Miracle-dz/UniFAR-FTUs huggingface dataset:
Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological implications from two decades of spectroscopic surveys at the Apache Point Observatory

We present the cosmological implications from final measurements of clustering using galaxies, quasars, and Ly$\alpha$ forests from the completed Sloan Digital Sky Survey (SDSS) lineage of experiments in large-scale structure. These experiments, composed of data from SDSS, SDSS-II, BOSS, and eBOSS, offer independent measurements of baryon acoustic oscillation (BAO) measurements of angular-diameter distances and Hubble distances relative to the sound horizon, $r_d$, from eight different samples and six measurements of the growth rate parameter, $f\sigma_8$, from redshift-space distortions (RSD). This composite sample is the most constraining of its kind and allows us to perform a comprehensive assessment of the cosmological model after two decades of dedicated spectroscopic observation. We show that the BAO data alone are able to rule out dark-energy-free models at more than eight standard deviations in an extension to the flat, $\Lambda$CDM model that allows for curvature. When combined with Planck Cosmic Microwave Background (CMB) measurements of temperature and polarization the BAO data provide nearly an order of magnitude improvement on curvature constraints. The RSD measurements indicate a growth rate that is consistent with predictions from Planck primary data and with General Relativity. When combining the results of SDSS BAO and RSD with external data, all multiple-parameter extensions remain consistent with a $\Lambda$CDM model. Regardless of cosmological model, the precision on $\Omega_\Lambda$, $H_0$, and $\sigma_8$, remains at roughly 1\%, showing changes of less than 0.6\% in the central values between models. The inverse distance ladder measurement under a o$w_0w_a$CDM yields $H_0= 68.20 \pm 0.81 \, \rm km\, s^{-1} Mpc^{-1}$, remaining in tension with several direct determination methods. (abridged)
Maxillary Sinus Natural Killer/T-Cell Extranodal Lymphoma Mimicking a Dentoalveolar Abscess

Extranodal natural killer/T-cell lymphoma (ENKL) is an aggressive and infrequent malignant neoplasm. Early sinonasal ENKL clinical symptomatology is often not specific which can mimic several clinical odontogenic processes such as dentoalveolar abscesses. A 41-year-old female was referred to our institution due to facial pain with skin rash, fever, and intraoral swelling in the left side of the maxillary region. Computed tomography (CT) revealed a soft tissue hypodense area in the left side with bone discontinuity in anterior and lateral maxillary sinuses. Initial laboratorial tests showed no alterations on hemogram, coagulation profile, and immune phenotype (CD3+/CD4+). However, the lesion progressed fastly showing an evident growth, so it was decided that an intraoral biopsy should be performed. The diagnosis was sinonasal ENKLs. This case is an example that the rare T-cell lymphoma can mimic an odontogenic lesion, thus puzzling the clinician. Fortunately, the rapid growth of the lesion prompted the correct diagnosis and early treatment.
Modulatory antibiotic activity and chemical composition of hydroalcoholic extract of Croton campestris

Croton campestris A. St.-Hill. , known as "Velame do campo" (Euphorbiaceae), native of the Brazilian Northeastern region, is used in the popular medicine as decoction against several diseases as inflammations, hematological disturbances, flu and gastritis. The study analyzed the chemical composition and the antibacterial activity against bacterial strains ofEscherichia coli and Staphylococcus aureus of the hydroalcoholic extract of the leaves of the C. campestris (HEFCC). The inhibition of the bacterial growth was demonstrated for the bacteria tested with MIC values ≥ 1024 µg/ml. When combined with aminoglycosides (gentamicin, kanamycin, amikacin and neomycin), the extract demonstrated synergistic effect. This result showed that HEFCC can be used as promisor font of new antimicrobial compounds for pathogenic diseases. Key words: Croton campestris, antimicrobial activity, modulatory aminoglycoside effect, antimicrobial activity, antibiotic, synergism.
Hyperparameter Selection for Offline Reinforcement Learning

Offline reinforcement learning (RL purely from logged data) is an important avenue for deploying RL techniques in real-world scenarios. However, existing hyperparameter selection methods for offline RL break the offline assumption by evaluating policies corresponding to each hyperparameter setting in the environment. This online execution is often infeasible and hence undermines the main aim of offline RL. Therefore, in this work, we focus on \textit{offline hyperparameter selection}, i.e. methods for choosing the best policy from a set of many policies trained using different hyperparameters, given only logged data. Through large-scale empirical evaluation we show that: 1) offline RL algorithms are not robust to hyperparameter choices, 2) factors such as the offline RL algorithm and method for estimating Q values can have a big impact on hyperparameter selection, and 3) when we control those factors carefully, we can reliably rank policies across hyperparameter choices, and therefore choose policies which are close to the best policy in the set. Overall, our results present an optimistic view that offline hyperparameter selection is within reach, even in challenging tasks with pixel observations, high dimensional action spaces, and long horizon.
BRP-NAS: Prediction-based NAS using GCNs

Neural architecture search (NAS) enables researchers to automatically explore broad design spaces in order to improve efficiency of neural networks. This efficiency is especially important in the case of on-device deployment, where improvements in accuracy should be balanced out with computational demands of a model. In practice, performance metrics of model are computationally expensive to obtain. Previous work uses a proxy (e.g., number of operations) or a layer-wise measurement of neural network layers to estimate end-to-end hardware performance but the imprecise prediction diminishes the quality of NAS. To address this problem, we propose BRP-NAS, an efficient hardware-aware NAS enabled by an accurate performance predictor-based on graph convolutional network (GCN). What is more, we investigate prediction quality on different metrics and show that sample efficiency of the predictor-based NAS can be improved by considering binary relations of models and an iterative data selection strategy. We show that our proposed method outperforms all prior methods on NAS-Bench-101, NAS-Bench-201 and DARTS. Finally, to raise awareness of the fact that accurate latency estimation is not a trivial task, we release LatBench -- a latency dataset of NAS-Bench-201 models running on a broad range of devices.
Security, Availability, and Multiple Information Sources: Exploring Update Behavior of System Administrators

Experts agree that keeping systems up to date is a powerful security measure. Previous work found that users sometimes explicitly refrain from performing timely updates, e.g., due to bad experiences which has a negative impact on end-user security. Another important user group has been investigated less extensively: system administrators, who are responsible for keeping complex and heterogeneous system landscapes available and secure. In this paper, we sought to understand administrators’ behavior, experiences, and attitudes regarding updates in a corporate environment. Based on the results of an interview study, we developed an online survey and quantified common practices and obstacles (e.g., downtime or lack of information about updates). The findings indicate that even experienced administrators struggle with update processes as the consequences of an update are sometimes hard to assess. Therefore, we argue that more usable monitoring and update p
Understanding and Diagnosing Vulnerability under Adversarial Attacks

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks. Currently, there is no clear insight into how slight perturbations cause such a large difference in classification results and how we can design a more robust model architecture. In this work, we propose a novel interpretability method, InterpretGAN, to generate explanations for features used for classification in latent variables. Interpreting the classification process of adversarial examples exposes how adversarial perturbations influence features layer by layer as well as which features are modified by perturbations. Moreover, we design the first diagnostic method to quantify the vulnerability contributed by each layer, which can be used to identify vulnerable parts of model architectures. The diagnostic results show that the layers introducing more information loss tend to be more vulnerable than other layers. Based on the findings, our evaluation results on MNIST and CIFAR10 datasets suggest that average pooling layers, with lower information loss, are more robust than max pooling layers for the network architectures studied in this paper.
Reconstruction of Events Recorded with the Surface Detector of the Pierre Auger Observatory

Cosmic rays arriving at Earth collide with the upper parts of the atmosphere, thereby inducing extensive air showers. When secondary particles from the cascade arrive at the ground, they are measured by surface detector arrays. We describe the methods applied to the measurements of the surface detector of the Pierre Auger Observatory to reconstruct events with zenith angles less than $60^\circ$ using the timing and signal information recorded using the water-Cherenkov detector stations. In addition, we assess the accuracy of these methods in reconstructing the arrival directions of the primary cosmic ray particles and the sizes of the induced showers.
Sequential Explanations with Mental Model-Based Policies

The act of explaining across two parties is a feedback loop, where one provides information on what needs to be explained and the other provides an explanation relevant to this information. We apply a reinforcement learning framework which emulates this format by providing explanations based on the explainee's current mental model. We conduct novel online human experiments where explanations generated by various explanation methods are selected and presented to participants, using policies which observe participants' mental models, in order to optimize an interpretability proxy. Our results suggest that mental model-based policies (anchored in our proposed state representation) may increase interpretability over multiple sequential explanations, when compared to a random selection baseline. This work provides insight into how to select explanations which increase relevant information for users, and into conducting human-grounded experimentation to understand interpretability.
TUDataset: A collection of benchmark datasets for learning with graphs

Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUDataset for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at this http URL. The experiments are fully reproducible from the code available at this http URL.
Silver Exposure Causes Transferable Defects of Phenotypes and Behaviors in Nematode Caenorhabditis elegans

Silver, as a global pollutant, is severely harmful to human health. However, whether the multi-biological toxicities of silver exposure could be transferred from exposed animals to their progeny has not been clarified. In the present study, we explored the model organism, nematode Caenorhabditis elegans, to analyze the multiple toxicities of silver exposure and their possibly transferable properties. Endpoints of life span, body size, brood size, generation time, body bend and head thrash were used for the evaluation of silver toxicity. High concentrations (75 and 200 μM) of silver exposure caused multi-biological defects of life span, development, reproduction and locomotion behavior, whereas low concentration (2.5 μM) of silver exposure did not resulted in severe life span defects. Moreover, most of these multi-biological toxicities could be transferred from exposed animals to their progeny at different concentrations of silver (2.5, 75 and 200 μM). Only the body bend defects could be largely recovered .
Improving key rates of the unbalanced phase-encoded BB84 protocol using the flag-state squashing model

All phase-encoded BB84 implementations have signal states with unbalanced amplitudes in practice. Thus, the original security analyses a priori do not apply to them. Previous security proofs use signal tagging of multi-photon pulses to recover the behaviour of regular BB84. This is overly conservative, as for unbalanced signals, the photon-number splitting attack does not leak full information to Eve. In this work, we exploit the flag-state squashing model to preserve some parts of the multi-photon generated private information in our analysis. Using a numerical proof technique, we obtain significantly higher key rates compared with previously published results in the low-loss regime. It turns out that the usual scenario of untrusted dark counts runs into conceptual difficulties in some parameter regime. Thus, we discuss the trusted dark count scenario in this paper as well. We also report a gain in key rates when part of the total loss is known to be induced by a trusted device. We highlight that all these key rate improvements can be achieved without modification of the experimental setup.
Drop impact on wetted walls: An analytical solution for modelling the crown spreading based on stagnation-point flow

An analytical solution is proposed to predict the crown propagation, generated by a single droplet impact on wetted walls. This approach enables a smooth transition from the inertia-driven to the viscous-controlled regime of crown propagation. The modelling strategy is based on the stagnation-point flow, because it resembles closely the hydrodynamic flow in the lamella and offers two main advantages. First, it allows a simple estimation of the wall-film thinning rate, caused by the impulse transfer from the impacting droplet to the wall film. Second, thanks to the self-similarity of the solution, it enables a straightforward estimation of momentum losses during film spreading along the wall. By incorporating this estimation into existing inviscid models, an excellent agreement with experiments is found during the entire crown elevation phase. In general, the analysis shows that momentum losses due to viscous effects cannot be neglected during a significant portion of crown propagation, particularly for thin wall films. The proposed methodology paves the way for predicting the inception of crown bottom breakup (CBB). In this case, the crown lamella disintegrates directly at its base due to the spontaneous creation of holes that create a web-like structure in the lamella prior to its break-up. Our theoretical analysis shows that this premature break-up of the crown lamella is associated to local instability effects, caused by the unbalance between inertial forces and surface tension.
Sparse Linear Networks with a Fixed Butterfly Structure: Theory and Practice

Fast Fourier transform, Wavelets, and other well-known transforms in signal processing have a structured representation as a product of sparse matrices which are referred to as butterfly structures. Research in the recent past have used such structured linear networks along with randomness as pre-conditioners to improve the computational performance of large scale linear algebraic operations. With the advent of deep learning and AI and the computational efficiency of such structured matrices, it is natural to study sparse linear deep networks in which the location of the non-zero weights are predetermined by the butterfly structure. This work studies, both theoretically and empirically, the feasibility of training such networks in different scenarios. Unlike convolutional neural networks, which are structured sparse networks designed to recognize local patterns in lattices representing a spatial or a temporal structure, the butterfly architecture used in this work can replace any dense linear operator with a gadget consisting of a sequence of logarithmically (in the network width) many sparse layers, containing a total of near linear number of weights. This improves on the quadratic number of weights required in a standard dense layer, with little compromise in expressibility of the resulting operator. We show in a collection of empirical experiments that our proposed architecture not only produces results that match and often outperform existing known architectures, but it also offers faster training and prediction in deployment. This empirical phenomenon is observed in a wide variety of experiments that we report, including both supervised prediction on NLP and vision data, as well as in unsupervised representation learning using autoencoders. Preliminary theoretical results presented in the paper explain why training speed and outcome are not compromised by our proposed approach.
Neutrino mass ordering in light of recent data

We inspect recently updated neutrino oscillation data -- specifically coming from the Tokai to Kamioka and NuMI Off-axis $\nu_e$ Appearance experiments -- and how they are analyzed to determine whether the neutrino mass ordering is normal ($m_1 < m_2 < m_3$) or inverted ($m_3 < m_1 < m_2$). We show that, despite previous results giving a strong preference for the normal ordering, with the newest data from T2K and NOvA, this preference has all but vanished. Additionally, we highlight the importance of this result for non-oscillation probes of neutrinos, including neutrinoless double beta decay and cosmology. Future experiments, including JUNO, DUNE, and T2HK will provide valuable information and determine the mass ordering at a high confidence level.
Klessydra-T: Designing Vector Coprocessors for Multithreaded Edge-Computing Cores

Computation-intensive kernels, such as convolutions, matrix multiplication, and Fourier transform, are fundamental to edge-computing AI, signal processing, and cryptographic applications. Interleaved-multithreading (IMT) processor cores are interesting to pursue energy efficiency and low hardware cost for edge computing, yet they need hardware acceleration schemes to run heavy computational workloads. Following a vector approach to accelerate computations, this article explores possible alternatives to implement vector coprocessing units in RISC-V cores, showing the synergy between IMT and data-level parallelism in the target workloads.
Extending Importance-Performance Analysis with Benefit-Based Segmentation

For decades, the motives and benefits sought by outdoor recreation participants have been of interest to recreation managers and researchers. Understanding visitors’ socio-psychological recreation experience dimensions affords management opportunities to provide more satisfying recreational experiences. Two methodological and conceptual approaches to understand motives and benefits are benefit-based segmentation and Importance-Performance (I-P) analysis. In this project, both approaches were used to understand visitors and their preferences at a frequently visited USDA Forest Service recreation area. Over a one-year period, contacts were made with campers at developed and dispersed campsites. The contacts were followed by a mail questionnaire. These procedures resulted in a 67.0% response rate and 402 usable questionnaires. First, 15 recreation experience preferences (benefits sought) were used in a principal components analysis to establish user groups. Next, cluster analysis of four factors resulted in three clusters (a) Nature Relaxers, (b) Area Enthusiasts, and (c) Passive Visitors, which were compared with a multivariate analysis of variance. As a final analysis step, I-P measures were analyzed according to the groups to determine significant patterns. Results showed there was a significant omnibus MANOVA for the importance attributes, but not for the performance attributes. Importance attributes differing between the three clusters were areas for large groups, natural setting, signs posting general information, and signs posting regulations. Attribute distributions for the three I-P cluster grids were similar, but no two were identical. Adequate distance among campers, adequate parking, control of discourteous users, signs posting general information, signs posting regulations, and unable to see adjacent campers floated among the grids. In general, those attributes oriented toward site conditions were more likely to fall in the “concentrate here” quadrant than facility attributes. Results suggest that applying I-P measures to benefit-based segmentation can enhance analysis. This study supports previous research suggesting that I-P analysis requires statistics beyond placing attributes on grids. Further, managers can improve site design, planning, and visitor information with visitor benefit-based segmentation. Subtle differences among clusters may be ignored or altogether missed without attention to market segments and decisions based on homogeneous groups may be inappropriate.
Solving hard cut problems via flow-augmentation

We present a new technique for designing FPT algorithms for graph cut problems in undirected graphs, which we call flow augmentation. Our technique is applicable to problems that can be phrased as a search for an (edge) $(s,t)$-cut of cardinality at most $k$ in an undirected graph $G$ with designated terminals $s$ and $t$. More precisely, we consider problems where an (unknown) solution is a set $Z \subseteq E(G)$ of size at most $k$ such that (1) in $G-Z$, $s$ and $t$ are in distinct connected components, (2) every edge of $Z$ connects two distinct connected components of $G-Z$, and (3) if we define the set $Z_{s,t} \subseteq Z$ as these edges $e \in Z$ for which there exists an $(s,t)$-path $P_e$ with $E(P_e) \cap Z = \{e\}$, then $Z_{s,t}$ separates $s$ from $t$. We prove that in this scenario one can in randomized time $k^{O(1)} (|V(G)|+|E(G)|)$ add a number of edges to the graph so that with $2^{-O(k \log k)}$ probability no added edge connects two components of $G-Z$ and $Z_{s,t}$ becomes a minimum cut between $s$ and $t$. We apply our method to obtain a randomized FPT algorithm for a notorious "hard nut" graph cut problem we call Coupled Min-Cut. This problem emerges out of the study of FPT algorithms for Min CSP problems, and was unamenable to other techniques for parameterized algorithms in graph cut problems, such as Randomized Contractions, Treewidth Reduction or Shadow Removal. To demonstrate the power of the approach, we consider more generally Min SAT($\Gamma$), parameterized by the solution cost. We show that every problem Min SAT($\Gamma$) is either (1) FPT, (2) W[1]-hard, or (3) able to express the soft constraint $(u \to v)$, and thereby also the min-cut problem in directed graphs. All the W[1]-hard cases were known or immediate, and the main new result is an FPT algorithm for a generalization of Coupled Min-Cut.