Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a rapid, in‐situ analysis method using sheath‐flow probe electrospray ionisation‐mass spectrometry for the direct identification of cocaine metabolites in dried blood spots

Rationale Small amounts of biofluid samples are frequently found at crime scenes; however, existing gold standard methods such as LC–MS frequently require destructive extraction of the sample before a time‐consuming analysis which puts strain on forensic analysis providers and can preclude further sample analysis. This study presents the application of sheath‐flow probe electrospray ionization‐mass spectrometry (sfPESI–MS) to the direct analysis of drug metabolites in dried blood spots (DBS) as a high throughput, minimally destructive alternative. Methods A rapid direct analysis method using a sfPESI ionisation source coupled to an Orbitrap Exactive mass spectrometer was applied to detect cocaine metabolites (benzoylecgonine, BZE, cocaethylene, CE, and ecgonine methyl ester, EME) from DBS. An optimisation study exploring the use of different chemical modifiers (formic acid and sodium acetate) in the sfPESI probe extraction solvent was conducted to enhance the sensitivity and reproducibility of the sfPESI–MS method. Results Optimisation of the extraction solvent significantly enhanced the sensitivity and reproducibility of the sfPESI–MS method. A quantitative response over a five‐point calibration range 0.5 to 10 μg/ml was obtained for BZE (R2 = 0.9979) and CE (R2 = 0.9948). Limits of detection (LOD) of 1.31, 0.29 and 0.15 μg/ml were achieved for EME, BZE and CE, respectively, from 48 h aged DBSs with % RSD (relative standard deviation) across the calibration range ranging between 19%–28% for [BZE + H]+, 13%–21% for [CE + H]+ and 12%–29% for [EME + H]+. Conclusions A rapid (< 20 s) quantitative method for the direct analysis of cocaine metabolites from DBS which requires no prior sample preparation was developed. Although the LOD achieved for BZE (LOD: 0.29 μg/ml) was above the UK threshold limit of exposure for drug driving (0.05 μg/ml), the method may be suitable for use in identifying overdose in forensic analysis.
Doppler cooling to the recoil limit using sharp atomic transitions

In this paper, we develop an analytical approach to Doppler cooling of atoms by one- or two-photon transitions when the natural width of the excited level is so small that the process leads to a Doppler temperature comparable to the recoil temperature. A “quenching” of the sharp line is introduced in order to allow control of the time scale of the problem. In such limit, the usual Fokker-Planck equation does not correctly describe the cooling process. We propose a generalization of the Fokker-Planck equation and derive a new model which is able to reproduce correctly the numerical results, up to the recoil limit. Two cases of practical interest, one-photon Doppler cooling of strontium and two-photon Doppler cooling of hydrogen are considered.
Chandra Early-type Galaxy Atlas

The hot interstellar medium (ISM) in early-type galaxies (ETGs) plays a crucial role in understanding their formation and evolution. The structural features of the hot gas identified by Chandra observations point to key evolutionary mechanisms, (e.g., active galactic nuclei, AGN, and stellar feedback, merging history). In our Chandra Galaxy Atlas (CGA) project, taking full advantage of the Chandra capabilities, we systematically analyzed the archival Chandra data of 70 ETGs and produced uniform data products for the hot gas properties. The primary data products are spatially resolved 2D spectral maps of the hot gas from individual galaxies. We emphasize that new features can be identified in the spectral maps that are not readily visible in the surface brightness maps. The high-level images can be viewed at the dedicated CGA website, and the CGA data products can be downloaded to compare with data at other wavelengths and to perform further analyses. Using our data products, we address a few focused science topics.
Adult Spinal Deformity: A Comprehensive Review of Current Advances and Future Directions

Owing to rapidly changing global demographics, adult spinal deformity (ASD) now accounts for a significant proportion of the Global Burden of Disease. Sagittal imbalance caused by age-related degenerative changes leads to back pain, neurological deficits, and deformity, which negatively affect the health-related quality of life (HRQoL) of patients. Along with the recognized regional, global, and sagittal spinopelvic parameters, poor paraspinal muscle quality has recently been acknowledged as a key determinant of the clinical outcomes of ASD. Although the Scoliosis Research Society-Schwab ASD classification system incorporates the radiological factors related to HRQoL, it cannot accurately predict the mechanical complications. With the rapid advances in surgical techniques, many surgical options for ASD have been developed, ranging from minimally invasive surgery to osteotomies. Therefore, structured patient-specific management is important in surgical decision-making, selecting the proper surgical technique, and to prevent serious complications in patients with ASD. Moreover, utilizing the latest technologies such as robotic-assisted surgery and machine learning, should help in minimizing the surgical risks and complications in the future.
Deep Learning for Design and Retrieval of Nano-photonic Structures

Our visual perception of our surroundings is ultimately limited by the diffraction limit, which stipulates that optical information smaller than roughly half the illumination wavelength is not retrievable. Over the past decades, many breakthroughs have led to unprecedented imaging capabilities beyond the diffraction-limit, with applications in biology and nanotechnology. In this context, nano-photonics has revolutionized the field of optics in recent years by enabling the manipulation of light-matter interaction with subwavelength structures. However, despite the many advances in this field, its impact and penetration in our daily life has been hindered by a convoluted and iterative process, cycling through modeling, nanofabrication and nano-characterization. The fundamental reason is the fact that not only the prediction of the optical response is very time consuming and requires solving Maxwell's equations with dedicated numerical packages. But, more significantly, the inverse problem, i.e. designing a nanostructure with an on-demand optical response, is currently a prohibitive task even with the most advanced numerical tools due to the high non-linearity of the problem. Here, we harness the power of Deep Learning, a new path in modern machine learning, and show its ability to predict the geometry of nanostructures based solely on their far-field response. This approach also addresses in a direct way the currently inaccessible inverse problem breaking the ground for on-demand design of optical response with applications such as sensing, imaging and also for plasmon's mediated cancer thermotherapy.
Razor-thin dust layers in protoplanetary disks: Limits on the vertical shear instability

Context: Recent observations with the Atacama Large Millimeter Array (ALMA) have shown that the large dust aggregates observed at millimeter wavelengths settle to the midplane into a remarkably thin layer. Aims: We intend to find out if the geometric thinness of these layers is evidence against the vertical shear instability (VSI) operating in these disks. Methods: We performed hydrodynamic simulations of a protoplanetary disk with a locally isothermal equation of state, and let the VSI fully develop. We sprinkled dust particles and followed their motion as they got stirred up by the VSI. We determined for which grain size the layer becomes geometrically thin enough to be consistent with ALMA observations. We then verified if, with these grain sizes, it is still possible to generate a moderately optically thick layer at millimeter wavelengths, as observations appear to indicate. Results: We found that even very large dust aggregates with Stokes numbers close to unity get stirred up to relatively large heights above the midplane by the VSI, which is in conflict with the observed geometric thinness. For grains so large that the Stokes number exceeds unity, the layer can be made to remain thin, but we show that it is hard to make dust layers optically thick at ALMA wavelengths (e.g., tau(1.3mm)>=1) with such large dust aggregates. Conclusions: We conclude that protoplanetary disks with geometrically thin midplane dust layers cannot be VSI unstable, at least not down to the disk midplane. Explanations for the inhibition of the VSI include a reduced dust-to-gas ratio of the small dust grains that are responsible for the radiative cooling of the disk. A reduction of small grains by a factor of between 10 and 100 is sufficient to quench the VSI. Such a reduction is plausible in dust growth models, and still consistent with observations at optical and infrared wavelengths.
Focused Concatenation for Context-Aware Neural Machine Translation

A straightforward approach to context-aware neural machine translation consists in feeding the standard encoder-decoder architecture with a window of consecutive sentences, formed by the current sentence and a number of sentences from its context concatenated to it. In this work, we propose an improved concatenation approach that encourages the model to focus on the translation of the current sentence, discounting the loss generated by target context. We also propose an additional improvement that strengthen the notion of sentence boundaries and of relative sentence distance, facilitating model compliance to the context-discounted objective. We evaluate our approach with both average-translation quality metrics and contrastive test sets for the translation of inter-sentential discourse phenomena, proving its superiority to the vanilla concatenation approach and other sophisticated context-aware systems.
Learning a Grammar Inducer from Massive Uncurated Instructional Videos

Video-aided grammar induction aims to leverage video information for finding more accurate syntactic grammars for accompanying text. While previous work focuses on building systems for inducing grammars on text that are well-aligned with video content, we investigate the scenario, in which text and video are only in loose correspondence. Such data can be found in abundance online, and the weak correspondence is similar to the indeterminacy problem studied in language acquisition. Furthermore, we build a new model that can better learn video-span correlation without manually designed features adopted by previous work. Experiments show that our model trained only on large-scale YouTube data with no text-video alignment reports strong and robust performances across three unseen datasets, despite domain shift and noisy label issues. Furthermore our model yields higher F1 scores than the previous state-of-the-art systems trained on in-domain data.
Global fits of Dirac dark matter effective field theories

In this proceeding, we present results from a global fit of Dirac fermion dark matter (DM) effective field theory (EFT) based on arXiv:2106.02056 using the GAMBIT framework. Here we show results only for the dimension-6 operators that describe the interactions between a gauge-singlet Dirac fermion and Standard Model quarks. Our global fit combines the latest constraints from Planck, direct and indirect DM detection, and the LHC. For DM mass below 100 GeV, it is impossible to simultaneously satisfy all constraints while maintaining the EFT validity at high energies. For higher masses, however, large regions of parameter space remain viable where the EFT is valid and saturates the observed DM abundance.
RACE: Retrieval-augmented Commit Message Generation

Commit messages are important for software development and maintenance. Many neural network-based approaches have been proposed and shown promising results on automatic commit message generation. However, the generated commit messages could be repetitive or redundant. In this paper, we propose RACE, a new retrieval-augmented neural commit message generation method, which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message. As the retrieved commit message may not always accurately describe the content/intent of the current code diff, we also propose an exemplar guider, which learns the semantic similarity between the retrieved and current code diff and then guides the generation of commit message based on the similarity. We conduct extensive experiments on a large public dataset with five programming languages. Experimental results show that RACE can outperform all baselines. Furthermore, RACE can boost the performance of existing Seq2Seq models in commit message generation.
End-to-End Lyrics Recognition with Self-supervised Learning

Lyrics recognition is an important task in music processing. Despite traditional algorithms such as the hybrid HMM- TDNN model achieving good performance, studies on applying end-to-end models and self-supervised learning (SSL) are limited. In this paper, we first establish an end-to-end baseline for lyrics recognition and then explore the performance of SSL models on lyrics recognition task. We evaluate a variety of upstream SSL models with different training methods (masked reconstruction, masked prediction, autoregressive reconstruction, and contrastive learning). Our end-to-end self-supervised models, evaluated on the DAMP music dataset, outperform the previous state-of-the-art (SOTA) system by 5.23% for the dev set and 2.4% for the test set even without a language model trained by a large corpus. Moreover, we investigate the effect of background music on the performance of self-supervised learning models and conclude that the SSL models cannot extract features efficiently in the presence of background music. Finally, we study the out-of-domain generalization ability of the SSL features considering that those models were not trained on music datasets.
Volatility forecasting using Deep Learning and sentiment analysis

Several studies have shown that deep learning models can provide more accurate volatility forecasts than the traditional methods used within this domain. This paper presents a composite model that merges a deep learning approach with sentiment analysis for predicting market volatility. To classify public sentiment, we use a Convolutional Neural Network, which obtained data from Reddit global news headlines. We then describe a composite forecasting model, a Long-Short-Term-Memory Neural Network method, to use historical sentiment and the previous day's volatility to make forecasts. We employed this method on the past volatility of the S&P500 and the major BRICS indices to corroborate its effectiveness. Our results demonstrate that including sentiment can improve Deep Learning volatility forecasting models. However, in contrast to return forecasting, the performance benefits of including sentiment appear for volatility forecasting appears to be market specific.
Surpassing the Standard Quantum Limit using an Optical Spring

Quantum mechanics places noise limits and sensitivity restrictions on physical measurements. The balance between unwanted backaction and the precision of optical measurements impose a standard quantum limit (SQL) on interferometric systems. In order to realize a sensitivity below the SQL, it is necessary to leverage a back-action evading measurement technique, or else exploit cancellations of any excess noise contributions at the detector. %Many proof of principle experiments have been performed, but only recently has an experiment achieved sensitivity below the SQL. In this work, we extend that initial demonstration and realize sub-SQL measurement sensitivity nearly two times better than previous measurements, and with architecture applicable to interferometric gravitational wave detectors. In fact, this technique is directly applicable to Advanced LIGO, which could observe similar effects with a detuned signal recycling cavity. By exploiting quantum correlations created by an optical spring, we measure a total sensitivity below the SQL by $\textbf{2.8}$ dB, corresponding to a reduction in the noise power by $\textbf{72}\pm\textbf{5.1}$ \% below the quantum limit. Through the use of a detuned optical spring, this noise reduction is tunable, allowing us to choose the desired range of frequencies that fall below the SQL. This result demonstrates access to sensitivities well below the SQL at frequencies ranges applicable to LIGO, with the potential to extend the reach of gravitational wave detectors further into the universe.
JefiPIC: A 3-D Full Electromagnetic Particle-in-Cell Simulator Based on Jefimenko's Equations on GPU

This paper presents a novel 3-D full electromagnetic particle-in-cell (PIC) code called JefiPIC, which uses Jefimenko's equations as the electromagnetic (EM) field solver through a full-space integration method. Leveraging the power of state-of-the-art graphic processing units (GPUs), we have made the challenging integral task of PIC simulations achievable. Our proposed code offers several advantages by utilizing the integral method. Firstly, it offers a natural solution for modeling non-neutral plasmas without the need for pre-processing such as solving Poisson's equation. Secondly, it eliminates the requirement for designing elaborate boundary layers to absorb fields and particles. Thirdly, it maintains the stability of the plasma simulation regardless of the time step chosen. Lastly, it does not require strict charge-conservation particle-to-grid apportionment techniques or electric field divergence amendment algorithms, which are commonly used in finite-difference time-domain (FDTD)-based PIC simulations. To validate the accuracy and advantages of our code, we compared the evolutions of particles and fields in different plasma systems simulated by three other codes. Our results demonstrate that the combination of Jefimenko's equations and the PIC method can produce accurate particle distributions and EM fields in open-boundary plasma systems. Additionally, our code is able to accomplish these computations within an acceptable execution time. This study highlights the effectiveness and efficiency of JefiPIC, showing its potential for advancing plasma simulations.
“Covid vaccine is against Covid but Oxford vaccine is made at Oxford!” Semantic Interpretation of Proper Noun Compounds

Proper noun compounds, e.g., “Covid vaccine”, convey information in a succinct manner (a “Covid vaccine” is a “vaccine that immunizes against the Covid disease”). These are commonly used in short-form domains, such as news headlines, but are largely ignored in information-seeking applications. To address this limitation, we release a new manually annotated dataset, ProNCI, consisting of 22.5K proper noun compounds along with their free-form semantic interpretations. ProNCI is 60 times larger than prior noun compound datasets and also includes non-compositional examples, which have not been previously explored. We experiment with various neural models for automatically generating the semantic interpretations from proper noun compounds, ranging from few-shot prompting to supervised learning, with varying degrees of knowledge about the constituent nouns. We find that adding targeted knowledge, particularly about the common noun, results in performance gains of upto 2.8%. Finally, we integrate our model generated interpretations with an existing Open IE system and observe an 7.5% increase in yield at a precision of 85%. The dataset and code are available at https://github.com/dair-iitd/pronci.
Tapered Optical Fiber-based Detection of Charged Particle Irradiation in Space Exploration and Nuclear Reactors

: In this work, we demonstrate the use of tapered optical fibers (TOF) to detect charged particle (ions), irradiated at various energies, fluences and species. The single mode tapered optical fiber has been used in various sensing applications in recent times. Here, tapered optical fibers have been exposed to two different ion species namely Ar + and N + at different energies and different fluences, respectively. The optical spectrum analyser (OSA) detects the changes in the free spectral range (FSR), period, and transmission power loss from the ion beam irradiated TOFs. The change in the refractive index of the cladding material due to the implanted ions influences the transmission spectra of the TOFs and we could able to detect ions of energy as low as 80 keV. COMSOL simulation results are employed to explain the observed changes in spectra. The ion beams induced surface modification and defect formation as well as the implantation in TOF have been predicted using Monte Carlo based 3D TRI3DYN ion-solid interaction simulation and corroborated with other experimental studies such as scanning electron microscopy and Raman scattering spectroscopy. Such tapered optical fiber-based detection technique will help to develop portable device to detect charged particles in space exploration and in nuclear reactors.
Rao-Blackwellized Monte Carlo Data Association for Multiple Target Tracking

We propose a new Rao-Blackwellized sequential Monte Carlo method for tracking multiple targets in presence of clutter and false alarm measurements. The advantage of the new approach is that Rao-Blackwellization allows the estimation algorithm to be partitioned into single target tracking and data association sub-problems, where the single target tracking sub-problem can be solved by Kalman filters or extended Kalman filters, and the data association by sequential importance resampling. Because the sampled sub-space is finite, it is possible to use the optimal importance distribution explicitly, which significantly reduces the required number of Monte Carlo samples.
Hypothesis Test Procedures for Detecting Leakage Signals in Water Pipeline Channels

We design statistical hypothesis tests for performing leak detection in water pipeline channels. By applying an appropriate model for signal propagation, we show that the detection problem becomes one of distinguishing signal from noise, with the noise being described by a multivariate Gaussian distribution with unknown covariance matrix. We first design a test procedure based on the generalized likelihood ratio test, which we show through simulations to offer appreciable leak detection performance gain over conventional approaches designed in an analogous context (for radar detection). Our proposed method requires estimation of the noise covariance matrix, which can become inaccurate under high-dimensional settings, and when the measurement data is scarce. To deal with this, we present a second leak detection method, which employs a regularized covariance matrix estimate. The regularization parameter is optimized for the leak detection application by applying results from large dimensional random matrix theory. This second proposed approach is shown to yield improved performance in leak detection compared with the first approach, at the expense of requiring higher computational complexity.