. To address this issue, we propose a novel blockchain-based data trading framework with Trusted Execution Environment (TEE) to provide a trusted decentralized platform for fair data trading. In our design, a blockchain network is proposed to realize the payments from data buyers to data sellers, and a trusted exchange is built by using a TEE for the first time to achieve fair data transmission. With these help, data buyers and data sellers can conduct transactions directly. We implement our proposed framework on Ethereum and Intel SGX, security analysis and experimental results have demonstrated that the framework proposed can effectively guarantee the fair completion of data tradings.
Efficient Power-Splitting and Resource Allocation for Cellular V2X Communications

The research efforts on cellular vehicle-to-everything (V2X) communications are gaining momentum with each passing year. It is considered as a paradigm-altering approach to connect a large number of vehicles with minimal cost of deployment and maintenance. This article aims to further push the state-of-the-art of cellular V2X communications by providing an optimization framework for wireless charging, power allocation, and resource block assignment. Specifically, we design a network model where roadside objects use wireless power from RF signals of electric vehicles for charging and information processing. Moreover, due to the resource-constraint nature of cellular V2X, the power allocation and resource block assignment are performed to efficiently use the resources. The proposed optimization framework shows an improvement in terms of the overall energy efficiency of the network when compared with the baseline technique. The performance gains of the proposed solution clearly demonstrate its feasibility and utility for cellular V2X communications.
Recent rigidity results for graphs with prescribed mean curvature

This survey describes some recent rigidity results obtained by the authors for the prescribed mean curvature problem on graphs u : M → $\mathbb{R}$. Emphasis is put on minimal, CMC and capillary graphs, as well as on graphical solitons for the mean curvature flow, in warped product ambient spaces. A detailed analysis of the mean curvature operator is given, focusing on maximum principles at infinity, Liouville properties, gradient estimates. Among the geometric applications, we mention the Bernstein theorem for positive entire minimal graphs on manifolds with non-negative Ricci curvature, and a splitting theorem for capillary graphs over an unbounded domain? M, namely, for CMC graphs satisfying an overdetermined boundary condition.
A Rayleigh-Ritz-method-based approach to computing seismic normal modes in rotating terrestrial planets

A Rayleigh-Ritz-based Continuous Galerkin method based approach is presented to compute the seismic normal modes of rotating terrestrial planets. The method is not restricted to terrestrial planets, only the examples. Special care is taken to separate out the essential spectrum in the presence of a fluid outer core. The relevant elastic-gravitational system of equations, including the Coriolis force, is subjected to a mixed finite-element method, while self-gravitation is accounted for with the fast multipole method. Our discretization utilizes fully unstructured tetrahedral meshes for both solid and fluid regions. The relevant eigenvalue problem is solved by a combination of several highly parallel and computationally efficient methods. We validate our three-dimensional results in the non-rotating case using analytical results for constant elastic balls, as well as numerical results for an isotropic Earth model from standard ``radial" algorithms. We also validate the computations in the rotating case, but only in the slowly-rotating regime where perturbation theory applies, because no other independent algorithms are available in the general case. The algorithm and code are used to compute the point spectra of eigenfrequencies in several Earth and Mars models studying the effects of heterogeneity on a large range of scales.
Two-dimensional Ga$_2$O$_3$ glass: a large scale passivation and protection material for monolayer WS$_2$

Atomically thin transition metal dichalcogenide crystals (TMDCs) have extraordinary optical properties that make them attractive for future optoelectronic applications. Integration of TMDCs into practical all-dielectric heterostructures hinges on the ability to passivate and protect them against necessary fabrication steps on large scales. Despite its limited scalability, encapsulation of TMDCs in hexagonal boron nitride (hBN) currently has no viable alternative for achieving high performance of the final device. Here, we show that the novel, ultrathin Ga$_2$O$_3$ glass is an ideal centimeter-scale coating material that enhances optical performance of the monolayers and protects them against further material deposition. In particular, Ga$_2$O$_3$ capping of commercial grade WS$_2$ monolayers outperforms hBN in both scalability and optical performance at room temperature. These properties make Ga$_2$O$_3$ highly suitable for large scale passivation and protection of monolayer TMDCs in functional heterostructures.
Deep Image Orientation Angle Detection

Estimating and rectifying the orientation angle of any image is a pretty challenging task. Initial work used the hand engineering features for this purpose, where after the invention of deep learning using convolution-based neural network showed significant improvement in this problem. However, this paper shows that the combination of CNN and a custom loss function specially designed for angles lead to a state-of-the-art results. This includes the estimation of the orientation angle of any image or document at any degree (0 to 360 degree),
Characterizing and monitoring student discomfort in upper-division quantum mechanics

We investigate student comfort with the material in an upper-division spins-first quantum mechanics course. Pre-lecture surveys probing students' comfort were administered weekly, in which students assigned themselves a "discomfort level" on a scale of 0--10 and provided a written explanation for their choice. The weekly class-wide average discomfort level was effectively constant over the semester, suggesting that the class found no single unit especially jarring nor especially easy. Student written responses were coded according to their reported source of discomfort---math, math-physics connection, physics, and notation. The relative prevalence of these categories varied significantly over the semester, indicating that students find that different units present different challenges, and also that some of these challenges fade in importance as the semester progresses. Semi-structured interviews with students in a similar quantum mechanics course at a different institution provided additional context and insight into these results.
Learning Accurate and Human-Like Driving using Semantic Maps and Attention

This paper investigates how end-to-end driving models can be improved to drive more accurately and human-like. To tackle the first issue we exploit semantic and visual maps from HERE Technologies and augment the existing Drive360 dataset with such. The maps are used in an attention mechanism that promotes segmentation confidence masks, thus focusing the network on semantic classes in the image that are important for the current driving situation. Human-like driving is achieved using adversarial learning, by not only minimizing the imitation loss with respect to the human driver but by further defining a discriminator, that forces the driving model to produce action sequences that are human-like. Our models are trained and evaluated on the Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world driving data. Extensive experiments show that our driving models are more accurate and behave more human-like than previous methods.
Biorthonormal Formalism for Nonadiabatic Coupled Cluster Dynamics

In coupled cluster theory, the electronic states are biorthonormal in the sense that the left states are orthonormal to the right states. Here, we present an extension of this formalism to a left and right total molecular wave function. Starting from left and right Born–Huang expansions, we derive projected Schrödinger equations for the left and right nuclear wave functions. Observables may be extracted from the resulting wave function pair using standard expressions. The formalism is shown to be invariant under electronic basis transformations, such as normalization of the electronic states. Consequently, the nonadiabatic coupling elements can be expressed with biorthonormal electronic wave functions. Calculating normalization factors that scale as full configuration interaction is not necessary, contrary to claims in the literature. For nonadiabatic nuclear dynamics, we need expressions for the derivative couplings in the biorthonormal formalism. These are derived in a Lagrangian framework.
Gradient Descent over Metagrammars for Syntax-Guided Synthesis

The performance of a syntax-guided synthesis algorithm is highly dependent on the provision of a good syntactic template, or grammar. Provision of such a template is often left to the user to do manually, though in the absence of such a grammar, state-of-the-art solvers will provide their own default grammar, which is dependent on the signature of the target program to be sythesized. In this work, we speculate this default grammar could be improved upon substantially. We build sets of rules, or metagrammars, for constructing grammars, and perform a gradient descent over these metagrammars aiming to find a metagrammar which solves more benchmarks and on average faster. We show the resulting metagrammar enables CVC4 to solve 26% more benchmarks than the default grammar within a 300s time-out, and that metagrammars learnt from tens of benchmarks generalize to performance on 100s of benchmarks.
What If I Don't Like Any Of The Choices? The Limits of Preference Elicitation for Participatory Algorithm Design

Emerging methods for participatory algorithm design have proposed collecting and aggregating individual stakeholder preferences to create algorithmic systems that account for those stakeholders' values. Using algorithmic student assignment as a case study, we argue that optimizing for individual preference satisfaction in the distribution of limited resources may actually inhibit progress towards social and distributive justice. Individual preferences can be a useful signal but should be expanded to support more expressive and inclusive forms of democratic participation.
Feedback Clustering for Online Travel Agencies Searches: a Case Study

Understanding choices performed by online customers is a growing need in the travel industry. In many practical situations, the only available information is the flight search query performed by the customer with no additional profile knowledge. In general, customer flight bookings are driven by prices, duration, number of connections, and so on. However, not all customers might assign the same importance to each of those criteria. Here comes the need of grouping together all flight searches performed by the same kind of customer, that is having the same booking criteria. The effectiveness of some set of recommendations, for a single cluster, can be measured in terms of the number of bookings historically performed. This effectiveness measure plays the role of a feedback, that is an external knowledge which can be recombined to iteratively obtain a final segmentation. In this paper, we describe our Online Travel Agencies (OTA) flight search use case and highlight its specific features. We address the flight search segmentation problem motivated above by proposing a novel algorithm called Split-or-Merge (S/M). This algorithm is a variation of the Split-Merge-Evolve (SME) method. The SME method has already been introduced in the community as an iterative process updating a clustering given by the K-means algorithm by splitting and merging clusters subject to feedback independent evaluations. No previous application of the SME method to the real-word data is reported in literature to the best of our knowledge. Here, we provide experimental evaluations over real-world data to the SME and the S/M methods. The impact on our domain-specific metrics obtained under the SME and the S/M methods suggests that feedback clustering techniques can be very promising in the handling of the domain of OTA flight searches.
Analyzing and Mitigating Data Stalls in DNN Training

Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While prior research has explored many different ways of reducing DNN training time, the impact of input data pipeline , i.e., fetching raw data items from storage and performing data pre-processing in memory, has been relatively unexplored. This paper makes the following contributions: (1) We present the first comprehensive analysis of how the input data pipeline affects the training time of widely-used computer vision and audio Deep Neural Networks (DNNs), that typically involve complex data pre-processing. We analyze nine different models across three tasks and four datasets while varying factors such as the amount of memory, number of CPU threads, storage device, GPU generation etc on servers that are a part of a large production cluster at Microsoft. We find that in many cases, DNN training time is dominated by data stall time : time spent waiting for data to be fetched and pre-processed. (2) We build a tool, DS-Analyzer to precisely measure data stalls using a differential technique, and perform predictive what-if analysis on data stalls. (3) Finally, based on the insights from our analysis, we design and implement three simple but effective techniques in a data-loading library, CoorDL, to mitigate data stalls. Our experiments on a range of DNN tasks, models, datasets, and hardware configs show that when PyTorch uses CoorDL instead of the state-of-the-art DALI data loading library, DNN training time is reduced significantly (by as much as 5X on a single server).
Measurements of gross and net ecosystem productivity and water vapour exchange of a Pinus ponderosa ecosystem, and an evaluation of two generalized models

Net ecosystem productivity (NEP), net primary productivity (NPP), and water vapour exchange of a mature Pinus ponderosa forest (44°30′ N, 121°37′ W) growing in a region subject to summer drought were investigated along with canopy assimilation and respiratory fluxes. This paper describes seasonal and annual variation in these factors, and the evaluation of two generalized models of carbon and water balance (PnET‐II and 3‐PG) with a combination of traditional measurements of NPP, respiration and water stress, and eddy covariance measurements of above‐and below‐canopy CO2 and water vapour exchange. The objective was to evaluate the models using two years of traditional and eddy covariance measurements, and to use the models to help interpret the relative importance of processes controlling carbon and water vapour exchange in a water‐limited pine ecosystem throughout the year. PnET‐II is a monthly time‐step model that is driven by nitrogen availability through foliar N concentration, and 3‐PG is a monthly time‐step quantum‐efficiency model constrained by extreme temperatures, drought, and vapour pressure deficits. Both models require few parameters and have the potential to be applied at the watershed to regional scale. There was 2/3 less rainfall in 1997 than in 1996, providing a challenge to modelling the water balance, and consequently the carbon balance, when driving the models with the two years of climate data, sequentially. Soil fertility was not a key factor in modelling processes at this site because other environmental factors limited photosynthesis and restricted projected leaf area index to ∼1.6. Seasonally, GEP and LE were overestimated in early summer and underestimated through the rest of the year. The model predictions of annual GEP, NEP and water vapour exchange were within 1–39% of flux measurements, with greater disparity in 1997 because soil water never fully recharged. The results suggest that generalized models can provide insights to constraints on productivity on an annual basis, using a minimum of site data.
Dynamics of B-cell repertoires and emergence of cross-reactive responses in COVID-19 patients with different disease severity

COVID-19 patients show varying severity of the disease ranging from asymptomatic to requiring intensive care. Although a number of monoclonal antibodies against SARS-CoV-2 have been identified, we still lack an understanding of the overall landscape of B-cell receptor (BCR) repertoires in COVID-19 patients. Here, we used high-throughput sequencing of BCR repertoires collected over multiple time points during an infection to characterize statistical and dynamical signatures of the B-cell response to SARS-CoV-2 in 19 patients with different disease severities. Based on principled statistical approaches, we determined differential sequence features of BCRs associated with different disease severity. We identified 34 significantly expanded rare clonal lineages shared among patients as candidates for a specific response to SARS-CoV-2. Moreover, we identified natural emergence of a BCR with cross-reactivity to SARS-CoV and SARS-CoV-2 in a number of patients. Overall, our results provide important insights for development of rational therapies and vaccines against COVID-19.
Calling Out Bluff: Attacking the Robustness of Automatic Scoring Systems with Simple Adversarial Testing

A significant progress has been made in deep-learning based Automatic Essay Scoring (AES) systems in the past two decades. The performance commonly measured by the standard performance metrics like Quadratic Weighted Kappa (QWK), and accuracy points to the same. However, testing on common-sense adversarial examples of these AES systems reveal their lack of natural language understanding capability. Inspired by common student behaviour during examinations, we propose a task agnostic adversarial evaluation scheme for AES systems to test their natural language understanding capabilities and overall robustness.
Microscopic dynamics of the evacuation phenomena

We studied the room evacuation problem within the context of the Social Force Model. We focused on a system of 225 pedestrians escaping from a room in different anxiety levels, and analyzed the clogging delays as the relevant magnitude responsible for the evacuation performance. We linked the delays with the clusterization phenomenon along the "faster is slower" and the "faster is faster" regimes. We will show that the "faster is faster" regime is characterized by the presence of a giant cluster structure (composed by more than 15 pedestrians), although no long lasting delays appear within this regime. For this system, we found that the relevant structures in the "faster is slower" regime are those blocking clusters that are somehow attached to the two walls defining the exit. At very low desired velocities, very small structures become relevant (composed by less than 5 pedestrians), but at intermediate velocities (vd = 3 m/s) the pedestrians involved in the blockings increases (not exceeding 15 pedestrians).
DETCID: Detection of Elongated Touching Cells with Inhomogeneous Illumination using a Deep Adversarial Network

Clostridioides difficile infection (C. diff) is the most common cause of death due to secondary infection in hospital patients in the United States. Detection of C. diff cells in scanning electron microscopy (SEM) images is an important task to quantify the efficacy of the under-development treatments. However, detecting C. diff cells in SEM images is a challenging problem due to the presence of inhomogeneous illumination and occlusion. An Illumination normalization pre-processing step destroys the texture and adds noise to the image. Furthermore, cells are often clustered together resulting in touching cells and occlusion. In this paper, DETCID, a deep cell detection method using adversarial training, specifically robust to inhomogeneous illumination and occlusion, is proposed. An adversarial network is developed to provide region proposals and pass the proposals to a feature extraction network. Furthermore, a modified IoU metric is developed to allow the detection of touching cells in various orientations. The results indicate that DETCID outperforms the state-of-the-art in detection of touching cells in SEM images by at least 20 percent improvement of mean average precision.
From Symmetry to Geometry: Tractable Nonconvex Problems

As science and engineering have become increasingly data-driven, the role of optimization has expanded to touch almost every stage of the data analysis pipeline, from the signal and data acquisition to modeling and prediction. The optimization problems encountered in practice are often nonconvex. While challenges vary from problem to problem, one common source of nonconvexity is nonlinearity in the data or measurement model. Nonlinear models often exhibit symmetries, creating complicated, nonconvex objective landscapes, with multiple equivalent solutions. Nevertheless, simple methods (e.g., gradient descent) often perform surprisingly well in practice. The goal of this survey is to highlight a class of tractable nonconvex problems, which can be understood through the lens of symmetries. These problems exhibit a characteristic geometric structure: local minimizers are symmetric copies of a single ``ground truth'' solution, while other critical points occur at balanced superpositions of symmetric copies of the ground truth, and exhibit negative curvature in directions that break the symmetry. This structure enables efficient methods to obtain global minimizers. We discuss examples of this phenomenon arising from a wide range of problems in imaging, signal processing, and data analysis. We highlight the key role of symmetry in shaping the objective landscape and discuss the different roles of rotational and discrete symmetries. This area is rich with observed phenomena and open problems; we close by highlighting directions for future research.