Miracle-dz/UniFAR-FTUs huggingface dataset:
Context Generation Improves Open Domain Question Answering

Closed-book question answering (QA) requires a model to directly answer an open-domain question without access to any external knowledge. Prior work on closed-book QA either directly finetunes or prompts a pretrained language model (LM) to leverage the stored knowledge. However, they do not fully exploit the parameterized knowledge. To address this inefficiency, we propose a two-stage, closed-book QA framework which employs a coarse-to-fine approach to extract the relevant knowledge and answer a question. We first generate a related context for a given question by prompting a pretrained LM. We then prompt the same LM to generate an answer using the generated context and the question. Additionally, we marginalize over the generated contexts to improve the accuracies and reduce context uncertainty. Experimental results on three QA benchmarks show that our method significantly outperforms previous closed-book QA methods. For example on TriviaQA, our method improves exact match accuracy from 55.3% to 68.6%, and is on par with open-book QA methods (68.6% vs. 68.0%). Our results show that our new methodology is able to better exploit the stored knowledge in pretrained LMs without adding extra learnable parameters or needing finetuning, and paves the way for hybrid models that integrate pretrained LMs with external knowledge.
On the Generalizability of ECG-based Stress Detection Models

Stress is prevalent in many aspects of everyday life including work, healthcare, and social interactions. Many works have studied handcrafted features from various bio-signals that are indicators of stress. Recently, deep learning models have also been proposed to detect stress. Typically, stress models are trained and validated on the same dataset, often involving one stressful scenario. However, it is not practical to collect stress data for every scenario. So, it is crucial to study the generalizability of these models and determine to what extent they can be used in other scenarios. In this paper, we explore the generalization capabilities of Electrocardiogram (ECG)-based deep learning models and models based on handcrafted ECG features, i.e., Heart Rate Variability (HRV) features. To this end, we train three HRV models and two deep learning models that use ECG signals as input. We use ECG signals from two popular stress datasets WESAD and SWELL-KW - differing in terms of stressors and recording devices. First, we evaluate the models using leave-one-subject-out (LOSO) cross-validation using training and validation samples from the same dataset. Next, we perform a cross-dataset validation of the models, that is, LOSO models trained on the WESAD dataset are validated using SWELL-KW samples and vice versa. While deep learning models achieve the best results on the same dataset, models based on HRV features considerably outperform them on data from a different dataset. This trend is observed for all the models on both datasets. Therefore, HRV models are a better choice for stress recognition in applications that are different from the dataset scenario. To the best of our knowledge, this is the first work to compare the cross-dataset generalizability between ECG-based deep learning models and HRV models.
Synthesis of Near-Field Arrays Based on Electromagnetic Inner Products

Near-field (NF) antennas have been successfully adopted in several wireless applications. To exploit the high reconfigurability of array antennas, multiple synthesis techniques for arrays operating in the NF region have been proposed. Building upon previous works on eigenmode expansions of the radiated fields, two synthesis methods for the excitations of NF arrays based on the definition of an inner product on the electromagnetic fields are investigated: the “maximum norm” and “minimum error field norm” methods. The “maximum norm” method computes the array excitations that maximize either the active power flow through a target surface or the electric/magnetic energy stored in an assigned volume, depending on the adopted inner product. The performance of the maximum active power flow method is compared with one of the simpler conjugate phase methods. Furthermore, the limit solution achieved when the target surface reaches the far-field region is compared against the “maximum beam collection efficiency” method. The “minimum error field norm” method allows to synthesize a given target field. As an example, the latter method is used to find the optimal excitation of a plane wave generator with a spherical quiet zone. The effectiveness and performance of the discussed synthesis methods are validated through numerical simulations.
Spectral Evolution of Ultraluminous X-Ray Pulsar NGC 300 ULX-1

We report on results from a 1 yr soft X-ray observing campaign of the ultraluminous X-ray pulsar NGC 300 ULX-1 by the Neutron star Interior Composition Explorer (NICER) during 2018–2019. Our analysis also made use of data from Swift/XRT and XMM-Newton in order to model and remove contamination from the nearby eclipsing X-ray binary NGC 300 X-1. We constructed and fitted a series of 5 day averaged NICER spectra of NGC 300 ULX-1 in the 0.4–4.0 keV range to evaluate the long-term spectral evolution of the source, and we found that an absorbed power-law model provided the best fit overall. Over the course of our observations, the source flux (0.4–4.0 keV; absorbed) dimmed from 2 × 10−12 to below 10−13 erg s−1 cm−2 and the spectrum softened, with the photon index going from Γ ≈ 1.6 to Γ ≈ 2.6. We interpret the spectral softening as reprocessed emission from the accretion disk edge coming into view while the pulsar was obscured by the possibly precessing disk. Some spectral fits were significantly improved by the inclusion of a disk blackbody component, and we surmise that this could be due to the pulsar emerging in between obscuration episodes by partial covering absorbers. We posit that we observed a low-flux state of the system (due to line-of-sight absorption) punctuated by the occasional appearance of the pulsar, indicating short-term source variability nested in longer-term accretion disk precession timescales.
The Econometrics of Rational Addiction

This article reexamines the econometric estimation of rational-addiction models considered by Becker, Grossman, and Murphy (BGM) for cigarette consumption. The rational-addiction model poses a number of additional econometric difficulties including endogeneity due to the presence of leads and lags of the dependent variable and serial correlation in the disturbances. BGM considered a fixed-effects two-stage least squares (2SLS) estimator. It is well known that this estimator is biased for fixed T. This article suggests a forward-filter first-difference 2SLS estimator and a generalized method of moments type of estimator that are consistent. Using a panel dataset of 46 states over the period 1963–1992, this article estimates the rational-addiction model for cigarettes. Our empirical results are both supportive of the rational-addiction hypothesis and more plausible than BGM's original results.
Spatio-Temporal Wildfire Prediction Using Multi-Modal Data

Due to severe societal and environmental impacts, wildfire prediction using multi-modal sensing data has become a highly sought-after data-analytical tool by various stakeholders (such as state governments and power utility companies) to achieve a more informed understanding of wildfire activities and plan preventive measures. A desirable algorithm should precisely predict fire risk and magnitude for a location in real time. In this paper, we develop a flexible spatio-temporal wildfire prediction framework using multi-modal time series data. We first predict the wildfire risk (the chance of a wildfire event) in real-time, considering the historical events using discrete mutually exciting point process models. Then we further develop a wildfire magnitude prediction set method based on the flexible distribution-free time-series conformal prediction (CP) approach. Theoretically, we prove a risk model parameter recovery guarantee, as well as coverage and set size guarantees for the CP sets. Through extensive real-data experiments with wildfire data in California, we demonstrate the effectiveness of our methods, as well as their flexibility and scalability in large regions.
Multiple measurements on an uncollapsed entangled two-photon state

The relativity of simultaneity together with definition of a quantum state's collapse result into experimental situations, where a multiple measurements can be taken on an uncollapsed quantum state. A quantum state's collapse space-time distribution is shown in a reference frame of a detector measuring the quantum system and in a reference frame moving relative to the detector. From their inspection follows, that under certain conditions, multiple measurements are allowed on the same uncollapsed quantum state. An application of the developed approach is shown on measurement of photon-pair state entangled in polarizations and energy. I derive conditions, under which two measurements can be taken on the uncollapsed photon-pair state. From allowance of multiple measurements on the same uncollapsed state follow serious consequences. For example, the measurements taken by both detectors in this situation are uncorrelated. Moreover, all the conservation laws could be violated in individual measurements, but not in mean value. This statement is proved on the two-photon state entangled in energy. This is in contradiction with experimental results observed by the detectors in rest relative to each other. If experimental results of the proposed experiment disagree with the predictions -- results measured on the quantum state are correlated, new space-time distribution of the quantum state's collapse, different from the Copenhagen interpretation, has to be proposed for proper solution of this situation.
Generalized Rashba electron-phonon coupling and superconductivity in strontium titanate

SrTiO3 is known for its proximity to a ferroelectric phase and for showing an 'optimal' doping for superconductivity with a characteristic dome-like behaviour resembling systems close to a quantum critical point. Several mechanisms have been proposed to link these phenomena, but the abundance of undetermined parameters prevents a definite assessment. Here, we use ab initio computations supplemented with a microscopic model to study the linear coupling between conduction electrons and the ferroelectric soft transverse modes allowed in the presence of spin-orbit coupling. We find a robust Rashba-like coupling, which can become surprisingly strong for particular forms of the polar eigenvector. We characterize this sensitivity for general eigenvectors and, for the particular form deduced by hyper-Raman scattering experiments, we find a BCS pairing coupling constant of the right order of magnitude to support superconductivity. The ab initio computations enable us to go beyond the linear-in-momentum conventional Rashba-like interaction and naturally explain the dome behaviour including a characteristic asymmetry. The dome is attributed to a momentum dependent quenching of the angular momentum due to a competition between spin-orbit and hopping energies. The optimum density for having maximum Tc results in rather good agreement with experiments without free parameters. These results make the generalized Rashba dynamic coupling to the ferroelectric soft mode a compelling pairing mechanism to understand bulk superconductivity in doped SrTiO3.
Observables in a lattice Universe: the cosmological fitting problem

We explore observables in a lattice Universe described by a recently found solution to Einstein field equations. This solution models a regular lattice of evenly distributed objects of equal masses. This inhomogeneous solution is perturbative, and, up to second order in a small parameter, it expands at a rate exactly equal to the one expected in a dust dominated Friedmann–Lemaître–Robertson–Walker (FLRW) model with the equivalent, smoothed energy density. Therefore, the kinematics of both cosmologies are identical up to the order of perturbation studied. Looking at the behaviour of the redshift and angular distance, we find a condition on the compactness of the objects at the centre of each cell under which corrections to the FLRW observables remain small, i.e. of order of a few percents at most. Nevertheless, we show that, if this condition is violated, i.e. if the objects are too compact, our perturbative scheme breaks down as far as the calculations of observables are concerned, even though the kinematics of the lattice remains identical to its FLRW counterpart (at the perturbative order considered). This may be an indication of an actual fitting problem, i.e. a situation in which the FLRW model obtained from lightcone observables does not correspond to the FLRW model obtained by smoothing the spatial distribution of matter. Fully non-perturbative treatments of the observables will be necessary to answer that question.
Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus

In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts. We provide a Bangla emotion classifier for six classes: anger, disgust, fear, joy, sadness, and surprise, from Bangla words using transformer-based models, which exhibit phenomenal results in recent days, especially for high-resource languages. The Unified Bangla Multi-class Emotion Corpus (UBMEC) is used to assess the performance of our models. UBMEC is created by combining two previously released manually labeled datasets of Bangla comments on six emotion classes with fresh manually labeled Bangla comments created by us. The corpus dataset and code we used in this work are publicly available.
Overcrowding and separation estimates for the Coulomb gas

We prove several results for the Coulomb gas in any dimension that follow from isotropic averaging, a transport method based on Newton's theorem. First, we prove a high‐density Jancovici–Lebowitz–Manificat law, extending the microscopic density bounds of Armstrong and Serfaty and establishing strictly sub‐Gaussian tails for charge excess in dimension 2. The existence of microscopic limiting point processes is proved at the edge of the droplet. Next, we prove optimal upper bounds on the k‐point correlation function for merging points, including a Wegner estimate for the Coulomb gas for . We prove the tightness of the properly rescaled kth minimal particle gap, identifying the correct order in and a three term expansion in , as well as upper and lower tail estimates. In particular, we extend the two‐dimensional “perfect‐freezing regime” identified by Ameur and Romero to higher dimensions. Finally, we give positive charge discrepancy bounds which are state of the art near the droplet boundary and prove incompressibility of Laughlin states in the fractional quantum Hall effect, starting at large microscopic scales. Using rigidity for fluctuations of smooth linear statistics, we show how to upgrade positive discrepancy bounds to estimates on the absolute discrepancy in certain regions.
Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation

Deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples, which can produce erroneous predictions by injecting imperceptible perturbations. In this work, we study the transferability of adversarial examples, which is significant due to its threat to real-world applications where model architecture or parameters are usually unknown. Many existing works reveal that the adversarial examples are likely to overfit the surrogate model that they are generated from, limiting its transfer attack performance against different target models. To mitigate the overfitting of the surrogate model, we propose a novel attack method, dubbed reverse adversarial perturbation (RAP). Specifically, instead of minimizing the loss of a single adversarial point, we advocate seeking adversarial example located at a region with unified low loss value, by injecting the worst-case perturbation (the reverse adversarial perturbation) for each step of the optimization procedure. The adversarial attack with RAP is formulated as a min-max bi-level optimization problem. By integrating RAP into the iterative process for attacks, our method can find more stable adversarial examples which are less sensitive to the changes of decision boundary, mitigating the overfitting of the surrogate model. Comprehensive experimental comparisons demonstrate that RAP can significantly boost adversarial transferability. Furthermore, RAP can be naturally combined with many existing black-box attack techniques, to further boost the transferability. When attacking a real-world image recognition system, Google Cloud Vision API, we obtain 22% performance improvement of targeted attacks over the compared method. Our codes are available at https://github.com/SCLBD/Transfer_attack_RAP.
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models

Understanding dynamics from visual observations is a challenging problem that requires disentangling individual objects from the scene and learning their interactions. While recent object-centric models can successfully decompose a scene into objects, modeling their dynamics effectively still remains a challenge. We address this problem by introducing SlotFormer -- a Transformer-based autoregressive model operating on learned object-centric representations. Given a video clip, our approach reasons over object features to model spatio-temporal relationships and predicts accurate future object states. In this paper, we successfully apply SlotFormer to perform video prediction on datasets with complex object interactions. Moreover, the unsupervised SlotFormer's dynamics model can be used to improve the performance on supervised downstream tasks, such as Visual Question Answering (VQA), and goal-conditioned planning. Compared to past works on dynamics modeling, our method achieves significantly better long-term synthesis of object dynamics, while retaining high quality visual generation. Besides, SlotFormer enables VQA models to reason about the future without object-level labels, even outperforming counterparts that use ground-truth annotations. Finally, we show its ability to serve as a world model for model-based planning, which is competitive with methods designed specifically for such tasks.
Quantum and stringy corrections to the equation of state of holographic QCD matter and the nature of the chiral transition

We consider the finite temperature phase diagram of holographic QCD in the Veneziano limit (${N}_{c}\ensuremath{\rightarrow}\ensuremath{\infty}$, ${N}_{f}\ensuremath{\rightarrow}\ensuremath{\infty}$ with ${x}_{f}={N}_{f}/{N}_{c}$ fixed) and calculate one string loop corrections to the free energy in certain approximations. Such corrections, especially due to the pion modes, are unsuppressed in the Veneziano limit. We find that under some extra assumptions the first order transition following from classical gravity solutions can become second order. If stringy asymptotics are of a special form and there are residual interactions, it may even become of third order. Operationally these computations imply modeling the low temperature chiral symmetry breaking phase with a hadron gas containing ${N}_{f}^{2}$ massless Goldstone bosons and an exponential spectrum of massive hadrons. A third order transition is possible only if repulsive hadron interactions via the excluded volume effect are included.
Task Compass: Scaling Multi-task Pre-training with Task Prefix

Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language models. Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. To tackle the challenge, we propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. We conduct extensive experiments on 40 datasets, which show that our model can not only serve as the strong foundation backbone for a wide range of tasks but also be feasible as a probing tool for analyzing task relationships. The task relationships reflected by the prefixes align transfer learning performance between tasks. They also suggest directions for data augmentation with complementary tasks, which help our model achieve human-parity results on commonsense reasoning leaderboards. Code is available at https://github.com/cooelf/CompassMTL
Systematic analysis of identified-hadron $\bf p_t$ spectra from 13 TeV p-p collisions

Identiﬁed-hadron (PID) p t spectra from 13 TeV p - p collisions are compared with a two-component (soft+hard) model (TCM) that accurately distinguishes jet-related hadron production (hard component) from nonjet projectile-nucleon dissociation (soft component). The present p - p study is similar to and is guided by recent TCM studies of PID spectra from 5 TeV p -Pb collisions. The combined analyses serve to establish a well-understood quantitative description of PID hadron production in small collision systems as a control experiment. The control can then be contrasted with conventional interpretations of collision data from more-central A-A collisions as indicating formation of a quark-gluon plasma (QGP). PID p t spectra from 13 TeV p - p collisions exhibit simple consistency with spectra from 5 TeV p -Pb collisions. Hadron species abundances are consistent with statistical-model trends predicted prior to commencement of the large hadron collider program. Diﬀerential spectrum structure and various ratio measures are quantitatively explained by the TCM, including its jet contribution, and admit no room for claims of hydrodynamic ﬂows in small collision systems.
Bifurcating-Paths: the relation between preferential flow bifurcations, void, and tortuosity on the Darcy scale

In recent years, Darcy scale transport in porous media was characterized to be Fickian or non-Fickian due to the homogeneity or heterogeneity of the porous medium conductivity layout. Yet, evidence shows that preferential flows that funnel the transport occur in heterogeneous and homogenous cases. We model the Darcy scale transport using a 2D conductivity field ranging from homogenous to heterogeneous and find that these preferential flows bifurcate, leaving voids where particles do not invade while forming a tortuous path. The fraction of bifurcations decreases downflow and reaches an asymptotical value, which scales as a power-law with the heterogeneity level. We show that the same power-law scaling of the bifurcations to heterogeneity level appears for the void fraction, tortuosity, and fractal dimension analysis with the same heterogeneity level. We conclude that the scaling with the heterogeneity is the dominant feature in the preferential flow geometry, which will lead to variations in weighting times for the transport and eventually to anomalous transport.