Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Neutral Hydrogen in Setting the Abundances of Molecular Species in the Milky Way’s Diffuse Interstellar Medium. II. Comparison between Observations and Theoretical Models

We compare observations of H i from the Very Large Array (VLA) and the Arecibo Observatory and observations of HCO+ from the Atacama Large Millimeter/submillimeter Array (ALMA) and the Northern Extended Millimeter Array (NOEMA) in the diffuse (A V ≲ 1) interstellar medium (ISM) to predictions from a photodissociation region (PDR) chemical model and multiphase ISM simulations. Using a coarse grid of PDR models, we estimate the density, FUV radiation field, and cosmic-ray ionization rate (CRIR) for each structure identified in HCO+ and H i absorption. These structures fall into two categories. Structures with T s < 40 K, mostly with N(HCO+) ≲ 1012 cm−2, are consistent with modest density, FUV radiation field, and CRIR models, typical of the diffuse molecular ISM. Structures with spin temperature T s > 40 K, mostly with N(HCO+) ≳ 1012 cm−2, are consistent with high density, FUV radiation field, and CRIR models, characteristic of environments close to massive star formation. The latter are also found in directions with a significant fraction of thermally unstable H i. In at least one case, we rule out the PDR model parameters, suggesting that alternative mechanisms (e.g., nonequilibrium processes like turbulent dissipation and/or shocks) are required to explain the observed HCO+ in this direction. Similarly, while our observations and simulations of the turbulent, multiphase ISM agree that HCO+ formation occurs along sight lines with N(H I) ≳ 1021 cm−2, the simulated data fail to explain HCO+ column densities ≳ few × 1012 cm−2. Because a majority of our sight lines with HCO+ had such high column densities, this likely indicates that nonequilibrium chemistry is important for these lines of sight.
Firefly: Supporting Drone Localization With Visible Light Communication

Drones are not fully trusted yet. Their reliance on radios and cameras for navigation raises safety and privacy concerns. These systems can fail, causing accidents, or be misused for unauthorized recordings. Considering recent regulations allowing commercial drones to operate only at night, we propose a radically new approach where drones obtain navigation information from artificial lighting. In our system, standard light bulbs modulate their intensity to send beacons and drones decode this information with a simple photodiode. This optical information is combined with the inertial and altitude sensors in the drones to provide localization without the need for radios, GPS or cameras. Our framework is the first to provide 3D drone localization with light and we evaluate it with a testbed consisting of four light beacons and a mini-drone. We show that, our approach allows to locate the drone within a few decimeters of the actual position and compared to state-of-the-art positioning methods, reduces the localization error by 42%.
A Scoping Review of Publicly Available Language Tasks in Clinical Natural Language Processing

OBJECTIVE To provide a scoping review of papers on clinical natural language processing (NLP) shared tasks that use publicly available electronic health record data from a cohort of patients. MATERIALS AND METHODS We searched 6 databases, including biomedical research and computer science literature databases. A round of title/abstract screening and full-text screening were conducted by 2 reviewers. Our method followed the PRISMA-ScR guidelines. RESULTS A total of 35 papers with 48 clinical NLP tasks met inclusion criteria between 2007 and 2021. We categorized the tasks by the type of NLP problems, including named entity recognition, summarization, and other NLP tasks. Some tasks were introduced as potential clinical decision support applications, such as substance abuse detection, and phenotyping. We summarized the tasks by publication venue and dataset type. DISCUSSION The breadth of clinical NLP tasks continues to grow as the field of NLP evolves with advancements in language systems. However, gaps exist with divergent interests between the general domain NLP community and the clinical informatics community for task motivation and design, and in generalizability of the data sources. We also identified issues in data preparation. CONCLUSION The existing clinical NLP tasks cover a wide range of topics and the field is expected to grow and attract more attention from both general domain NLP and clinical informatics community. We encourage future work to incorporate multidisciplinary collaboration, reporting transparency, and standardization in data preparation. We provide a listing of all the shared task papers and datasets from this review in a GitLab repository.
Directed Speech Separation for Automatic Speech Recognition of Long Form Conversational Speech

Many of the recent advances in speech separation are primarily aimed at synthetic mixtures of short audio utterances with high degrees of overlap. Most of these approaches need an additional stitching step to stitch the separated speech chunks for long form audio. Since most of the approaches involve Permutation Invariant training (PIT), the order of separated speech chunks is nondeterministic and leads to difficulty in accurately stitching homogenous speaker chunks for downstream tasks like Automatic Speech Recognition (ASR). Also, most of these models are trained with synthetic mixtures and do not generalize to real conversational data. In this paper, we propose a speaker conditioned separator trained on speaker embeddings extracted directly from the mixed signal using an over-clustering based approach. This model naturally regulates the order of the separated chunks without the need for an additional stitching step. We also introduce a data sampling strategy with real and synthetic mixtures which generalizes well to real conversation speech. With this model and data sampling technique, we show significant improvements in speaker-attributed word error rate (SA-WER) on Hub5 data.
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations

We consider a multi-armed bandit setting where, at the beginning of each round, the learner receives noisy independent, and possibly biased, \emph{evaluations} of the true reward of each arm and it selects $K$ arms with the objective of accumulating as much reward as possible over $T$ rounds. Under the assumption that at each round the true reward of each arm is drawn from a fixed distribution, we derive different algorithmic approaches and theoretical guarantees depending on how the evaluations are generated. First, we show a $\widetilde{O}(T^{2/3})$ regret in the general case when the observation functions are a genearalized linear function of the true rewards. On the other hand, we show that an improved $\widetilde{O}(\sqrt{T})$ regret can be derived when the observation functions are noisy linear functions of the true rewards. Finally, we report an empirical validation that confirms our theoretical findings, provides a thorough comparison to alternative approaches, and further supports the interest of this setting in practice.
The Lost Meaning of Jupiter’s High-degree Love Numbers

NASA’s Juno mission recently reported Jupiter’s high-degree (degree ℓ, azimuthal order m = 4, 2) Love number k 42 = 1.289 ± 0.063 (1σ), an order of magnitude above the hydrostatic k 42 obtained in a nonrotating Jupiter model. After numerically modeling rotation, the hydrostatic k 42 = 1.743 ± 0.002 is still 7σ away from the observation, raising doubts about our understanding of Jupiter’s tidal response. Here, we use first-order perturbation theory to explain the hydrostatic k 42 result analytically. We use a simple Jupiter equation of state (n = 1 polytrope) to obtain the fractional change in k 42 when comparing a rotating model with a nonrotating model. Our analytical result shows that the hydrostatic k 42 is dominated by the tidal response at ℓ = m = 2 coupled into the spherical harmonic ℓ, m = 4, 2 by the planet’s oblate figure. The ℓ = 4 normalization in k 42 introduces an orbital factor (a/s)2 into k 42, where a is the satellite semimajor axis and s is Jupiter’s average radius. As a result, different Galilean satellites produce a different k 42. We conclude that high-degree tesseral Love numbers (ℓ > m, m ≥ 2) are dominated by lower-degree Love numbers and thus provide little additional information about interior structure, at least when they are primarily hydrostatic. Our results entail important implications for a future interpretation of the currently observed Juno k 42. After including the coupling from the well-understood ℓ = 2 dynamical tides (Δk 2 ≈ −4%), Jupiter’s hydrostatic k 42 requires an unknown dynamical effect to produce a fractional correction Δk 42 ≈ −11% in order to fit Juno’s observation within 3σ. Future work is required to explain the required Δk 42.
Emergent dual holographic description as a nonperturbative generalization of the Wilsonian renormalization group

In holographic duality, dynamics along the emergent extra-dimensional space describes a renormalization group (RG) flow of the corresponding quantum field theory (QFT). Following this idea, we develop an emergent holographic description of a QFT, where not only the information of the RG flow is introduced into an IR holographic dual effective field theory (HDEFT), but also the UV information of the QFT is encoded in the HDEFT through the IR boundary condition. In particular, we argue that this dual holographic construction is self-consistent within the assumption of bulk locality, showing the following two aspects: The solution of the Hamilton-Jacobi equation is given by the IR boundary effective action, and the Ward identity involving the QFT energy-momentum tensor current is satisfied naturally. We discuss the role of the RG $\beta$-function in the bulk effective dynamics of the metric tensor near a conformally invariant fixed point. We claim that this emergent dual gravity theory generalizes the perturbative Wilsonian RG framework into a non-perturbative way.
A new spinner magnetometer: principles and techniques

Summary Design scheme, theory of operation, and practical techniques of a new spinner magnetometer are developed and tested. In this apparatus, a pair of bevel gears are used to rotate the sample simultaneously around two orthogonal axes, and magnetic signals are picked up by a fluxgate sensor, amplified by a programable gain amplifier, and sampled by an analogue - digital (AD) converter at timing signals from a rotary encorder. Magnetic signals contain many frequency components corresponding to dipole terms, quadrupole terms, and so on, and calculation of magnetization can be done by the fast Fourier transform (FFT) technique. As there is no need to replace the sample during a measurement, this new magnetometer is easily adapted to digital control and automatic measurement using a microcomputer. These possibilities are briefly discussed.
Depth Uncertainty Networks for Active Learning

In active learning, the size and complexity of the training dataset changes over time. Simple models that are well specified by the amount of data available at the start of active learning might suffer from bias as more points are actively sampled. Flexible models that might be well suited to the full dataset can suffer from overfitting towards the start of active learning. We tackle this problem using Depth Uncertainty Networks (DUNs), a BNN variant in which the depth of the network, and thus its complexity, is inferred. We find that DUNs outperform other BNN variants on several active learning tasks. Importantly, we show that on the tasks in which DUNs perform best they present notably less overfitting than baselines.
Holistic Interpretation of Public Scenes Using Computer Vision and Temporal Graphs to Identify Social Distancing Violations

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy.
The Taylor-Wiles method for coherent cohomology, II

We show that the Taylor-Wiles method can be applied to the cohomology of a Shimura variety S of PEL type attached to a unitary similitude group G, with coefficients in the coherent sheaf attached to an automorphic vector bundle F , when S has a smooth model over a p-adic integer ring. This generalizes the main results of the article [25], which treated the case when S is compact. As in the previous article, the starting point is a theorem of Lan and Suh that proves the vanishing of torsion in the cohomology under certain conditions on the parameters of the bundle F and the prime p. Most of the additional difficulty in the non-compact case is related to showing that the contributions of boundary cohomology are all of Eisenstein type. We also need to show that the coverings giving rise to the diamond operators can be extended to étale coverings of appropriate toroidal compactifications. The result is applied to show that, when the Taylor-Wiles method applies, the congruence ideal attached to a coherent cohomological realization of an automorphic Galois representation is independent of the signatures of the hermitian form to which G is attached. We also show that the Gorenstein hypothesis used to construct p-adic L-functions in [13] is valid under rather general hypotheses.
Inferring Infection-Spreading Links in an Air Traffic Network

The objective of this paper is to present a network-based optimization method for identifying links in an air traffic network responsible for carrying infected passengers into previously unexposed regions. The required data include individual infection reports (i.e., when the disease was first reported in a region), travel pattern data, and other geographic properties. The network structure is defined by nodes and links, which represent regions (cities, states, countries) and travel routes, respectively. The proposed methodology is novel in its attempt to replicate an outbreak pattern atop a transportation network by exploiting regional infection data. The problem parallels a related problem in phylodynamics, which uses genetic sequencing data to reconstruct the most likely spatiotemporal path of infection.
Representing Knowledge as Predictions (and State as Knowledge)

This paper shows how a single mechanism allows knowledge to be constructed layer by layer directly from an agent's raw sensorimotor stream. This mechanism, the General Value Function (GVF) or"forecast,"captures high-level, abstract knowledge as a set of predictions about existing features and knowledge, based exclusively on the agent's low-level senses and actions. Thus, forecasts provide a representation for organizing raw sensorimotor data into useful abstractions over an unlimited number of layers--a long-sought goal of AI and cognitive science. The heart of this paper is a detailed thought experiment providing a concrete, step-by-step formal illustration of how an artificial agent can build true, useful, abstract knowledge from its raw sensorimotor experience alone. The knowledge is represented as a set of layered predictions (forecasts) about the agent's observed consequences of its actions. This illustration shows twelve separate layers: the lowest consisting of raw pixels, touch and force sensors, and a small number of actions; the higher layers increasing in abstraction, eventually resulting in rich knowledge about the agent's world, corresponding roughly to doorways, walls, rooms, and floor plans. I then argue that this general mechanism may allow the representation of a broad spectrum of everyday human knowledge.
Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection

Anomaly detection is concerned with identifying examples in a dataset that do not conform to the expected behaviour. While a vast amount of anomaly detection algorithms exist, little attention has been paid to explaining why these algorithms flag certain examples as anomalies. However, such an explanation could be extremely useful to anyone interpreting the algorithms' output. This paper develops a method to explain the anomaly predictions of the state-of-the-art Isolation Forest anomaly detection algorithm. The method outputs an explanation vector that captures how important each attribute of an example is to identifying it as anomalous. A thorough experimental evaluation on both synthetic and real-world datasets shows that our method is more accurate and more efficient than most contemporary state-of-the-art explainability methods.
Controlled-rearing studies of newborn chicks and deep neural networks

Convolutional neural networks (CNNs) can now achieve human-level performance on challenging object recognition tasks. CNNs are also the leading quantitative models in terms of predicting neural and behavioral responses in visual recognition tasks. However, there is a widely accepted critique of CNN models: unlike newborn animals, which learn rapidly and efficiently, CNNs are thought to be"data hungry,"requiring massive amounts of training data to develop accurate models for object recognition. This critique challenges the promise of using CNNs as models of visual development. Here, we directly examined whether CNNs are more data hungry than newborn animals by performing parallel controlled-rearing experiments on newborn chicks and CNNs. We raised newborn chicks in strictly controlled visual environments, then simulated the training data available in that environment by constructing a virtual animal chamber in a video game engine. We recorded the visual images acquired by an agent moving through the virtual chamber and used those images to train CNNs. When CNNs received similar visual training data as chicks, the CNNs successfully solved the same challenging view-invariant object recognition tasks as the chicks. Thus, the CNNs were not more data hungry than animals: both CNNs and chicks successfully developed robust object models from training data of a single object.
MIN-turns and MAX-turns in k-Dyck paths: a pure generating function approach

$k$-Dyck paths differ from ordinary Dyck paths by using an up-step of length $k$. We analyze at which level the path is after the $s$-th up-step and before the $(s+1)$st up-step. In honour of Rainer Kemp who studied a related concept 40 years ago the terms \textsc{max}-terms and \textsc{min}-terms are used. Results are obtained by an appropriate use of trivariate generating functions; practically no combinatorial arguments are used.
DSPS: Differentiable Stellar Population Synthesis

Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for stellar population synthesis. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, ii) support for the Diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modeling the colors of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speedup over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy–halo connection that fully forward model galaxy spectra and photometry.
Remnant Radio Galaxy Candidates of Small Angular Sizes

Remnant radio galaxies (RRGs), characterized by the cessation of AGN activity, represent a short-lived last phase of radio galaxy’s life-cycle. Hitherto, searches for RRGs, mainly based on the morphological criteria, have identified large angular size sources resulting into a bias towards the remnants of powerful FR-II radio galaxies. In this study we make the first attempt to perform a systematic search for RRGs of small angular sizes (<30′′) in the XMM−LSS field. By using spectral curvature criterion we discover 48 remnant candidates exhibiting strong spectral curvature i.e., α150MHz325MHz−α325MHz1.4GHz≥ 0.5. Spectral characteristics at higher frequency regime (>1.4 GHz) indicate that some of our remnant candidates can depict recurrent AGN activity with an active core. We place an upper limit on the remnant fraction (frem) to be 3.9%, which increases to 5.4% if flux cutoff limit of S150MHz≥ 10 mJy is considered. Our study unveils, hitherto unexplored, a new population of small-size (<200 kpc) remnant candidates that are often found to reside in less dense environments and at higher redshifts (z) > 1.0. We speculate that a relatively shorter active phase and/or low jet power can be plausible reasons for the small size of remnant candidates.
SVIP: Sequence VerIfication for Procedures in Videos

In this paper, we propose a novel sequence verification task that aims to distinguish positive video pairs performing the same action sequence from negative ones with step-level transformations but still conducting the same task. Such a challenging task resides in an open-set setting without prior action detection or segmentation that requires event-level or even frame-level annotations. To that end, we carefully reorganize two publicly available action-related datasets with step-procedure-task structure. To fully investigate the effectiveness of any method, we collect a scripted video dataset enumerating all kinds of step-level transformations in chemical experiments. Besides, a novel evaluation metric Weighted Distance Ratio is introduced to ensure equivalence for different step-level transformations during evaluation. In the end, a simple but effective baseline based on the transformer encoder with a novel sequence alignment loss is introduced to better characterize long-term dependency between steps, which outperforms other action recognition methods. Codes and data will be released1: