Miracle-dz/UniFAR-FTUs huggingface dataset:
Cluster induced quenching of galaxies in the massive cluster XMMXCS J2215.9−1738 at z ∼ 1.5 traced by enhanced metallicities inside half R200

Aims. Cluster environments at z < 0.5 were found to increase the gas metallicities of galaxies which enter inner regions of the clusters where the density of the intracluster medium is high enough to remove their hot halo gas by ram-pressure stripping effects and to stop the inflow of pristine gas. To extend these studies to z > 1, the most massive clusters known at these redshifts are the sites where these environmental effects should be more pronounced and more easily observed with present day telescopes. Methods. We explore the massive cluster XMMXCS J2215.9−1738 at z ∼ 1.5 with KMOS spectroscopy of Hα and [N II] λ 6584 covering a region that corresponds to about one virial radius. Using published spectroscopic redshifts of 108 galaxies in and around the cluster we computed the location of galaxies in the projected velocity-versus-position phase-space to separate our cluster sample into a virialized region of objects accreted longer ago (roughly inside half R200) and a region of infalling galaxies. We measured oxygen abundances for ten cluster galaxies with detected [N II] λ 6584 lines in the individual galaxy spectra and compared the mass–metallicity relation of the galaxies inside half R200 with the infalling galaxies and a field sample at similar redshifts. Results. We find that the oxygen abundances of individual z ∼ 1.5 star-forming cluster galaxies inside half R200 are comparable, at the respective stellar mass, to the higher local SDSS metallicity values. We compare our measurements with a field galaxy sample from the KMOS3D survey at similar redshifts. We find that the [N II] λ 6584/Hα line ratios inside half R200 are higher by 0.2 dex and that the resultant metallicities of the galaxies in the inner part of the cluster are higher by about 0.1 dex, at a given mass, than the metallicities of infalling galaxies and of field galaxies at z ∼ 1.5. The enhanced metallicities of cluster galaxies at z ∼ 1.5 inside 0.5R200 indicate that the density of the intracluster medium in this massive cluster becomes high enough toward the cluster center such that the ram pressure exceeds the restoring pressure of the hot gas reservoir of cluster galaxies. This can remove the gas reservoir and initiate quenching; although the galaxies continue to form stars, albeit at slightly lower rates, using the available cold gas in the disk which is not stripped.
A Simulation Based Dynamic Evaluation Framework for System-wide Algorithmic Fairness

We propose the use of Agent Based Models (ABMs) inside a reinforcement learning framework in order to better understand the relationship between automated decision making tools, fairness-inspired statistical constraints, and the social phenomena giving rise to discrimination towards sensitive groups. There have been many instances of discrimination occurring due to the applications of algorithmic tools by public and private institutions. Until recently, these practices have mostly gone unchecked. Given the large-scale transformation these new technologies elicit, a joint effort of social sciences and machine learning researchers is necessary. Much of the research has been done on determining statistical properties of such algorithms and the data they are trained on. We aim to complement that approach by studying the social dynamics in which these algorithms are implemented. We show how bias can be accumulated and reinforced through automated decision making, and the possibility of finding a fairness inducing policy. We focus on the case of recidivism risk assessment by considering simplified models of arrest. We find that if we limit our attention to what is observed and manipulated by these algorithmic tools, we may determine some blatantly unfair practices as fair, illustrating the advantage of analyzing the otherwise elusive property with a system-wide model. We expect the introduction of agent based simulation techniques will strengthen collaboration with social scientists, arriving at a better understanding of the social systems affected by technology and to hopefully lead to concrete policy proposals that can be presented to policymakers for a true systemic transformation.
Rate-Flexible Fast Polar Decoders

Polar codes have gained extensive attention during the past few years and recently they have been selected for the next generation of wireless communications standards (5G). Successive-cancellation-based (SC-based) decoders, such as SC list (SCL) and SC flip (SCF), provide a reasonable error performance for polar codes at the cost of low decoding speed. Fast SC-based decoders, such as Fast-SSC, Fast-SSCL, and Fast-SSCF, identify the special constituent codes in a polar code graph off-line, produce a list of operations, store the list in memory, and feed the list to the decoder to decode the constituent codes in order efficiently, thus increasing the decoding speed. However, the list of operations is dependent on the code rate and as the rate changes, a new list is produced, making fast SC-based decoders not rate-flexible. In this paper, we propose a completely rate-flexible fast SC-based decoder by creating the list of operations directly in hardware, with low implementation complexity. We further propose a hardware architecture implementing the proposed method and show that the area occupation of the rate-flexible fast SC-based decoder in this paper is only $\text{38}{\%}$ of the total area of the memory-based base-line decoder when 5G code rates are supported.
A Type-coherent, Expressive Representation as an Initial Step to Language Understanding

A growing interest in tasks involving language understanding by the NLP community has led to the need for effective semantic parsing and inference. Modern NLP systems use semantic representations that do not quite fulfill the nuanced needs for language understanding: adequately modeling language semantics, enabling general inferences, and being accurately recoverable. This document describes underspecified logical forms (ULF) for Episodic Logic (EL), which is an initial form for a semantic representation that balances these needs. ULFs fully resolve the semantic type structure while leaving issues such as quantifier scope, word sense, and anaphora unresolved; they provide a starting point for further resolution into EL, and enable certain structural inferences without further resolution. This document also presents preliminary results of creating a hand-annotated corpus of ULFs for the purpose of training a precise ULF parser, showing a three-person pairwise interannotator agreement of 0.88 on confident annotations. We hypothesize that a divide-and-conquer approach to semantic parsing starting with derivation of ULFs will lead to semantic analyses that do justice to subtle aspects of linguistic meaning, and will enable construction of more accurate semantic parsers.
Detecting Earth-like Biosignatures on Rocky Exoplanets around Nearby Stars with Ground-based Extremely Large Telescopes

As we begin to discover rocky planets in the habitable zone of nearby stars with missions like TESS and CHEOPS, we will need quick advancements on instrumentation and observational techniques that will enable us to answer key science questions, such as What are the atmospheric characteristics of habitable zone rocky planets? How common are Earth-like biosignatures in rocky planets? } How similar or dissimilar are those planets to Earth? Over the next decade we expect to have discovered several Earth-analog candidates, but we will not have the tools to study the atmospheres of all of them in detail. Ground-based ELTs can identify biosignatures in the spectra of these candidate exo-Earths and understand how the planets' atmospheres compare to the Earth at different epochs. Transit spectroscopy, high-resolution spectroscopy, and reflected-light direct imaging on ELTs can identify multiple biosignatures for habitable zone, rocky planets around M stars at optical to near-infrared wavelengths. Thermal infrared direct imaging can detect habitable zone, rocky planets around AFGK stars, identifying ozone and motivating reflected-light follow-up observations with NASA missions like HabEx/LUVOIR. Therefore, we recommend that the Astro2020 Decadal Survey Committee support: (1) the search for Earth-like biosignatures on rocky planets around nearby stars as a key science case; (2) the construction over the next decade of ground-based Extremely Large Telecopes (ELTs), which will provide the large aperture and spatial resolution necessary to start revealing the atmospheres of Earth-analogues around nearby stars; (3) the development of instrumentation that optimizes the detection of biosignatures; and (4) the generation of accurate line lists for potential biosignature gases, which are needed as model templates to detect those molecules.
EXPECTEDf‐VECTOR OF THE POISSON ZERO POLYTOPE AND RANDOM CONVEX HULLS IN THE HALF‐SPHERE

We prove an explicit combinatorial formula for the expected number of faces of the zero polytope of the homogeneous and isotropic Poisson hyperplane tessellation in $\mathbb R^d$. The expected $f$-vector is expressed through the coefficients of the polynomial $$ (1+ (d-1)^2x^2) (1+(d-3)^2 x^2) (1+(d-5)^2 x^2) \ldots. $$ Also, we compute explicitly the expected $f$-vector and the expected volume of the spherical convex hull of $n$ random points sampled uniformly and independently from the $d$-dimensional half-sphere. In the case when $n=d+2$, we compute the probability that this spherical convex hull is a spherical simplex, thus solving an analogue of the Sylvester four-point problem on the half-sphere.
Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems

High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often limits their use in practice for design optimization and experiment planning. It also precludes their use as online models tied directly to accelerator operation. We introduce an approach based on machine learning to create nonlinear, fast-executing surrogate models that are informed by a sparse sampling of the physics simulation. The models are 10^6 to 10^7 times more efficient to execute.We also demonstrate that these models can be reliably used with multi-objective optimization to obtain orders-of-magnitude speedup in initial design studies and experiment planning. For example, we required 132 times fewer simulation evaluations to obtain an equivalent solution for our main test case, and initial studies suggest that between 330 to 550 times fewer simulation evaluations are needed when using an iterative retraining process. Our approach enables new ways for high-fidelity particle accelerator simulations to be used, at comparatively little computational cost.
Heat-to-mechanical energy conversion in graphene: Manifestation of Umklapp enhancement with strain

Conversion of heat-flux from a steady state temperature difference to mechanical vibration is demonstrated in graphene nanoribbons using direct non-equilibrium molecular dynamics. We observe that this effect is independent of the method of imposing the temperature gradient, heat flux, as well as imposed boundary conditions. We propose that simply dividing the nanoribbon in long and short sections using a partially immobilized area will lead to excitation of long-wavelength vibrations into the long section of the nanoribbon. This results in simpler architectures for heat-to-vibration converter devices based on graphene. Furthermore, we observe that applying tensile axial strain to nanoribbons facilitates vibrational instability by reducing the required threshold heat flux or the temperature gradient. Finally, we discuss the role played by Umklapp scattering for physical mechanisms behind these observations.
Low Resource Text Classification with ULMFit and Backtranslation

In computer vision, virtually every state-of-the-art deep learning system is trained with data augmentation. In text classification, however, data augmentation is less widely practiced because it must be performed before training and risks introducing label noise. We augment the IMDB movie reviews dataset with examples generated by two families of techniques: random token perturbations introduced by Wei and Zou [2019] and backtranslation -- translating to a second language then back to English. In low resource environments, backtranslation generates significant improvement on top of the state of-the-art ULMFit model. A ULMFit model pretrained on wikitext103 and then fine-tuned on only 50 IMDB examples and 500 synthetic examples generated by backtranslation achieves 80.6% accuracy, an 8.1% improvement over the augmentation-free baseline with only 9 minutes of additional training time. Random token perturbations do not yield any improvements but incur equivalent computational cost. The benefits of training with backtranslated examples decreases with the size of the available training data. On the full dataset, neither augmentation technique improves upon ULMFit's state of the art performance. We address this by using backtranslations as a form of test time augmentation as well as ensembling ULMFit with other models, and achieve small improvements.
Vaccine Prophylaxis of Pneumococcal Infections in Children under Conditions of Severe Flood in the Amur River Basin

Background Pneumococcal infection being one of the dominant causes of acute respiratory diseases and exacerbation of chronic ones is a serious problem for human health and society. The flood in the Amur river basin in the summer of 2013 created a special zone and risk conditions for the formation of respiratory pathology in the Far-Eastern region of Russia. We aimed to give clinical and epidemiological assessment of the effectiveness of vaccination programs of respiratory viral and pneumococcal infections and generalization of regional experience in the organization of a set of measures aimed at their prevention in the postflood period in the Far-Eastern region. Methods The monitoring program includes children aged 2 to 5 years in the amount of 4988 with risk factors for pneumococcal infection. The pneumococcal conjugate vaccine Prevenar-13 was used for immunization. Data on the incidence of ARVI and pneumonia in children in pre- and postvaccination periods were to be recorded. The indicators and special criteria were used to assess the effectiveness of vaccination. To study the circulation of serovariants of pneumococcus in inflammatory diseases of the respiratory tract and nasopharyngeal carrier, bacteriological and molecular genetic methods (RT-PCR in the mode of multiprime detection) were used. Results Differences in the frequency and range of serovariants of circulating isolates of pneumococcus in the postvaccinal period and in unvaccinated children, elimination of a number of serotypes, and appearance of circulation of nonvaccinated strains were revealed. The incidence of acute respiratory diseases and pneumonia among the vaccinated population for 2 years in the region decreased by 2.5 times. The coefficient of effectiveness of vaccination according to the indicator of morbidity of children with pneumonia reaches 75-100% with direct dependence on the age of children (r=0.98). Conclusion Comparative statistical analysis revealed a high degree of effectiveness of regional programs with the methods of immunoprophylaxis of pneumococcal infections.
Antimicrobial-Resistant Escherichia coli from Environmental Waters in Northern Colorado

Waterborne Escherichia coli are a major reservoir of antimicrobial resistance (AMR), including but not limited to extended-spectrum beta-lactamase (ESBL) and Klebsiella pneumoniae carbapenemase (KPC) mechanisms. This study quantified and described ESBL- and KPC-producing E. coli in Northern Colorado from sewer water, surface water, and influent and effluent wastewater treatment sources. Total detected bacteria and E. coli abundances, and the percentages that contain ESBL and/or KPC, were compared between water sources. Seventy E. coli isolates from the various waters had drug resistance validated with a panel of 17 antibiotics using a broth microdilution assay. The diverse drug resistance observed across E. coli isolates was further documented by polymerase chain reaction of common ESBL genes and functional relatedness by PhenePlate assay-generated dendrograms (n=70). The total E. coli abundance decreased through the water treatment process as expected, yet the percentages of E. coli harboring ESBL resistance were increased (1.70%) in surface water. Whole-genome sequencing analysis was completed for 185 AMR genes in wastewater E. coli isolates and confirmed the presence of diverse AMR gene classes (e.g., beta-lactams and efflux pumps) in isolate genomes. This study completed surveillance of AMR patterns in E. coli that reside in environmental water systems and suggests a role for integrating both phenotypic and genotypic profiling beyond ESBL and KPC mechanisms. AMR screening via multiple approaches may assist in the prevention of drug-resistant E. coli spread from waters to animals and humans.
Few-Shot Adaptive Faster R-CNN

To mitigate the detection performance drop caused by domain shift, we aim to develop a novel few-shot adaptation approach that requires only a few target domain images with limited bounding box annotations. To this end, we first observe several significant challenges. First, the target domain data is highly insufficient, making most existing domain adaptation methods ineffective. Second, object detection involves simultaneous localization and classification, further complicating the model adaptation process. Third, the model suffers from over-adaptation (similar to overfitting when training with a few data example) and instability risk that may lead to degraded detection performance in the target domain. To address these challenges, we first introduce a pairing mechanism over source and target features to alleviate the issue of insufficient target domain samples. We then propose a bi-level module to adapt the source trained detector to the target domain: 1) the split pooling based image level adaptation module uniformly extracts and aligns paired local patch features over locations, with different scale and aspect ratio; 2) the instance level adaptation module semantically aligns paired object features while avoids inter-class confusion. Meanwhile, a source model feature regularization (SMFR) is applied to stabilize the adaptation process of the two modules. Combining these contributions gives a novel few-shot adaptive Faster-RCNN framework, termed FAFRCNN, which effectively adapts to target domain with a few labeled samples. Experiments with multiple datasets show that our model achieves new state-of-the-art performance under both the interested few-shot domain adaptation(FDA) and unsupervised domain adaptation(UDA) setting.
Efficient Algorithms for Geometric Partial Matching

Let $A$ and $B$ be two point sets in the plane of sizes $r$ and $n$ respectively (assume $r \leq n$), and let $k$ be a parameter. A matching between $A$ and $B$ is a family of pairs in $A \times B$ so that any point of $A \cup B$ appears in at most one pair. Given two positive integers $p$ and $q$, we define the cost of matching $M$ to be $c(M) = \sum_{(a, b) \in M}\|{a-b}\|_p^q$ where $\|{\cdot}\|_p$ is the $L_p$-norm. The geometric partial matching problem asks to find the minimum-cost size-$k$ matching between $A$ and $B$. We present efficient algorithms for geometric partial matching problem that work for any powers of $L_p$-norm matching objective: An exact algorithm that runs in $O((n + k^2) {\mathop{\mathrm{polylog}}} n)$ time, and a $(1 + \varepsilon)$-approximation algorithm that runs in $O((n + k\sqrt{k}) {\mathop{\mathrm{polylog}}} n \cdot \log\varepsilon^{-1})$ time. Both algorithms are based on the primal-dual flow augmentation scheme; the main improvements involve using dynamic data structures to achieve efficient flow augmentations. With similar techniques, we give an exact algorithm for the planar transportation problem running in $O(\min\{n^2, rn^{3/2}\} {\mathop{\mathrm{polylog}}} n)$ time.
Heating in Nanophotonic Traps for Cold Atoms

Laser-cooled atoms that are trapped and optically interfaced with light in nanophotonic waveguides are a powerful platform for fundamental research in quantum optics as well as for applications in quantum communication and quantum information processing. Ever since the first realization of such a hybrid quantum nanophotonic, heating rates of the atomic motion observed in various experimental settings have typically been exceeding those in comparable free-space optical microtraps by about three orders of magnitude. This excessive heating is a roadblock for the implementation of certain protocols and devices. Its origin has so far remained elusive and, at the typical atom-surface separations of less than an optical wavelength encountered in nanophotonic traps, numerous effects may potentially contribute to atom heating. Here, we theoretically describe the effect of mechanical vibrations of waveguides on guided light fields and provide a general theory of particle-phonon interaction in nanophotonic traps. We test our theory by applying it to the case of laser-cooled cesium atoms in nanofiber-based two-color optical traps. We find excellent quantitative agreement between the predicted heating rates and experimentally measured values. Our theory predicts that, in this setting, the dominant heating process stems from the optomechanical coupling of the optically trapped atoms to the continuum of thermally occupied flexural mechanical modes of the waveguide structure. Beyond unraveling the long-standing riddle of excessive heating in nanofiber-based atom traps, we also study the dependence of the heating rates on the relevant system parameters. Our findings allow us to propose several strategies for minimizing the heating. Finally, our findings are also highly relevant for optomechanics experiments with dielectric nanoparticles that are optically trapped close to nanophotonic waveguides.
Implicit regularization via hadamard product over-parametrization in high-dimensional linear regression

We consider Hadamard product parametrization as a change-of-variable (over-parametrization) technique for solving least square problems in the context of linear regression. Despite the non-convexity and exponentially many saddle points induced by the change-of-variable, we show that under certain conditions, this over-parametrization leads to implicit regularization: if we directly apply gradient descent to the residual sum of squares with sufficiently small initial values, then under proper early stopping rule, the iterates converge to a nearly sparse rate-optimal solution with relatively better accuracy than explicit regularized approaches. In particular, the resulting estimator does not suffer from extra bias due to explicit penalties, and can achieve the parametric root-$n$ rate (independent of the dimension) under proper conditions on the signal-to-noise ratio. We perform simulations to compare our methods with high dimensional linear regression with explicit regularizations. Our results illustrate advantages of using implicit regularization via gradient descent after over-parametrization in sparse vector estimation.