Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasibound states of charged dilatonic black holes

Investigation of quasibound states of black holes is significant for expected ultralight particles, as well as black holes through gravitational waves. We first investigate quasibound states of a massive scalar field for dilatonic charged black holes via numerical analysis. We study the complex eigenfrequencies of the massive scalar field in a wide range of gravitational fine structure constant $\ensuremath{\mu}M$ in detail, and show the effects of charge. Further, we study the eigenfrequencies of the massive field through an analytical approach by matching the near horizon solution and far field solution, and find its spectra for excited states by the iteration method. We demonstrate that the numerical solution and analytical solution perfectly agree with each other in the region where charge of the black hole is large, both for real and imaginary parts of the eigenfrequencies.
Yang–Mills monopoles in extremal Reissner–Nordström black hole metric

We show that the closed-form monotone solution linking two different vacuum states, for the exterior Yang–Mills wave equation over the extremal Reissner–Nordström spacetime found in a recent work of Bizoń and Kahl, is the unique solution to an associated domain-wall type energy minimization problem. We also derive the accompanying interior Yang–Mills wave equation within the same formalism. We obtain a few more closed-form solutions, regular and singular, among other solutions of oscillatory behavior, and discuss several interesting features of the solutions based on some energy consideration.
Microcircuit synchronization and heavy tailed synaptic weight distribution in preBötzinger Complex contribute to generation of breathing rhythm

The preBötzinger Complex, the mammalian inspiratory rhythm generator, encodes inspiratory time as motor pattern. Spike synchronization throughout this sparsely connected network generates inspiratory bursts albeit with variable latencies after preinspiratory activity onset in each breathing cycle. Using preBötC rhythmogenic microcircuit minimal models, we examined the variability in probability and latency to burst, mimicking experiments. Among various physiologically plausible graphs of 1000 point neurons with experimentally determined neuronal and synaptic parameters, directed Erdős-Rényi graphs best captured the experimentally observed dynamics. Mechanistically, preBötC (de)synchronization and oscillatory dynamics are regulated by the efferent connectivity of spiking neurons that gates the amplification of modest preinspiratory activity through input convergence. Furthermore, to replicate experiments, a lognormal distribution of synaptic weights was necessary to augment the efficacy of convergent coincident inputs. These mechanisms enable exceptionally robust yet flexible preBötC attractor dynamics that, we postulate, represent universal temporal-processing and decision-making computational motifs throughout the brain.
On non-adaptive majority problems of large query size

We are given $n$ balls and an unknown coloring of them with two colors. Our goal is to find a ball that belongs to the larger color class, or show that the color classes have the same size. We can ask sets of $k$ balls as queries, and the problem has different variants, according to what the answers to the queries can be. These questions has attracted several researchers, but the focus of most research was the adaptive version, where queries are decided sequentially, after learning the answer to the previous query. Here we study the non-adaptive version, where all the queries have to be asked at the same time.
Machine Learning Advances for Time Series Forecasting

In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data.
The Life and Death of SSDs and HDDs: Similarities, Differences, and Prediction Models

Data center downtime typically centers around IT equipment failure. Storage devices are the most frequently failing components in data centers. We present a comparative study of hard disk drives (HDDs) and solid state drives (SSDs) that constitute the typical storage in data centers. Using a six-year field data of 100,000 HDDs of different models from the same manufacturer from the BackBlaze dataset and a six-year field data of 30,000 SSDs of three models from a Google data center, we characterize the workload conditions that lead to failures and illustrate that their root causes differ from common expectation but remain difficult to discern. For the case of HDDs we observe that young and old drives do not present many differences in their failures. Instead, failures may be distinguished by discriminating drives based on the time spent for head positioning. For SSDs, we observe high levels of infant mortality and characterize the differences between infant and non-infant failures. We develop several machine learning failure prediction models that are shown to be surprisingly accurate, achieving high recall and low false positive rates. These models are used beyond simple prediction as they aid us to untangle the complex interaction of workload characteristics that lead to failures and identify failure root causes from monitored symptoms.
Injection locking of a levitated optomechanical oscillator for precision force sensing

Siamak Dadras,1, 2 Robert M. Pettit,3 Danika R. Luntz-Martin,2, 4 Kewen Xiao,5 M. Bhattacharya,2, 5 and A. Nick Vamivakas1, 2, 4, ∗ The Institute of Optics, University of Rochester, Rochester, NY 14627, USA Center for Coherence and Quantum Optics, University of Rochester, Rochester, NY 14627, USA Institute for Research in Electronics and Applied Physics, and Joint Quantum Institute, University of Maryland, College Park, MD 20742, USA Department of Physics and Astronomy, University of Rochester, Rochester, NY 14627, USA School of Physics and Astronomy, Rochester Institute of Technology, Rochester, NY 14623, USA
Rényi free energy and variational approximations to thermal states

We propose the construction of thermodynamic ensembles that minimize the Rényi free energy, as an alternative to Gibbs states. For large systems, the local properties of these Rényi ensembles coincide with those of thermal equilibrium, and they can be used as approximations to thermal states. We provide algorithms to find tensor network approximations to the 2-Rényi ensemble. In particular, a matrix-product-state representation can be found by using gradient-based optimization on Riemannian manifolds, or via a non-linear evolution which yields the desired state as a fixed point. We analyze the performance of the algorithms and the properties of the ensembles on onedimensional spin chains.
The Structure of Morphisms in Persistent Homology, I. Functorial Dualities

We prove duality results for absolute and relative versions of persistent (co)homology, generalizing previous results in terms of barcodes. Due to their naturality, we can apply these results to morphisms in persistent homology that are induced by morphisms between filtrations. This lays the groundwork for the efficient computation of barcodes for images, kernels, and cokernels of such morphisms.
Noisy Labels Can Induce Good Representations

The current success of deep learning depends on large-scale labeled datasets. In practice, high-quality annotations are expensive to collect, but noisy annotations are more affordable. Previous works report mixed empirical results when training with noisy labels: neural networks can easily memorize random labels, but they can also generalize after training with noisy labels. To explain this puzzle, we study how architecture affects learning with noisy labels. We observe that if an architecture “suits” the task, training with noisy labels can induce useful hidden representations, even when the model generalizes poorly; i.e., the last few layers of the model are more negatively affected by noisy labels. This ﬁnding leads to a simple method to improve models trained on noisy labels: replacing the ﬁnal dense layers with a linear model, whose weights are learned from a small set of clean data. We empirically validate our ﬁndings across three architectures (Convolutional Neural Networks, Graph Neural Networks, and Multi-Layer Perceptrons) and two domains (graph algorithmic tasks and image classiﬁcation). Furthermore, we achieve state-of-the-art results on image classiﬁcation benchmarks by combining our method with existing approaches on noisy label training.
Influencer Videos: Unboxing the Mystique

Influencer marketing has become a very popular tool to reach customers. Despite the rapid growth in influencer videos, there has been little research on the effectiveness of their constituent features in explaining video engagement. We study YouTube influencers and analyze their unstructured video data across text, audio and images using an"interpretable deep learning"framework that accomplishes both goals of prediction and interpretation. Our prediction-based approach analyzes unstructured data and finds that"what is said"in words (text) is more influential than"how it is said"in imagery (images) or acoustics (audio). Our novel interpretation-based approach is implemented after completion of model prediction by analyzing the same source of unstructured data to measure importance attributed to the video features. We eliminate several spurious relationships in two steps, identifying a subset of relationships which are confirmed using theory. We uncover novel findings that establish distinct associations for measures of shallow and deep engagement based on the dual-system framework of human thinking. Our approach is validated using simulated data, and we discuss the learnings from our findings for influencers and brands.
Multi-Modality Cut and Paste for 3D Object Detection

Three-dimensional (3D) object detection is essential in autonomous driving. There are observations that multi-modality methods based on both point cloud and imagery features perform only marginally better or sometimes worse than approaches that solely use single-modality point cloud. This paper investigates the reason behind this counter-intuitive phenomenon through a careful comparison between augmentation techniques used by single modality and multi-modality methods. We found that existing augmentations practiced in single-modality detection are equally useful for multi-modality detection. Then we further present a new multi-modality augmentation approach, Multi-mOdality Cut and pAste (MoCa). MoCa boosts detection performance by cutting point cloud and imagery patches of ground-truth objects and pasting them into different scenes in a consistent manner while avoiding collision between objects. We also explore beneficial architecture design and optimization practices in implementing a good multi-modality detector. Without using ensemble of detectors, our multi-modality detector achieves new state-of-the-art performance on nuScenes dataset and competitive performance on KITTI 3D benchmark. Our method also wins the best PKL award in the 3rd nuScenes detection challenge. Code and models will be released at https://github.com/open-mmlab/mmdetection3d.
Analyzing Representations inside Convolutional Neural Networks

How can we discover and succinctly summarize the concepts that a neural network has learned? Such a task is of great importance in applications of networks in areas of inference that involve classification, like medical diagnosis based on fMRI/x-ray etc. In this work, we propose a framework to categorize the concepts a network learns based on the way it clusters a set of input examples, clusters neurons based on the examples they activate for, and input features all in the same latent space. This framework is unsupervised and can work without any labels for input features, it only needs access to internal activations of the network for each input example, thereby making it widely applicable. We extensively evaluate the proposed method and demonstrate that it produces human-understandable and coherent concepts that a ResNet-18 has learned on the CIFAR-100 dataset.
Architecture, Dataflow and Physical Design Implications of 3D-ICs for DNN-Accelerators

The everlasting demand for higher computing power for deep neural networks (DNNs) drives the development of parallel computing architectures. 3D integration, in which chips are integrated and connected vertically, can further increase performance because it introduces another level of spatial parallelism. Therefore, we analyze dataflows, performance, area, power and temperature of such 3D-DNN-accelerators. Monolithic and TSV-based stacked 3D-ICs are compared against 2D-ICs. We identify workload properties and architectural parameters for efficient 3D-ICs and achieve up to 9. 14x speedup of 3Dvs.2D. We discuss area-performance trade-offs. We demonstrate applicability as the 3D-IC draws similar power as 2D-ICs and is not thermal limited.
Double field theory and pseudosupersymmetry

Supersymmetric bosonic backgrounds governed by first-order BPS equations, can be realised in a much broader setting by relaxing the requirement of closure of the superalgebra beyond the level of quadratic fermion terms. The resulting pseudo-supersymmetric theories can be defined in arbitrary spacetime dimensions. We focus here on the ${\cal N}=1$ pseudo-supersymmetric extensions of the arbitrary-dimensional bosonic string action, which were constructed a few years ago. In this paper, we recast these in the language of generalised geometry. More precisely, we construct the action and the corresponding supersymmetry transformation rules in terms of O($D$)$\times$O($D$) covariant derivatives, and we discuss consistent truncations on manifolds with generalised $G$-structure. As explicit examples, we discuss Minkowski$\times G$ vacuum solutions and their corresponding pseudo-supersymmetry. We also briefly discuss squashed group manifold solutions, including an example with a Lorentzian signature metric on the group manifold $G$.
C-RAN Zero-Forcing With Imperfect CSI: Analysis and Precode & Quantize Feedback

Downlink joint transmission by a cluster of remote radio-heads (RRHs) is essential for enhancing throughput in future cellular networks. This method requires global channel state information (CSI) at the processing unit that designs the joint precoder. To this end, extensive CSI must be shared between the RRHs and that unit. This paper proposes two contributions. The first is a new upper bound on the rate loss, which implies a lower bound on the achievable rate for an RRHs-cluster employing joint zero-forcing (ZF) with incomplete CSI with single-user decoding (SUD) at the mobile stations (MSs). The second contribution, which follows insights from the bound, is a new CSI sharing scheme that drastically reduces the significant overhead associated with acquiring global CSI for joint transmission. In a nutshell, each RRH applies a local precoding matrix that creates low-dimensional effective channels that can be quantized more accurately with fewer bits, thereby reducing the overhead of sharing CSI. In addition to the CSI sharing-overhead, this scheme reduces the data rate delivered to each RRH in the cluster.
Prevalence of Voluntary Counseling and Testing Utilization and Its Associated Factors Among Merawi Preparatory School Students in Merawi Town, West Gojjam, Ethiopia

Purpose HIV/AIDS is a major public health problem in many parts of the world. Voluntary counseling and testing (VCT) is one of the essential strategies for its prevention and an entry point to AIDS care. Nevertheless, utilization of VCT is low in Ethiopia. Hence, the aim of this study was to assess the prevalence of VCT utilization and associated factors among Merawi preparatory school students, West Gojjam, Ethiopia. Methods An institution-based cross-sectional study was conducted from December 2 to 20, 2019 using a stratified random sampling method to enroll 365 participants. Data were collected using pretested structured self-administered questionnaires, entered into EpiData version 3.1 and exported to SPSS version 20 for analysis. A logistic regression was used to determine an association between a number of independent variables and dependent variables. Adjusted odds ratios were calculated at 95% CI and considered significant with a p value of ≤ 0.05. Results The mean age of participants was 20.2 ± 2.6 years and more than half (52.6%) of the respondents were male. From the study participants, 77.5% had good knowledge of VCT, 75% had a positive attitude towards VCT for HIV and 31.5% had undergone VCT for HIV. According to the multivariable logistic regression, having a boyfriend or girlfriend (AOR = 10.49; 95% CI: 5.48, 20.09), discussing with parents about HIV (AOR = 3.63; 95% CI: 1.97, 6.68), good knowledge (AOR: 3.9; 95% CI: 1.80, 8.46) and positive attitude (AOR: 3.14; 95% CI:1.45, 6.82) were significantly associated with VCT utilization. Conclusion The majority of the respondents had good knowledge and a positive attitude towards VCT; however, its utilization was low. Having a boyfriend or girlfriend, discussion with parents about HIV, good knowledge and positive attitude towards VCT were significantly associated with VCT utilization. Therefore, a health education program aimed at bringing behavioral change should be planned and implemented.
Dyselectrolytemia-management and implications in hemodialysis (Review)

Hemodialysis is a method for the renal replacement therapy followed by series of acute and chronic complications. Dyselectrolytemia appears in patients undergoing dialysis through mechanisms related to the chronic kidney disease and/or to the dialysis therapy and for this group of patients it is associated with an increase of morbidity and mortality. The dialysate has a standard composition, which can be modified according to the patient's characteristics. During hemodialysis patients are exposed to 18,000-36.000 litres of water/year, and the water purity along with the biochemical composition of the dialysate are essential. The individualization of the dialysis prescription is recommended for each patient and it has an important role in preventing the occurrence of dyselectrolyemia. The individualization of the treatment prescription according to the blood constants of each patient is the prerogative of the nephrologist and the association of the electrolyte imbalances with the patients cardiovascular mortality explains the importance of paying special attention to them.
Thromboembolism, Hypercoagulopathy, and Antiphospholipid Antibodies in Critically Ill Coronavirus Disease 2019 Patients: A Before and After Study of Enhanced Anticoagulation

Supplemental Digital Content is available in the text. Objectives: To determine the prevalence of thrombotic events, functional coagulation tests, inflammatory biomarkers, and antiphospholipid antibodies before and after enhanced anticoagulation in critically ill coronavirus disease 2019 patients. Design: Retrospective. Setting: Tertiary intensive care unit. Patients: Two cross-sectional cohorts of ICU-treated coronavirus disease 2019 patients were included before (cohort 1, n = 12) and after (cohort 2, n = 14) enhanced prophylactic anticoagulation strategy. Interventions: Before and after study of enhanced anticoagulation. Measurements and Main Results: Thromboelastometry point-of-care coagulation tests were performed by thromboelastography (Tem International GmbH, Munich, Germany), standard blood tests were extracted from patient charts, and presence of antiphospholipid antibodies in plasma was measured. All patients were males on mechanical ventilation. In cohort 1 (low-molecular-weight heparin dose: 129 ± 53 U/kg/24 hr), 50% had pulmonary embolism, and thromboelastography analysis revealed hypercoagulation in a majority of patients and greater than 80% had detectable antiphospholipid antibodies. In the second cohort (enhanced low-molecular-weight heparin dose: 200 ± 82 U/kg/24 hr; p = 0.04 vs cohort 1), we found a nonsignificantly lower prevalence of pulmonary embolism (21%; p = 0.22), lower fibrinogen (6.3 ± 2.5 vs 8.7 ± 2.0; p = 0.02), reduced fibrinogen-dependent thromboelastography (p < 0.001), and lower inflammatory markers. Conclusions: In these two cross-sectional cohorts of ICU-treated coronavirus disease 2019 patients, thromboembolic complications, hypercoagulation, and antiphospholipid antibodies were common. A more aggressive anticoagulation regime was associated with a reduction in inflammatory biomarkers including plasma fibrinogen and a reduction in fibrinogen-dependent hypercoagulation, as indicated by thromboelastography analyses.
Effect of ankle joint position on triceps surae contractile properties and motor unit discharge rates

The triceps surae (TS) length–tension relationship can be altered by changing the knee joint position, ankle joint position or both. However, studies exploring the effect of muscle length on neuromuscular properties have focused only on knee joint position changes affecting two of the three muscle components of the TS. Thus, the purpose of this study is to compare the neuromuscular properties of the three TS muscles during plantar flexion contractions at two ankle joint positions, 20° dorsiflexed (DF) and 20° plantar flexed (PF). Maximal isometric voluntary strength (MVC), voluntary activation, and evoked contractile properties of the ankle plantar flexors were compared between both ankle joint positions. Additionally, soleus, medial (MG), and lateral (LG) gastrocnemii motor unit discharge rates (MUDRs) were sampled during plantar flexion contractions at 25%, 50%, 75%, and 100% MVC using indwelling tungsten electrodes. MVC and peak twitch torque were lower by ~61% and 70%, respectively, whereas the maximal rate of torque relaxation was 39% faster in the PF compared with the DF position. Voluntary activation (~95%) was unaffected by changes in ankle joint position. LG MUDRs showed no differences between ankle joint positions, regardless of contraction intensity. Submaximal MG and soleus MUDRs showed no differences between the two ankle joint positions, however both muscles had 9% and 20% higher MUDRs in the DF position, respectively. These results provide further evidence for the differential activation among the three components of the TS with the greatest increases in soleus MUDRs compared with the gastrocnemii when the muscles are lengthened.