Miracle-dz/UniFAR-FTUs huggingface dataset:
Backward Stackelberg Differential Game with Constraints: A Mixed Terminal-Perturbation and Linear-Quadratic Approach

We discuss an open-loop backward Stackelberg differential game involving single leader and single follower. Unlike most Stackelberg game literature, the state to be controlled is characterized by a backward stochastic differential equation (BSDE) for which the terminal- instead initial-condition is specified as a priori; the decisions of leader consist of a static terminal-perturbation and a dynamic linear-quadratic control. In addition, the terminal control is subject to (convex-closed) pointwise and (affine) expectation constraints. Both constraints are arising from real applications such as mathematical finance. For information pattern: the leader announces both terminal and open-loop dynamic decisions at the initial time while takes account the best response of follower. Then, two interrelated optimization problems are sequentially solved by the follower (a backward linear-quadratic (BLQ) problem) and the leader (a mixed terminal-perturbation and backward-forward LQ (BFLQ) problem). Our open-loop Stackelberg equilibrium is represented by some coupled backward-forward stochastic differential equations (BFSDEs) with mixed initial-terminal conditions. Our BFSDEs also involve nonlinear projection operator (due to pointwise constraint) combining with a Karush-Kuhn-Tucker (KKT) system (due to expectation constraint) via Lagrange multiplier. The global solvability of such BFSDEs is also discussed in some nontrivial cases. Our results are applied to one financial example.
A levitated atom-nanosphere hybrid quantum system

Near-field, radially symmetric optical potentials supported by a levitated nanosphere can be used for sympathetic cooling and for creating a bound nanosphere-atom system analogous to a large molecule. We demonstrate that the long range, Coulomb-like potential produced by a single blue detuned field increases the collisional cross-section by eight orders of magnitude, allowing fast sympathetic cooling of a trapped nanosphere to microKelvin temperatures using cold atoms. By using two optical fields to create a combination of repulsive and attractive potentials, we demonstrate that a cold atom can be bound to a nanosphere creating a new levitated hybrid quantum system suitable for exploring quantum mechanics with massive particles.
DECREASED CONCENTRATION OF MYOCARDIAL α‐ADRENOCEPTORS WITH INCREASING AGE IN FOETAL LAMBS

Using [3H]‐dihydroergocryptine, we identified myocardial α‐adrenoceptor binding sites in foetal lambs and demonstrated that the concentration of receptors decreased with increasing foetal age. The presence of the receptor in the foetus correlated with the presence of myocardial α‐adrenergic responsiveness. However, we found neither the α‐receptor binding site nor responsiveness to α‐adrenoceptor stimulation in the myocardium of adult sheep.
Factor analysis of mixed data for anomaly detection

Anomaly detection aims to identify observations that deviate from the typical pattern of data. Anomalous observations may correspond to financial fraud, health risks, or incorrectly measured data in practice. We focus on unsupervised detection and the continuous and categorical (mixed) variable case. We show that detecting anomalies in mixed data is enhanced through first embedding the data then assessing an anomaly scoring scheme. We propose a kurtosis‐weighted Factor Analysis of Mixed Data for anomaly detection to obtain a continuous embedding for anomaly scoring. We illustrate that anomalies are highly separable in the first and last few ordered dimensions of this space, and test various anomaly scoring experiments within this subspace. Results are illustrated for both simulated and real datasets, and the proposed approach is highly accurate for mixed data throughout these diverse scenarios.
Deblending galaxies with variational autoencoders: A joint multiband, multi-instrument approach

Blending of galaxies has a major contribution in the systematic error budget of weak-lensing studies, affecting photometric and shape measurements, particularly for ground-based, deep, photometric galaxy surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST). Existing deblenders mostly rely on analytic modelling of galaxy profiles and suffer from the lack of flexible yet accurate models. We propose to use generative models based on deep neural networks, namely variational autoencoders (VAE), to learn probabilistic models directly from data. We train a VAE on images of centred, isolated galaxies, which we reuse, as a prior, in a second VAE-like neural network in charge of deblending galaxies. We train our networks on simulated images including six LSST bandpass filters and the visible and near-infrared bands of the Euclid satellite, as our method naturally generalizes to multiple bands and can incorporate data from multiple instruments. We obtain median reconstruction errors on ellipticities and r-band magnitude between ±0.01 and ±0.05, respectively, in most cases, and ellipticity multiplicative bias of 1.6 per cent for blended objects in the optimal configuration. We also study the impact of decentring and prove the method to be robust. This method only requires the approximate centre of each target galaxy, but no assumptions about the number of surrounding objects, pointing to an iterative detection/deblending procedure we leave for future work. Finally, we discuss future challenges about training on real data and obtain encouraging results when applying transfer learning.
A De-Biased Direct Question Approach to Measuring Consumers' Willingness to Pay

Knowledge of consumers' willingness to pay (WTP) is a prerequisite to profitable price-setting. To gauge consumers' WTP, practitioners often rely on a direct single question approach in which consumers are asked to explicitly state their WTP for a product. Despite its popularity among practitioners, this approach has been found to suffer from hypothetical bias. In this paper, we propose a rigorous method that improves the accuracy of the direct single question approach. Specifically, we systematically assess the hypothetical biases associated with the direct single question approach and explore ways to de-bias it. Our results show that by using the de-biasing procedures we propose, we can generate a de-biased direct single question approach that is accu-rate enough to be useful for managerial decision-making. We validate this approach with two studies in this paper.
"I Cannot Do All of This Alone": Exploring Instrumental and Prayer Support in Online Health Communities

Online Health Communities (OHCs) are known to provide substantial emotional and informational support to patients and family caregivers facing life-threatening diagnoses like cancer and other illnesses, injuries, or chronic conditions. Yet little work explores how OHCs facilitate other vital forms of social support, especially instrumental support. We partner with this http URL---a prominent OHC for journaling about health crises---to complete a two-phase study focused on instrumental support. Phase one involves a content analysis of 641 CaringBridge updates. Phase two is a survey of 991 CaringBridge users. Results show that patients and family caregivers diverge from their support networks in their preferences for specific instrumental support types. Furthermore, ``prayer support'' emerged as the most prominent support category across both phases. We discuss design implications to accommodate divergent preferences and to expand the instrumental support network. We also discuss the need for future work to empower family caregivers and to support spirituality.
The effect of thermoregulatory substitution on optimal energy reserves of small birds in winter

Previous models have predicted the body mass of small birds in winter on the basis of a trade-off between starvation and predation. Many of these models have assumed that energy expenditure while active increases with body mass. The implications of the fact that the metabolic cost of activity can substitute for internal heat production and help keep the bird warm have not been investigated. In this paper we show that if thermoregulatory substitution occurs then there is a critical level of energy reserves above which an active bird is thermoneutral. This critical level increases as temperature decreases. Below this level, substitution of energy results in higher optimal levels of reserves than would be predicted in the absence of substitution. Our model thus predicts that at low temperatures body mass will be higher when thermoregulatory substitution occurs.
An Effective Entropy-assisted Mind-wandering Detection System with EEG Signals based on MM-SART Database

Mind-wandering (MW), which occurs between 20%-40% of the time, has negative effects on our daily life. In this work, we first collect a multi-modal Sustained Attention to Response Task (MM-SART) database for detecting MW. Eighty-two participants' data were collected in our experiments. For each participant, we collected measures of 32-channels electroencephalogram (EEG) signals, photoplethysmography (PPG) signals, galvanic skin response (GSR) signals, eye tracker signals, and several questionnaires for detailed analyses. Then, we propose an effective MW detection system based on the collected EEG signals. To explore the non-linear characteristics of EEG signals, we utilize the entropy-based features in time, frequency, and wavelet domains. The experimental results show that we can reach 0.712 AUC score by using the random forest (RF) classifier with the leave-one-subject-out cross-validation. Moreover, to lower the overall computational complexity of the MW detection system, we apply techniques of channel selection and feature selection. By using the only two most significant EEG channels, we can reduce the training time of the classifier by 44.16%. By performing correlation importance feature elimination (CIFE) on the feature set, we can further improve the AUC score to 0.725 but with only 14.6% of the selection time compared with the recursive feature elimination (RFE) method.
Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks

We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values. Our algorithm is fully unsupervised, however, can be readily extended to supervised or semisupervised cases when the anomaly labels are present as remarked throughout the paper. Our approach uses the Long Short Term Memory (LSTM) networks in order to extract temporal features and find the most relevant feature vectors for anomaly detection. We incorporate the sampling time information to our model by modulating the standard LSTM model with time modulation gates. After obtaining the most relevant features from the LSTM, we label the sequences using a Support Vector Data Descriptor (SVDD) model. We introduce a loss function and then jointly optimize the feature extraction and sequence processing mechanisms in an end-to-end manner. Through this joint optimization, the LSTM extracts the most relevant features for anomaly detection later to be used in the SVDD, hence completely removes the need for feature selection by expert knowledge. Furthermore, we provide a training algorithm for the online setup, where we optimize our model parameters with individual sequences as the new data arrives. Finally, on real-life datasets, we show that our model significantly outperforms the standard approaches thanks to its combination of LSTM with SVDD and joint optimization.
The Discussion Tracker Corpus of Collaborative Argumentation

Although NLP research on argument mining has advanced considerably in recent years, most studies draw on corpora of asynchronous and written texts, often produced by individuals. Few published corpora of synchronous, multi-party argumentation are available. The Discussion Tracker corpus, collected in high school English classes, is an annotated dataset of transcripts of spoken, multi-party argumentation. The corpus consists of 29 multi-party discussions of English literature transcribed from 985 minutes of audio. The transcripts were annotated for three dimensions of collaborative argumentation: argument moves (claims, evidence, and explanations), specificity (low, medium, high) and collaboration (e.g., extensions of and disagreements about others’ ideas). In addition to providing descriptive statistics on the corpus, we provide performance benchmarks and associated code for predicting each dimension separately, illustrate the use of the multiple annotations in the corpus to improve performance via multi-task learning, and finally discuss other ways the corpus might be used to further NLP research.
Relative biological effectiveness (RBE) of 210 Po alpha-particles versus X-rays on lethality in bovine endothelial cells

Purpose : Alpha-radiation from polonium-210 (210 Po) can elevate background radiation dose by an order of magnitude in people consuming large quantities of meat and seafood, particularly caribou and reindeer. Because up to 50% of the ingested 210 Po body burden is initially found in the blood, a primary target for the short range alpha-particles is the endothelial cells lining the blood vessels. This study examined the relative biological effectiveness (RBE) of 210 Po alpha-particles versus 250 kVp X-rays in producing injury to cultured bovine aortic endothelial cells. Materials and methods : Radiation effects on cells were measured in four different ways: the percentage viable cells by trypan blue dye exclusion, the number of live cells, the lactate dehydrogenase (LDH) release to medium and the ability to form colonies (clonogenic survival). Results : Comparison of dose-response curves yielded RBE values of 13.1 ±2.5 (SEM) for cell viability, 10.3 ±1.0 for live cell number and 11.1 ±3.0 for LDH activity. The RBE values for clonogenic survival were 14.0 ±1.0 based on the ratio of the initial slopes of the dose-response curves and 13.1, 9.9 and 7.7 for 50, 10 and 1% survival rate, respectively. At X-ray doses <0.25 Gy, a pronounced stimulatory effect on proliferation was noted. Conclusions : Exposure to 210 Po alpha-particles was seven to 14 times more effective than X-ray exposure in causing endothelial cell damage.
Reproductive Patterns of two Varieties of Yucca whipplei (Liliaceae) with Different Life Histories

Flower, fruit, and seed production were compared for two varieties of Yucca whipplei, Y. whipplei whipplei (which reproduces semelparously) and Y. whipplei caespitosa (which is iteroparous), to determine differences in fecundity with respect to leaf surface area. Vegetative characteristics such as leaf surface area and leaf area index, which are related to the potential for gas exchange and photosynthate production in these plants, and reproductive characteristics such as inflorescence size, total flowers, mature fruit, and seed production as well as seed viability were determined for individuals of each variety. There was no significant difference in leaf surface area between varieties, but the number of viable seeds produced per unit leaf surface area was greater for Y w. whipplei than for Y. w. caespitosa. There was a significant difference in seed quality between varieties; Y. w. whipplei had seeds that were 1.5 times more viable and germinated twice as fast as Y. w. caespitosa. The total number of viable seeds per plant increased with leaf surface area for Y. w. whipplei, but for Y. w. caespitosa the total number of viable seeds per plant increased with increasing rosette number per individual. For Y. w. whipplei, the percentage of viable seeds per plant decreased as inflorescence size increased. However, the total number of viable seeds produced was positively correlated with inflorescence size, indicating that for a greater investment in flowers and ultimately fruit, the plant received diminishing returns in terms of seed number. There was no such relationship for Y. w. caespitosa, indicating that attached rosettes may provide resources for producing viable seeds. These reproductive patterns are consistent with observed patterns of resource use in semelparous and iteroparous plants but allow new insight on size/fecundity patterns in closely related plants.
Learning From Naturalistic Driving Data for Human-Like Autonomous Highway Driving

Driving in a human-like manner is important for an autonomous vehicle to be a smart and predictable traffic participant. To achieve this goal, parameters of the motion planning module should be carefully tuned, which needs great effort and expert knowledge. In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed. The learning is achieved by encouraging the selected trajectory to approximate the human driving trajectory under the same traffic situation. The employed motion planner follows a widely accepted methodology that first samples candidate trajectories in the trajectory space, then select the one with minimal cost as the planned trajectory. Moreover, in addition to traditional factors such as comfort, efficiency and safety, the cost function is proposed to incorporate incentive of behavior decision like a human driver, so that both lane change decision and motion planning are coupled into one framework. Two types of lane incentive cost — heuristic and learning based — are proposed and implemented. To verify the validity of the proposed method, a data set is developed by using the naturalistic trajectory data of human drivers collected on the motorways in Beijing, containing samples of lane changes to the left and right lanes, and car followings. Experiments are conducted with respect to both lane change decision and motion planning, and promising results are achieved.
Multicarrier Spectral Shaping for Non-White Interference Channels: Application to Aeronautical Communications in the L-Band

Adaptive radio techniques have been extensively studied over the past two decades. In this paper, we investigate an adaptive algorithm to attain additive white Gaussian noise (AWGN) performance in non-white channels, using multicarrier communications such as OFDM and filter-bank multicarrier (FBMC). The non-white channel can be from non-white noise, or more commonly, interference. Our proposed algorithm: spectral shaping (SS), follows a simple optimization problem to find what we term reliable subcarriers, and then per subcarrier power allocations are made in order to attain the AWGN channel bit error ratio (BER). Subcarriers that experience high interference are deactivated or chosen as guard subcarriers. After describing our analysis, we show some simulation results in two non-white interference signal examples: the Gaussian pulse shaped high-power distance measuring equipment (DME) and a classical rectangular-pulse signal. The DME example is pertinent for currently proposed aeronautical communication systems, where new multicarrier techniques, e.g., the L-band digital Aeronautical communication systems (LDACS) have been designed as an inlay approach between the high-power DME channels in L-band. Our results show that in high DME power interferences, using this adaptive technique can improve the overall reliability and attain AWGN BER performance in AWGN channel. We provide power spectral density (PSD) results to compare OFDM and FBMC based spectra when our SS algorithm is applied. Also, we provide BER vs. bit energy to noise density ratio (Eb/N0) results in the AWGN, as well as more realistic aeronautical channels to compare SS system performance. Our results show that in the presence of high power DME interference, using this adaptive technique can improve the overall reliability and enable us to attain non-interference BER performance in realistic aeronautical channels.
Sparse Attention Guided Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning

Training deep reinforcement learning agents on environments with multiple levels / scenes from the same task, has become essential for many applications aiming to achieve generalization and domain transfer from simulation to the real world. While such a strategy is helpful with generalization, the use of multiple scenes significantly increases the variance of samples collected for policy gradient computations. Current methods, effectively continue to view this collection of scenes as a single Markov decision process (MDP), and thus learn a scene-generic value function V(s). However, we argue that the sample variance for a multi-scene environment is best minimized by treating each scene as a distinct MDP, and then learning a joint value function V(s,M) dependent on both state s and MDP M. We further demonstrate that the true joint value function for a multi-scene environment, follows a multi-modal distribution which is not captured by traditional CNN / LSTM based critic networks. To this end, we propose a dynamic value estimation (DVE) technique, which approximates the true joint value function through a sparse attention mechanism over multiple value function hypothesis / modes. The resulting agent not only shows significant improvements in the final reward score across a range of OpenAI ProcGen environments, but also exhibits enhanced navigation efficiency and provides an implicit mechanism for unsupervised state-space skill decomposition.
Machine Vision using Diffractive Spectral Encoding

Machine vision systems mostly rely on lens-based optical imaging architectures that relay the spatial information of objects onto high pixel-count opto-electronic sensor arrays, followed by digital processing of this information. Here, we demonstrate an optical machine vision system that uses trainable matter in the form of diffractive layers to transform and encode the spatial information of objects into the power spectrum of the diffracted light, which is used to perform optical classification of objects with a single-pixel spectroscopic detector. Using a time-domain spectroscopy setup with a plasmonic nanoantenna-based detector, we experimentally validated this framework at terahertz spectrum to optically classify the images of handwritten digits by detecting the spectral power of the diffracted light at ten distinct wavelengths, each representing one class/digit. We also report the coupling of this spectral encoding achieved through a diffractive optical network with a shallow electronic neural network, separately trained to reconstruct the images of handwritten digits based on solely the spectral information encoded in these ten distinct wavelengths within the diffracted light. These reconstructed images demonstrate task-specific image decompression and can also be cycled back as new inputs to the same diffractive network to improve its optical object classification. This unique framework merges the power of deep learning with the spatial and spectral processing capabilities of trainable matter, and can also be extended to other spectral-domain measurement systems to enable new 3D imaging and sensing modalities integrated with spectrally encoded classification tasks performed through diffractive networks.