Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracing the evolution of dust-obscured activity using sub-millimetre galaxy populations from STUDIES and AS2UDS

We analyse the physical properties of 121 SNR ≥ 5 sub-millimetre galaxies (SMGs) from the STUDIES 450 μm survey. We model their UV-to-radio spectral energy distributions using magphys+photo-z and compare the results to similar modelling of 850 μm-selected SMG sample from AS2UDS, to understand the fundamental physical differences between the two populations at the observed depths. The redshift distribution of the 450-μm sample has a median of z = 1.85 ± 0.12 and can be described by strong evolution of the far-infrared luminosity function. The fainter 450-μm sample has ∼14 times higher space density than the brighter 850-μm sample at z ≲ 2, and a comparable space density at z = 2–3, before rapidly declining, suggesting LIRGs are the main obscured population at z ∼ 1–2, while ULIRGs dominate at higher redshifts. We construct rest-frame ∼180-μm-selected and dust-mass-matched samples at z = 1–2 and z = 3–4 from the 450 and 850-μm samples, respectively, to probe the evolution of a uniform sample of galaxies spanning the cosmic noon era. Using far-infrared luminosity, dust masses, and an optically thick dust model, we suggest that higher redshift sources have higher dust densities due to inferred dust continuum sizes which are roughly half of those for the lower redshift population at a given dust mass, leading to higher dust attenuation. We track the evolution in the cosmic dust mass density and suggest that the dust content of galaxies is governed by a combination of both the variation of gas content and dust destruction time-scale.
Training independent subnetworks for robust prediction

Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over the original network. However, these methods still require multiple forward passes for prediction, leading to a significant computational cost. In this work, we show a surprising result: the benefits of using multiple predictions can be achieved `for free' under a single model's forward pass. In particular, we show that, using a multi-input multi-output (MIMO) configuration, one can utilize a single model's capacity to train multiple subnetworks that independently learn the task at hand. By ensembling the predictions made by the subnetworks, we improve model robustness without increasing compute. We observe a significant improvement in negative log-likelihood, accuracy, and calibration error on CIFAR10, CIFAR100, ImageNet, and their out-of-distribution variants compared to previous methods.
Symmetric Helmholtz Fermi-surface harmonics for an optimal representation of anisotropic quantities on the Fermi surface: Application to the electron-phonon problem

We outline a numerical procedure to incorporate the crystal symmetries in the Helmholtz Fermi-surface harmonics basis set, which are the solutions of the Helmholtz equation defined on the Fermi surface. This improvement allows for an optimal representation of anisotropic quantities defined on the Fermi surface in terms of few symmetric elements of the set. We demonstrate the general validity of our approach by identifying the fully symmetric Helmholtz Fermi-surface harmonics subset for several representative systems with different crystal structures, namely, FCC-Cu, HEX-MgB$_2$, and BCC-YH$_6$. Furthermore, we illustrate the potential of the method applied to the electron-phonon problem, showing that the anisotropic electron-phonon mass-enhancement parameter $\lambda_{\bf k}$ can be represented to high accuracy by a handful of coefficients. This works as an effective filter, paving the way for a reduction of several orders of magnitude in the computation of superconductivity, impurity problems, or any other Fermi surface dependent property of metals from first principles.
Late-time Radio and Millimeter Observations of Superluminous Supernovae and Long Gamma-Ray Bursts: Implications for Central Engines, Fast Radio Bursts, and Obscured Star Formation

We present the largest and deepest late-time radio and millimeter survey to date of superluminous supernovae (SLSNe) and long-duration gamma-ray bursts (LGRBs) to search for associated nonthermal synchrotron emission. Using the Karl G. Jansky Very Large Array (VLA) and the Atacama Large Millimeter/submillimeter Array (ALMA), we observed 43 sources at 6 and 100 GHz on a timescale of ∼ 1–19 yr post-explosion. We do not detect radio/millimeter emission from any of the sources, with the exception of a 6 GHz detection of PTF10hgi, as well as the detection of 6 GHz emission near the location of the SLSN PTF12dam, which we associate with its host galaxy. We use our data to place constraints on central engine emission due to magnetar wind nebulae and off-axis relativistic jets. We also explore nonrelativistic emission from the SN ejecta, and place constraints on obscured star formation in the host galaxies. In addition, we conduct a search for fast radio bursts (FRBs) from some of the sources using VLA phased-array observations; no FRBs are detected to a limit of 16 mJy (7σ; 10 ms duration) in about 40 minutes on source per event. A comparison to theoretical models suggests that continued radio monitoring may lead to detections of persistent radio emission on timescales of ≳ a decade.
Origin of phase stability in Fe with long-period stacking order as an intermediate phase in cyclic 
γ−ε
 martensitic transformation

A class of Fe-Mn-Si based alloys was found to exhibit a reversible martensitic transformation between the $\gamma$ ($fcc$) and $\epsilon$ ($hcp$) phases. During the deformation-induced $\gamma$-$\epsilon$ transformation, a new phase was identified that is different from the $epsilon$-phase, where the electron diffraction spots are located at the 1/3 position corresponding to the $hcp$ (2H) phase, which suggests long-period stacking order (LPSO). To understand the actual stacking pattern and discuss the possible realization of a LPSO phase as the intermediate phase between the $\gamma$ and $\epsilon$ phases, the phase stability of various structural polytypes of iron was examined using first-principles calculations with a spin-polarized form of the generalized gradient approximation in density functional theory. We found that an antiferromagnetic ordered 6H$_2$ structure is the most stable among candidate LPSO structures and is energetically close to the $epsilon$ phase, which suggests that the observed LPSO-like phase adopts the 6H$_2$ structure. We determined that the origin of the phase stability can be attributed to the depth of the deep valley in the density of states close to the Fermi level.
Prediction and model-assisted estimation of diameter distributions using Norwegian national forest inventory and airborne laser scanning data

Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data in an 8.7 Mha study area and compared the predictive performance of parameter prediction methods using linear-mixed effects (PPM) and generalized linear-mixed models (GLM), and a k nearest neighbor (NN) approach. With PPM and GLM, it was assumed that the data follow a truncated Weibull distribution. While GLM resulted in slightly smaller errors than PPM, both were clearly outperformed by NN. We applied NN to study the variance of model-assisted (MA) estimates of the DBH distribution in the whole study area. The MA estimator yielded greater than or almost equal efficiencies as the direct estimator in the 2 cm DBH classes (6, 8,., 50 cm) where relative efficiencies (REs) varied in the range of 0.97$-$1.63. RE was largest in the DBH classes $\leq$ 10 cm and decreased towards the right tail of the distribution. A forest mask and tree species map introduced further uncertainty beyond the DBH distribution model, which reduced REs to 0.97$-$1.50.
Vokenization: Improving Language Understanding via Contextualized, Visually-Grounded Supervision

Humans learn language by listening, speaking, writing, reading, and also, via interaction with the multimodal real world. Existing language pre-training frameworks show the effectiveness of text-only self-supervision while we explore the idea of a visually-supervised language model in this paper. We find that the main reason hindering this exploration is the large divergence in magnitude and distributions between the visually-grounded language datasets and pure-language corpora. Therefore, we develop a technique named "vokenization" that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images (which we call "vokens"). The "vokenizer" is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks such as GLUE, SQuAD, and SWAG. Code and pre-trained models publicly available at this https URL
Measuring Visual Generalization in Continuous Control from Pixels

Self-supervised learning and data augmentation have significantly reduced the performance gap between state and image-based reinforcement learning agents in continuous control tasks. However, it is still unclear whether current techniques can face a variety of visual conditions required by real-world environments. We propose a challenging benchmark that tests agents' visual generalization by adding graphical variety to existing continuous control domains. Our empirical analysis shows that current methods struggle to generalize across a diverse set of visual changes, and we examine the specific factors of variation that make these tasks difficult. We find that data augmentation techniques outperform self-supervised learning approaches and that more significant image transformations provide better visual generalization \footnote{The benchmark and our augmented actor-critic implementation are open-sourced @ this https URL)
Notes on Gramirieae: Bambusoideae in Hong Kong

This paper reports on two new species discovered in our study of the bamboos of Hong Kong, as well as on a new combination and a new synonym. Our collections are designated by the title Nan-Zhu, for the Collections of Bamboos of Southern China. We wish to thank the Directors and staff members of the Royal Botanic Gardens at Kew, the Agriculture and Fisheries Department, the Kadoorie Agricultural Aid Association, and The Chinese University of Hong Kong (particularly Dr Y.C. Kong) for their kind assistance. We use CUHK as an abbreviation for Biology Dept., The Chinese University of Hong Kong which is not listed in ed. 7 of Index Herbariorum.
On the GHKS compactification of the moduli space of K3 surfaces of degree two.

We investigate a toroidal compactification of the moduli space of K3 surfaces of degree $2$ originating from the program formulated by Gross-Hacking-Keel-Siebert. This construction uses Dolgachev's formulation of mirror symmetry and the birational geometry of the mirror family. Our main result in an analysis of the toric fan. For this we use the methods developed by two of us in a previous paper.
The Effects of Biotic Additives on Growth Performance and Meat Qualities in Broiler Chickens

Commercial broiler chickens were treated with five diets containing probiotics (Bacillus subtilis), prebiotics (mannan oligosaccharide-MOS), synbiotics (Saccharomyces cerevisiae, Lactobacillus acidophilus, Lactobacillus casei, Bifidobacterium bifidum, MOS and FOS (fructoligosaccharides), Avilamycin, or a control treatment (no additives). Performance parameters including total weight, daily weight gain, feed intake, viability production efficiency index and yield of carcasses and cuts were evaluated. In addition, meat quality parameters including the proportion of PSE meat (Pale, Soft, Exudative) and lipid oxidation were measured. The results indicated that the biotic treatments did not cause significant differences in any of the parameters evaluated. With regard to the meat quality, birds fed biotic diets showed a reduction in the development of PSE meat and also a decrease in lipid oxidation. These additives are therefore nutritionally feasible replacements for growth promoters and the animal husbandry indices of animals treated with these additives were similar to those of animals fed the normal rations and the use of additives contributed to improvements in the meat quality.
Are all negatives created equal in contrastive instance discrimination?

Self-supervised learning has recently begun to rival supervised learning on computer vision tasks. Many of the recent approaches have been based on contrastive instance discrimination (CID), in which the network is trained to recognize two augmented versions of the same instance (a query and positive) while discriminating against a pool of other instances (negatives). The learned representation is then used on downstream tasks such as image classification. Using methodology from MoCo v2 (Chen et al., 2020), we divided negatives by their difficulty for a given query and studied which difficulty ranges were most important for learning useful representations. We found a minority of negatives -- the hardest 5% -- were both necessary and sufficient for the downstream task to reach nearly full accuracy. Conversely, the easiest 95% of negatives were unnecessary and insufficient. Moreover, the very hardest 0.1% of negatives were unnecessary and sometimes detrimental. Finally, we studied the properties of negatives that affect their hardness, and found that hard negatives were more semantically similar to the query, and that some negatives were more consistently easy or hard than we would expect by chance. Together, our results indicate that negatives vary in importance and that CID may benefit from more intelligent negative treatment.
Heterogeneous Graph Collaborative Filtering using Textual Information

Due to the development of graph neural network models, like graph convolutional network (GCN), graph-based representation learning methods have made great progress in recommender systems. However, the data sparsity is still a challenging problem that graph-based methods are confronted with. Recent works try to solve this problem by utilizing the side information. In this paper, we introduce easily accessible textual information to alleviate the negative effects of data sparsity. Specifically, to incorporate with rich textual knowledge, we utilize a pre-trained context-awareness natural language processing model to initialize the embeddings of text nodes. By a GCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can finally be enriched by the textual knowledge. The matching function used by most graph-based representation learning methods is the inner product, this linear operation can not fit complex semantics well. We design a predictive network, which can combine the graph-based representation learning with the matching function learning, and demonstrate that this predictive architecture can gain significant improvements. Extensive experiments are conducted on three public datasets and the results verify the superior performance of our method over several baselines.
Exploring KSZ velocity reconstruction with N-body simulations and the halo model

KSZ velocity reconstruction is a recently proposed method for mapping the largest-scale modes of the universe, by applying a quadratic estimator v̂ r to the small-scale CMB and a galaxy catalog. We implement kSZ velocity reconstruction in an N-body simulation pipeline and explore its properties. We find that the reconstruction noise can be larger than the analytic prediction which is usually assumed. We revisit the analytic prediction and find additional noise terms which explain the discrepancy. The new terms are obtained from a six-point halo model calculation, and are analogous to the N (1) and N (3/2) biases in CMB lensing. We implement an MCMC pipeline which estimates fNL from N-body kSZ simulations, and show that it recovers unbiased estimates of fNL , with statistical errors consistent with a Fisher matrix forecast. Overall, these results confirm that kSZ velocity reconstruction will be a powerful probe of cosmology in the near future, but new terms should be included in the noise power spectrum.
Influence Maximization Based on Dynamic Personal Perception in Knowledge Graph

Viral marketing on social networks, also known as Influence Maximization (IM), aims to select k users for the promotion of a target item by maximizing the total spread of their influence. However, most previous works on IM do not explore the dynamic user perception of promoted items in the process. In this paper, by exploiting the knowledge graph (KG) to capture dynamic user perception, we formulate the problem of Influence Maximization based on Dynamic Personal Perception (IMDPP) that considers user preferences and social influence reflecting the impact of relevant item adoptions. We prove the hardness of IMDPP and design an approximation algorithm, named Dynamic perception for seeding in target markets (Dysim), by exploring the concepts of dynamic reachability, target markets, and substantial influence to select and promote a sequence of relevant items. We evaluate the performance of Dysim in comparison with the state-of-the-art approaches using real social networks with real KGs. The experimental results show that Dysim effectively achieves at least 6 times of influence spread in large datasets over the state-of-the-art approaches.
Privacy Concerns Regarding Occupant Tracking in Smart Buildings

Tracking of occupants within buildings has become a topic of interest in the past decade. Occupant tracking has been used in the public safety, energy conservation, and marketing fields. Various methods have been demonstrated which can track people outside of and inside buildings; including GPS, visual-based tracking using surveillance cameras, and vibration-based tracking using sensors such as accelerometers. In this work, those main systems for tracking occupants are compared and contrasted for the levels of detail they give about where occupants are, as well as their respective privacy concerns and how identifiable the tracking information collected is to a specific person. We discuss a case study using vibrations sensors mounted in Virginia Tech's Goodwin Hall that was recently conducted, demonstrating that similar levels of accuracy in occupant localization can be achieved to current methods, and highlighting the amount of identifying information in the vibration signals dataset. Finally, a method of transforming the vibration data to preserve occupant privacy was proposed and tested on the dataset. The results indicate that our proposed method has successfully resulted in anonymizing the occupant's gender information which was previously identifiable from the vibration data, while minimally impacting the localization accuracy achieved without anonymization.
The X-SHOOTER/ALMA Sample of Quasars in the Epoch of Reionization. I. NIR Spectral Modeling, Iron Enrichment, and Broad Emission Line Properties

We present X-SHOOTER near-IR spectroscopy of a large sample of 38 luminous (M1450 = −29.0 to −24.4) quasars at 5.78 < z < 7.54, which have complementary [C ii]158μm observations from ALMA. This X-SHOOTER/ALMA sample provides us with the most comprehensive view of reionization-era quasars to date, allowing us to connect the quasar properties with those of its host galaxy. In this work we introduce the sample, discuss data reduction and spectral fitting, and present an analysis of the broad emission line properties. The measured Fe ii/Mg ii flux ratio suggests that the broad-line regions of all quasars in the sample are already enriched in iron. We also find the Mg ii line to be on average blueshifted with respect to the [C ii] redshift with a median of −391 km s−1. A significant correlation between the Mg ii−[C ii]158μm and C iv−[C ii]158μm velocity shifts indicates a common physical origin. Furthermore, we fRequently detect large C iv–Mg ii emission line velocity blueshifts in our sample with a median value of −1848 km s−1. While we find all other broad emission line properties not to be evolving with redshift, the median C iv–Mg ii blueshift is much larger than found in low-redshift, luminosity-matched quasars (−800 km s−1). Dividing our sample into two redshift bins, we confirm an increase of the average C iv–Mg ii blueshift with increasing redshift. Future observations of the rest-frame optical spectrum with the James Webb Space Telescope will be instrumental in further constraining the possible evolution of quasar properties in the epoch of reionization.
Better Patch Stitching for Parametric Surface Reconstruction

Recently, parametric mappings have emerged as highly effective surface representations, yielding low reconstruction error. In particular, the latest works represent the target shape as an atlas of multiple mappings, which can closely encode object parts. Atlas representations, however, suffer from one major drawback: The individual mappings are not guaranteed to be consistent, which results in holes in the reconstructed shape or in jagged surface areas.We introduce an approach that explicitly encourages global consistency of the local mappings. To this end, we introduce two novel loss terms. The first term exploits the surface normals and requires that they remain locally consistent when estimated within and across the individual mappings. The second term further encourages better spatial configuration of the mappings by minimizing novel stitching error. We show on standard benchmarks that the use of normal consistency requirement outperforms the baselines quantitatively while enforcing better stitching leads to much better visual quality of the reconstructed objects as compared to the state-of-the-art.
Residency and behavioural rhythmicity of ballan wrasse (Labrus bergylta) and rays (Raja spp.) captured in Portelet Bay, Jersey: implications for Marine Protected Area design

We monitored the long-term residency of reef-associated ballan wrasse and sand-dwelling rays captured at the site of a potential future Marine Protected Area (MPA: Portelet Bay, Jersey) by implanting them with small transmitters and deploying underwater receivers inside the bay. Individual fish were detected at Portelet Bay for up to 618 days, but there were species-specific differences in residency and detection patterns. Ballan wrasse were year-round residents at the study site where they exhibited distinct, rhythmic, diel, tidal and seasonal patterns of behaviour, whereas rays were occasional visitors to Portelet Bay with no discernible pattern to their visits. Results indicate relatively small MPAs (<0.5 km2) that with suitable habitat could provide effective, long-term protection for ballan wrasse, but would likely be of little conservation benefit for rays. Our findings emphasize the importance of quantifying fish movements when planning MPAs which intend to protect multi-species assemblages of coastal fishes.