Miracle-dz/UniFAR-FTUs huggingface dataset:
Red-emission analysis, Judd–Ofelt intensity parameters and laser properties of CdMgZnO:xEu3+ nanocrystals: the effects of Eu3+ concentration

In this paper, we discuss a strong red Eu3+-concentration-dependent emission line associated with an electric dipole transition (5D0 → 7F2) at 616 nm in CdMgZnO:xEu3+ nanocrystals made via a sol–gel synthesis route. Oxygen interstitial defects created by the presence of a mixed-valence state of Eu 3+ and Eu2+ ions as rare-earth cations lead to a structural distortion which is confirmed by x-ray photon electron spectroscopy. The Judd–Ofelt intensity parameters, Ω2 and Ω4, obtained from the emission spectra, show a trend for Ω2 > Ω4 that translates to stronger covalence and a stable local structure surrounding the Eu3+ ions in CdMgZnO nanocrystals. The asymmetry ratio (R o), radiative lifetime ( τrad ), branching ratio β , stimulated emission cross-section, δe and the gain bandwidth (δ e× Δλ eff) for 5D0 are computed and analysed. A radiative lifetime ranging from 789 to 984 µs points to a suitable material for orange-red fluorescent phosphors for lighting and display technologies. The gain bandwidth (δ e× Δλ eff), which is dependent on the Eu3+ concentration, suggests the use of the CdMgZnO:xEu3+ nanocrystal material in fibre amplifiers, while the sample with x = 1.5%, which yields the largest emission cross-section (15.79 × 10−22 cm2), the highest gain bandwidth (28.8 × 10−28 cm3), and a high branching ratio (71%), is the best candidate for red laser applications.
Models for the PET quantitative imaging of the adenosine receptor radioligand [11C] SCH442416 in humans

Positron Emission Tomography is an important imaging technique, that enables regional function to be assayed in a fully quantitative and noninvasive manner. It is based on the use of radioactive tracers: [11C]SCH442416, which is the compound of interest in our work, seems to be the first tracer suitable for in vivo imaging of adenosine A2A receptors and it seems to be able to diagnose the Parkinson’s disease. Starting from the results of a general approach, known in PET area as “Spectral Analysis”, we proposed the best compartmental model to describe the kinetic of the tracer inside the brain. Moreover we estimated some parameters of interest, even if the small number of subjects involved in the study do not allow many comparisons between the different patients
Can Adolescents Acquire Cultural Capital Through Social Capital? The Impact of Ties to College-Educated Adults

Scholars debate whether cultural capital reproduces existing inequalities or provides a path to upward mobility. Most studies, however, focus only on cross-sectional associations and are unclear about how disadvantaged adolescents can increase their amounts of cultural capital. Adolescents may be able to increase cultural capital through ties to adults with high educational attainment. We investigate this topic using experimental longitudinal data on mentoring relationships. We find that mentors with a college degree or greater have positive effects on cultural capital, but primarily for adolescents with a parent with some college or greater. Thus, cultural capital may not be an engine of social mobility if adolescents from low-SES households cannot obtain or increase their cultural capital.
Estimating and Testing Skewness in a Stochastic Volatility Model

In this paper we propose a novel approach to estimating and testing skewness in a stochastic volatility (SV) model. Our key idea is to replace a normal return error in the standard SV model with a split normal error. We show that this simple variation in the model brings about two large computational advantages. First, the SV can be simulated fast and efficiently using a one-block Gibbs sampling technique. Second, more importantly, this is the first to provide a marginal likelihood calculation method to formally test the skewness and SV in a Bayesian framework. We subsequently demonstrate the efficiency and reliability of our posterior sampling and model comparison methods through a simulation study. The simulation study results also show that neglecting skewness leads to inaccurate SV estimates and conditional expected returns. Our empirical applications to daily stock return data also show strong evidence of negative skewness.
On existence of private unemployment insurance with advance information on future job losses

We study the existence of a profitable unemployment insurance market in a dynamic economy with adverse selection rooting in information on future job losses. The new feature of the model is that the insurer and workers interact repeatedly. Repeated interactions make it possible to threaten workers with exclusion from future insurance benefits after a default on insurance premia. With exclusion, not only the insurance against the fundamental risk, but also against future bad news about job losses matters. In contrast to conventional wisdom, we find that private unemployment insurance in the US can be profitable for a relatively short exclusion length of one year. To stimulate the emergence of a private unemployment insurance market, policy makers can facilitate the creation of a registry that archives past defaults on insurance premia. <br>
The Nonstationary Newsvendor: Data-Driven Nonparametric Learning

We study a newsvendor problem with unknown demand distribution in a nonstationary demand environment over a multi-period time horizon. The demand in each period consists of a time-varying demand level and an additive random shock. Neither the demand level nor the random shock is separately observable. The amount of change in the demand level over the time horizon is measured by a cumulative variation metric. The problem has widespread applications, such as perishable inventory planning, staffing, and medical resource capacity planning in the wake of COVID-19. We design a nonparametric dynamic ordering policy, termed the moving window ordering policy, that tracks the shifts in the unknown demand level while accounting for the unobservable random demand shocks. To compute the order quantity in each period, this policy only needs the past demand observations, without any access to the underlying demand distribution. For a finite variation "budget," we prove that our ordering policy is first-order optimal in the sense that its regret grows at the smallest possible rate. We also extend our analysis to the case of asymptotically large variation budgets. Through case studies based on real-life data, we show that our policy can save 20-80% of overage and underage costs, relative to policies widely used for perishable inventory replenishment and nurse staffing.
The Collusive Efficacy of Competition Clauses in Bertrand Markets with Capacity-Constrained Retailers

We study the collusive efficacy of competition clauses (CC) such as the meeting competition clause (MCC) and the beating competition clauses (BCC) in a general framework. In contrast to previous theoretical studies, we allow for repeated interaction among the retailers and heterogeneity in their sales capacities. Besides that, the selection of the form of the CC is endogeneized. The retailers choose among a wide range of CC types - including the conventional ones such as the MCC and the BCCs with lump sum refunds. Several common statements about the collusive (in)efficacy of CCs cannot be upheld in our framework. We show that in the absence of hassle costs, MCCs might induce collusion in homogeneous markets even if they are adopted only by few retailers. If hassle and implementation costs are mild, collusion can be enforced by BCCs with lump sum refunds. Remarkably, these findings hold for any reasonable rationing rule. However, a complete specification of all collusive CCs is in general impossible without any further reference to the underlying rationing rule.
How Effective Is Thermal Remediation of DNAPL Source Zones in Reducing Groundwater Concentrations?

Dense nonaqueous phase liquid (DNAPL) source areas containing chlorinated volatile organic compounds (cVOCs) such as trichloroethene (TCE) and perchloroethene (PCE) often give rise to significant dissolved plumes in groundwater, leading to the closure of downgradient water supply wells and creating vapor intrusion issues in buildings located above the plume. Hydraulic containment via pump‐and‐treat has often been implemented to limit migration but must continue indefinitely. Removal of the DNAPL source area by means such as in situ thermal remediation (ISTR) offers the potential to diminish or end the need for hydraulic containment if the associated dissolved plume attenuates sufficiently following source removal. A question often raised is whether this occurs or whether the back diffusion of contaminants from secondary sources such as low‐permeability lenses in the dissolved plume precludes it. The authors conducted DNAPL source removal using ISTR at dozens of sites. This paper presents a compilation of cases—10 separate DNAPL source areas at five project sites—where data indicate that the implementation of a thorough ISTR in a DNAPL source area can result in the attenuation of the associated dissolved plume, such that in several cases, long‐standing pump‐and‐treat systems could be turned off. Our findings contrast with recent assertions that aggressive source remediation may not be justifiable because dissolved plume concentrations will not decline sufficiently. We show that the application of ISTR can result in the thorough removal of the DNAPL source, effective diminution of dissolved plume groundwater concentrations, and achievement of drinking water standards.
Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows

We propose a supervised-machine-learning-based wall model for coarse-grid wall-resolved large-eddy simulation (LES). Our consideration is made on LES of turbulent channel flows with a first grid point set relatively far from the wall ($\sim$ 10 wall units), while still resolving the near-wall region, to present a new path to save the computational cost. Convolutional neural network (CNN) is utilized to estimate a virtual wall-surface velocity from $x-z$ sectional fields near the wall, whose training data are generated by a direct numerical simulation (DNS) at ${\rm Re}_{\tau}=180$. The virtual wall-surface velocity is prepared with the extrapolation of the DNS data near the wall. This idea enables us to give a proper wall condition to correct a velocity gradient near the wall. The estimation ability of the model from near wall information is first investigated as a priori test. The estimated velocity fields by the present CNN model are in statistical agreement with the reference DNS data. The model trained in a priori test is then combined with the LES as a posteriori test. We find that the LES can successfully be augmented using the present model at both the friction Reynolds number ${\rm Re}_{\tau}=180$ used for training and the unseen Reynolds number ${\rm Re}_{\tau}=360$ even when the first grid point is located at 5 wall units off the wall. We also investigate the robustness of the present model for the choice of sub-grid scale model and the possibility of transfer learning in a local domain. The observations through the paper suggest that the present model is a promising tool for recovering the accuracy of LES with a coarse grid near the wall.
QWin: Enforcing Tail Latency SLO at Shared Storage Backend

Consolidating latency-critical (LC) and best-effort (BE) tenants at storage backend helps to increase resources utilization. Even if tenants use dedicated queues and threads to achieve performance isolation, threads are still contend for CPU cores. Therefore, we argue that it is necessary to partition cores between LC and BE tenants, and meanwhile each core is dedicated to run a thread. Expect for frequently changing bursty load, fluctuated service time at storage backend also drastically changes the need of cores. In order to guarantee tail latency service level objectives (SLOs), the abrupt changing need of cores must be satisfied immediately. Otherwise, tail latency SLO violation happens. Unfortunately, partitioning-based approaches lack the ability to react the changing need of cores, resulting in extreme spikes in latency and SLO violation happens. In this paper, we present QWin, a tail latency SLO aware core allocation to enforce tail latency SLO at shared storage backend. QWin consists of an SLO-to-core calculation model that accurately calculates the number of cores combining with definitive runtime load determined by a flexible request-based window, and an autonomous core allocation that adjusts cores at adaptive frequency by dynamically changing core policies. When consolidating multiple LC and BE tenants, QWin outperforms the-state-of-the-art approaches in guaranteeing tail latency SLO for LC tenants and meanwhile increasing bandwidth of BE tenants by up to 31x.
Cooperative Multi-Agent Reinforcement-Learning-Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks

With the development of wireless communication and Internet of Things (IoT), there are massive wireless devices that need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this article, we investigate the distributed DSA problem for multiusers in a typical multichannel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we propose a centralized off-line training and distributed online execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent $Q$ -network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of a cognitive radio network in a distributed fashion without information exchange between cognitive users. Finally, we validate the proposed algorithm in various settings through extensive experiments. The experimental results show that the proposed CoMARL-DSA algorithm outperforms the state-of-the-art deep $Q$ -learning for spectrum access (DQSA) in terms of successful access rate and collision rate by at least 14% and 12%, respectively.
On the stability of the L2 projection and the quasiinterpolant in the space of smooth periodic splines

In this paper we derive stability estimates in Land L∞based Sobolev spaces for the L projection and a family of quasiinterolants in the space of smooth, 1-periodic, polynomial splines defined on a uniform mesh in [0, 1]. As a result of the assumed periodicity and the uniform mesh, cyclic matrix techniques and suitable decay estimates of the elements of the inverse of a Gram matrix associated with the standard basis of the space of splines, are used to establish the stability results.
BABEL: Bodies, Action and Behavior with English Labels

Understanding the semantics of human movement – the what, how and why of the movement – is an important problem that requires datasets of human actions with semantic labels. Existing datasets take one of two approaches. Large-scale video datasets contain many action labels but do not contain ground-truth 3D human motion. Alternatively, motion-capture (mocap) datasets have precise body motions but are limited to a small number of actions. To address this, we present BABEL, a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of language labels for over 43 hours of mocap sequences from AMASS, containing over 250 unique actions. Each action label in BABEL is precisely aligned with the duration of the corresponding action in the mocap sequence. BABELalso allows overlap of multiple actions, that may each span different durations. This results in a total of over 66000 action segments. The dense annotations can be leveraged for tasks like action recognition, temporal localization, motion synthesis, etc. To demonstrate the value of BABEL as a benchmark, we evaluate the performance of models on 3D action recognition. We demonstrate that BABEL poses interesting learning challenges that are applicable to real-world scenarios, and can serve as a useful benchmark for progress in 3D action recognition. The dataset, baseline methods, and evaluation code are available and supported for academic research purposes at https://babel.is. tue.mpg. de/.
Localized Uncertainty Attacks

The susceptibility of deep learning models to adversarial perturbations has stirred renewed attention in adversarial examples resulting in a number of attacks. However, most of these attacks fail to encompass a large spectrum of adversarial perturbations that are imperceptible to humans. In this paper, we present localized uncertainty attacks, a novel class of threat models against deterministic and stochastic classifiers. Under this threat model, we create adversarial examples by perturbing only regions in the inputs where a classifier is uncertain. To find such regions, we utilize the predictive uncertainty of the classifier when the classifier is stochastic or, we learn a surrogate model to amortize the uncertainty when it is deterministic. Unlike $\ell_p$ ball or functional attacks which perturb inputs indiscriminately, our targeted changes can be less perceptible. When considered under our threat model, these attacks still produce strong adversarial examples; with the examples retaining a greater degree of similarity with the inputs.
Seeing Differently, Acting Similarly: Imitation Learning with Heterogeneous Observations

In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces. This situation brings significant obstacles to existing imitation learning approaches, since most of them learn policies under homogeneous observation spaces. On the other hand, previous studies under different observation spaces have strong assumptions that these two observation spaces coexist during the entire learning process. However, in reality, the observation coexistence will be limited due to the high cost of acquiring expert observations. In this work, we study this challenging problem with limited observation coexistence under heterogeneous observations: Heterogeneously Observable Imitation Learning (HOIL). We identify two underlying issues in HOIL: the dynamics mismatch and the support mismatch, and further propose the Importance Weighting with REjection (IWRE) algorithm based on importance weighting and learning with rejection to solve HOIL problems. Experimental results show that IWRE can solve various HOIL tasks, including the challenging tasks of transforming the vision-based demonstrations to random access memory (RAM)-based policies in the Atari domain, even with limited visual observations.
Mixed-Integer Nonlinear Programming for State-Based Non-Intrusive Load Monitoring

Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the energy consumption of each appliance given the aggregate signal recorded by a single smart meter. In this paper, we propose a novel two-stage optimization-based approach for energy disaggregation. In the first phase, a small training set consisting of disaggregated power profiles is used to estimate the parameters and the power states by solving a mixed integer programming problem. Once the model parameters are estimated, the energy disaggregation problem is formulated as a constrained binary quadratic optimization problem. We incorporate penalty terms that exploit prior knowledge on how the disaggregated traces are generated, and appliance-specific constraints characterizing the signature of different types of appliances operating simultaneously. Our approach is compared with existing optimization-based algorithms both on a synthetic dataset and on three real-world datasets. The proposed formulation is computationally efficient, able to disambiguate loads with similar consumption patterns, and successfully reconstruct the signatures of known appliances despite the presence of unmetered devices, thus overcoming the main drawbacks of the optimization-based methods available in the literature.
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i.e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them. The interpolation, expressed as a deformation field, changes the pose of the source shape to resemble the target, but leaves the object identity unchanged. NeuroMorph uses an elegant architecture combining graph convolutions with global feature pooling to extract local features. During training, the model is incentivized to create realistic deformations by approximating geodesics on the underlying shape space manifold. This strong geometric prior allows to train our model end-to-end and in a fully unsupervised manner without requiring any manual correspondence annotations. NeuroMorph works well for a large variety of input shapes, including non-isometric pairs from different object categories. It obtains state-of-the-art results for both shape correspondence and interpolation tasks, matching or surpassing the performance of recent unsupervised and supervised methods on multiple benchmarks.
Large Scale Private Learning via Low-rank Reparametrization

We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing individual gradients, 2) the added noise suffering notorious dimensional dependence. Specifically, we reparametrize each weight matrix with two \emph{gradient-carrier} matrices of small dimension and a \emph{residual weight} matrix. We argue that such reparametrization keeps the forward/backward process unchanged while enabling us to compute the projected gradient without computing the gradient itself. To learn with differential privacy, we design \emph{reparametrized gradient perturbation (RGP)} that perturbs the gradients on gradient-carrier matrices and reconstructs an update for the original weight from the noisy gradients. Importantly, we use historical updates to find the gradient-carrier matrices, whose optimality is rigorously justified under linear regression and empirically verified with deep learning tasks. RGP significantly reduces the memory cost and improves the utility. For example, we are the first able to apply differential privacy on the BERT model and achieve an average accuracy of $83.9\%$ on four downstream tasks with $\epsilon=8$, which is within $5\%$ loss compared to the non-private baseline but enjoys much lower privacy leakage risk.