Miracle-dz/UniFAR-FTUs huggingface dataset:
Taking snapshots of the jet-ISM interplay with ALMA

Abstract We present an update of our ongoing project to characterise the impact of radio jets on the interstellar medium (ISM). This is done by tracing the distribution, kinematics and excitation of the molecular gas at high spatial resolution using ALMA. The radio active galactic nuclei (AGN) studied are in the interesting phase of having a recently born radio jet. In this stage, the plasma jets can have the largest impact on the ISM, as also predicted by state-of-the-art simulations. The two targets we present have quite different ages, allowing us to get snapshots of the effects of radio jets as they grow and evolve. Interestingly, both also host powerful quasar emission, making them ideal for studying the full impact of AGN. The largest mass outflow rate of molecular gas is found in a radio galaxy () hosting a newly born radio jet still in the early phase of emerging from an obscuring cocoon of gas and dust. Although the molecular mass outflow rate is high (few hundred), the outflow is limited to the inner few hundred pc region. In a second object (), the jet is larger (a few kpc) and is in a more advanced evolutionary phase. In this object, the distribution of the molecular gas is reminiscent of what is seen, on larger scales, in cool-core clusters hosting radio galaxies. Interestingly, gas deviating from quiescent kinematics (possibly indicating an outflow) is not very prominent, limited only to the very inner region, and has a low mass outflow rate. Instead, on kpc scales, the radio lobes appear associated with depressions in the distribution of the molecular gas. This suggests that the lobes have broken out from the dense nuclear region. However, the AGN does not appear to be able, at present, to stop the star formation observed in this galaxy. These results support the idea that the effects of the radio source start in the very first phases by producing outflows which, however, tend to be limited to the kpc region. After that, the effects turn into producing large-scale bubbles which could, in the long term, prevent the surrounding gas from cooling. Thus, our results provide a way to characterise the effect of radio jets in different phases of their evolution and in different environments, bridging the studies done for radio galaxies in clusters.
Some Levin-Steckin's Type Inequalities for Operator Convex Functions on Hilbert Spaces

A real valued continuous function f on an interval I is said to be operator convex (operator concave) on I if (1.1) f ((1− λ)A+ λB) ≤ (≥) (1− λ) f (A) + λf (B) in the operator order, for all λ ∈ [0, 1] and for every selfadjoint operator A and B on a Hilbert space H whose spectra are contained in I. Notice that a function f is operator concave if −f is operator convex. A real valued continuous function f on an interval I is said to be operator monotone if it is monotone with respect to the operator order, i.e., A ≤ B with Sp (A) , Sp (B) ⊂ I imply f (A) ≤ f (B) . For some fundamental results on operator convex (operator concave) and operator monotone functions, see [12] and the references therein. As examples of such functions, we note that f (t) = t is operator monotone on [0,∞) if and only if 0 ≤ r ≤ 1. The function f (t) = t is operator convex on (0,∞) if either 1 ≤ r ≤ 2 or −1 ≤ r ≤ 0 and is operator concave on (0,∞) if 0 ≤ r ≤ 1. The logarithmic function f (t) = ln t is operator monotone and operator concave on (0,∞). The entropy function f (t) = −t ln t is operator concave on (0,∞). The exponential function f (t) = e is neither operator convex nor operator monotone. In [8] we obtained among others the following Hermite-Hadamard type inequalities for operator convex functions f : I → R
Left properness of flows.

Using Reedy techniques, this paper gives a correct proof of the left properness of the q-model structure of flows. It fixes the preceding proof which relies on an incorrect argument. The last section is devoted to fix some arguments published in past papers coming from this incorrect argument. These Reedy techniques also enable us to study the interactions between the path space functor of flows with various notions of cofibrations. The proofs of this paper are written to work with many convenient categories of topological spaces like the ones of $k$-spaces and of weakly Hausdorff $k$-spaces and their locally presentable analogues, the $\Delta$-generated spaces and the $\Delta$-Hausdorff $\Delta$-generated spaces.
Improving phase change memory performance with data content aware access

Phase change memory (PCM) is a scalable non-volatile memory technology that has low access latency (like DRAM) and high capacity (like Flash). Writing to PCM incurs significantly higher latency and energy penalties compared to reading its content. A prominent characteristic of PCM’s write operation is that its latency and energy are sensitive to the data to be written as well as the content that is overwritten. We observe that overwriting unknown memory content can incur significantly higher latency and energy compared to overwriting known all-zeros or all-ones content. This is because all-zeros or all-ones content is overwritten by programming the PCM cells only in one direction, i.e., using either SET or RESET operations, not both. In this paper, we propose data content aware PCM writes (DATACON), a new mechanism that reduces the latency and energy of PCM writes by redirecting these requests to overwrite memory locations containing all-zeros or all-ones. DATACON operates in three steps. First, it estimates how much a PCM write access would benefit from overwriting known content (e.g., all-zeros, or all-ones) by comprehensively considering the number of set bits in the data to be written, and the energy-latency trade-offs for SET and RESET operations in PCM. Second, it translates the write address to a physical address within memory that contains the best type of content to overwrite, and records this translation in a table for future accesses. We exploit data access locality in work- loads to minimize the address translation overhead. Third, it re-initializes unused memory locations with known all- zeros or all-ones content in a manner that does not interfere with regular read and write accesses. DATACON overwrites unknown content only when it is absolutely necessary to do so. We evaluate DATACON with workloads from state- of-the-art machine learning applications, SPEC CPU2017, and NAS Parallel Benchmarks. Results demonstrate that DATACON improves the effective access latency by 31%, overall system performance by 27%, and total memory system energy consumption by 43% compared to the best of performance-oriented state-of-the-art techniques.
Multidirectional Associative Optimization of Function-Specific Word Representations

We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information about word group membership even in the joint space, and can thereby effectively be applied to a number of tasks reasoning over the SVO structure. We show the robustness and versatility of the proposed framework by reporting state-of-the-art results on the tasks of estimating selectional preference and event similarity. The results indicate that the combinations of representations learned with our task-independent model outperform task-specific architectures from prior work, while reducing the number of parameters by up to 95%.
Antisense derivatives of U7 and other small nuclear RNAs as tools to modify pre-mRNA splicing patterns

The importance of alternative splicing for the diversity of the proteome and the large number of genetic diseases that are due to splicing defects call for methods to modulate alternative splicing decisions. Although splicing can be modulated by antisense oligonucleotides, this approach is confronted with problems of efficient delivery and the need for repeated administrations of large amounts of the oligonucleotides. Therefore we have developed methods allowing us to modulate splicing with the help of modified derivatives of the U7 small nuclear RNA involved in histone RNA 3′ end processing. Its nuclear accumulation as a stable ribonucleoprotein particle makes U7 snRNA especially useful for this purpose. In particular, U7 derivatives containing two tandem antisense sequences directed against targets upstream and downstream of an exon can induce the efficient and specific skipping of that exon. U7 expression cassettes have been successfully introduced into a great number of cell lines, primary cells or tissues with the help of lentiviral and adeno-associated viral vectors. Examples of these therapeutic strategies in the fields of β-thalassemia, Duchenne muscular dystrophy and HIV/AIDS are discussed.
Implications of a Fast Radio Burst from a Galactic Magnetar

A luminous radio burst was recently detected in temporal coincidence with a hard X-ray flare from the Galactic magnetar SGR 1935+2154 with a time and frequency structure consistent with cosmological fast radio bursts (FRBs) and a fluence within a factor of ≲10 of the least energetic extragalactic FRB previously detected. Although active magnetars are commonly invoked FRB sources, several distinct mechanisms have been proposed for generating the radio emission that make different predictions for the accompanying higher-frequency radiation. We show that the properties of the coincident radio and X-ray flares from SGR 1935+2154, including their approximate simultaneity and relative fluence , as well as the duration and spectrum of the X-ray emission, are consistent with extant predictions for the synchrotron maser shock model. Rather than arising from the inner magnetosphere, the X-rays are generated by (incoherent) synchrotron radiation from thermal electrons heated at the same internal shocks that produce the coherent maser emission as ultrarelativistic flare ejecta collides with a slower particle outflow (e.g., as generated by earlier flaring activity) on a radial scale of cm. Although the rate of SGR 1935+2154–like bursts in the local universe is not sufficient to contribute appreciably to the extragalactic FRB rate, the inclusion of an additional population of more active magnetars with stronger magnetic fields than the Galactic population can explain both the FRB rate and the repeating fraction, but only if the population of active magnetars are born at a rate that is at least 2 orders of magnitude lower than that of the SGR 1935+2154–like magnetars. This may imply that the more active magnetar sources are not younger magnetars formed in a similar way to the Milky Way population (e.g., via ordinary supernovae) but are instead formed through more exotic channels, such as superluminous supernovae, accretion-induced collapse, or neutron star mergers.
Diversifying Dialogue Generation with Non-Conversational Text

Neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low-diversity problem when it comes to open-domain dialogue generation. As bland and generic utterances usually dominate the frequency distribution in our daily chitchat, avoiding them to generate more interesting responses requires complex data filtering, sampling techniques or modifying the training objective. In this paper, we propose a new perspective to diversify dialogue generation by leveraging non-conversational text. Compared with bilateral conversations, non-conversational text are easier to obtain, more diverse and cover a much broader range of topics. We collect a large-scale non-conversational corpus from multi sources including forum comments, idioms and book snippets. We further present a training paradigm to effectively incorporate these text via iterative back translation. The resulting model is tested on two conversational datasets from different domains and is shown to produce significantly more diverse responses without sacrificing the relevance with context.
Text-Based Ideal Points

Ideal point models analyze lawmakers’ votes to quantify their political positions, or ideal points. But votes are not the only way to express a political position. Lawmakers also give speeches, release press statements, and post tweets. In this paper, we introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors. We demonstrate the TBIP with two types of politicized text data: U.S. Senate speeches and senator tweets. Though the model does not analyze their votes or political affiliations, the TBIP separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. One benefit of analyzing texts, as opposed to votes, is that the TBIP can estimate ideal points of anyone who authors political texts, including non-voting actors. To this end, we use it to study tweets from the 2020 Democratic presidential candidates. Using only the texts of their tweets, it identifies them along an interpretable progressive-to-moderate spectrum.
Stopover strategies in birds: a review of methods for estimating stopover length

This study categorizes and reviews methods used to estimate stopover length in migratory birds. Stopover length is an important variable in analysing migration strategies. The analysis of the stopover pattern of migratory birds typically attempts to investigate variability in the number of stopovers and in the length of time spent at stopover sites in relation to species, age, sex and body condition. Although the number of capture-recapture studies analysing stopover length has increased substantially, most are analysed using the simple last-first capture method which gives biased estimates. The use of mathematical models to calculate stopover length is uncommon. Most researchers assume homogeneity in stopover length and do not determine the capture probability. Intensive studies are available for many migratory passerines, waders and geese. Some of the data sets are highly standardized and could be reanalysed with new applications and innovative uses of existing techniques, such as the CJS models.
Variation in the reproductive rate of bats

In many respects, bats have relatively slow life histories. However, the reproductive rate of bats (i.e., the proportion of females that reproduce in any breeding season) has not been critically examined. We compiled data on the reproductive rates of bats to test predictions based on life-history theory. Among 257 samples from 103 species, reproductive rate varied considerably and was typically under 100%. Temperate-zone species had significantly lower and more variable reproductive rates than did tropical species. Reproductive rate also varied among families, with species in the Vespertilionidae having particularly high rates. As predicted based on life-history theory, reproductive rate was negatively correlated with longevity, and among vespertilionids, species with larger litters had higher reproductive rates. Thus, the data suggest that bats have relatively slow reproductive rates and, as in other life-history traits, fall at the "slow" end of the fast–slow life-history continuum found among mammals.
ICE-GAN: Identity-aware and Capsule-Enhanced GAN for Micro-Expression Recognition and Synthesis

Micro-expressions can reflect peoples true feelings and motives, which attracts an increasing number of researchers into the studies of automatic facial micro-expression recognition (MER). The detection window of micro-expressions is too short in duration to be perceived by human eye, while their subtle face muscle movements also make MER a challenging task for pattern recognition. To this end, we propose a novel Identity-aware and Capsule-Enhanced Generative Adversarial Network (ICE-GAN), which is adversarially completed with the micro-expression synthesis (MES) task, where synthetic faces with controllable micro-expressions can be produced by the generator with distinguishable identity information to improve the MER performance. Meanwhile, the capsule-enhanced discriminator is optimized to simultaneously detect the authenticity and micro-expression class labels. Our ICE-GAN was evaluated on the 2nd Micro-Expression Grand Challenge (MEGC2019) and outperformed the winner by a significant margin (7%). To the best of our knowledge, we are the first work generating identity-preserving faces with different micro-expressions based on micro-expression datasets only.
Sheaf quantization and intersection of rational Lagrangian immersions

We study rational Lagrangian immersions in a cotangent bundle, based on the microlocal theory of sheaves. We construct a sheaf quantization of a rational Lagrangian immersion and investigate its properties in Tamarkin category. Using the sheaf quantization, we give an explicit bound for the displacement energy and a Betti/cup-length estimate for the number of the intersection points of the immersion and its Hamiltonian image by a purely sheaf-theoretic method.
Comment on "No-Reference Video Quality Assessment Based on the Temporal Pooling of Deep Features"

In Neural Processing Letters 50,3 (2019) a machine learning approach to blind video quality assessment was proposed. It is based on temporal pooling of features of video frames, taken from the last pooling layer of deep convolutional neural networks. The method was validated on two established benchmark datasets and gave results far better than the previous state-of-the-art. In this letter we report the results from our careful reimplementations. The performance results, claimed in the paper, cannot be reached, and are even below the state-of-the-art by a large margin. We show that the originally reported wrong performance results are a consequence of two cases of data leakage. Information from outside the training dataset was used in the fine-tuning stage and in the model evaluation.
CFDNet: a deep learning-based accelerator for fluid simulations

CFD is widely used in physical system design and optimization, where it is used to predict engineering quantities of interest, such as the lift on a plane wing or the drag on a motor vehicle. However, many systems of interest are prohibitively expensive for design optimization, due to the expense of evaluating CFD simulations. To render the computation tractable, reduced-order or surrogate models are used to accelerate simulations while respecting the convergence constraints provided by the higher-fidelity solution. This paper introduces CFDNet - a physical simulation and deep learning coupled framework, for accelerating the convergence of Reynolds Averaged Navier-Stokes simulations. CFDNet is designed to predict the primary physical properties of the fluid including velocity, pressure, and eddy viscosity using a single convolutional neural network at its core. We evaluate CFDNet on a variety of use-cases, both extrapolative and interpolative, where test geometries are observed/not-observed during training. Our results show that CFDNet meets the convergence constraints of the domain-specific physics solver while outperforming it by 1.9 - 7.4X on both steady laminar and turbulent flows. Moreover, we demonstrate the generalization capacity of CFDNet by testing its prediction on new geometries unseen during training. In this case, the approach meets the CFD convergence criterion while still providing significant speedups over traditional domain-only models.
Shrinkage estimation for dose-response modeling in phase II trials with multiple schedules

Recently, phase II trials with multiple schedules (frequency of administrations) have become more popular, for instance in the development of treatments for atopic dermatitis. If the relationship of the dose and response is described by a parametric model, a simplistic approach is to scale doses from different schedules to a common unit and pool all re-scaled doses. However, this approach ignores the potential heterogeneity in dose-response curves between schedules. A more reasonable approach is the partial pooling, i.e. certain parameters of the dose-response curves are shared, while others are allowed to vary. Rather than using schedule-specific fixed-effects, we propose a Bayesian hierarchical model with random-effects to model the between-schedule heterogeneity with regard to certain parameters. Schedule-specific dose-response relationships can then be estimated using shrinkage estimation. Considering Emax models, the proposed method displayed desirable performance in terms of the mean absolute error and the coverage probabilities for the dose-response curve compared to the complete pooling. Furthermore, it outperformed the partial pooling with schedule-specific fixed-effects by producing lower mean absolute error and shorter credible intervals. The methods are illustrated using simulations and a phase II trial example in atopic dermatitis. A publicly available R package, ModStan, is developed to automate the implementation of the proposed method.
Spatial Ecology and Population Genetics of Northern Map Turtles (Graptemys geographica) in Fragmented and Continuous Habitats in Canada

Abstract In riverine systems, the construction of locks and dams may present barriers to movement for aquatic organisms and effectively fragment otherwise continuous populations. We examined the spatial ecology and population genetics of northern map turtles (Graptemys geographica) in the Trent-Severn Waterway (TSW), Ontario, Canada. We hypothesized that the locks and dams on the TSW are fragmenting map turtle habitats and creating barriers to movement, and predicted that turtles from fragmented habitats have significantly reduced home range sizes and daily movements compared to turtles from unfragmented habitats, and that populations on either side of a lock and dam would be genetically distinct. Home ranges and average daily movements were smaller for turtles from fragmented habitats compared with continuous habitats, indicating that locks and dams are restricting mobility. However, populations in fragmented habitats showed none of the predicted genetic consequences of fragmentation (e.g., reduced heterozygosity and allelic richness, differentiation across a barrier). Genetic data may be reflecting historic population structure because only a few turtle generations have passed since the construction of the locks and dams about 100 years ago. Additionally, our genetic data may not be sufficiently robust to detect differentiation at this relatively small geographic scale. Our study highlights the conservation importance of examining multiple facets of a single potential threat, especially for long-lived species that may show differences in short- and long-term effects of fragmentation.