Miracle-dz/UniFAR-FTUs huggingface dataset:
Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO

The recent introduction of powerful embedded graphics processing units (GPUs) has allowed for unforeseen improvements in real-time computer vision applications. It has enabled algorithms to run onboard, well above the standard video rates, yielding not only higher information processing capability, but also reduced latency. This work focuses on the applicability of efficient low-level, GPU hardware-specific instructions to improve on existing computer vision algorithms in the field of visual-inertial odometry (VIO). While most steps of a VIO pipeline work on visual features, they rely on image data for detection and tracking, of which both steps are well suited for parallelization. Especially non-maxima suppression and the subsequent feature selection are prominent contributors to the overall image processing latency. Our work first revisits the problem of non-maxima suppression for feature detection specifically on GPUs, and proposes a solution that selects local response maxima, imposes spatial feature distribution, and extracts features simultaneously. Our second contribution introduces an enhanced FAST feature detector that applies the aforementioned non-maxima suppression method. Finally, we compare our method to other state-of-the-art CPU and GPU implementations, where we always outperform all of them in feature tracking and detection, resulting in over 1000fps throughput on an embedded Jetson TX2 platform. Additionally, we demonstrate our work integrated into a VIO pipeline achieving a metric state estimation at ∼200fps.Code available at: https://github.com/uzh-rpg/vilib
Diode‐Like Selective Enhancement of Carrier Transport through Metal–Semiconductor Interface Decorated by Monolayer Boron Nitride

2D semiconductors such as monolayer molybdenum disulfide (MoS2) are promising material candidates for next‐generation nanoelectronics. However, there are fundamental challenges related to their metal–semiconductor (MS) contacts, which limit the performance potential for practical device applications. In this work, 2D monolayer hexagonal boron nitride (h‐BN) is exploited as an ultrathin decorating layer to form a metal–insulator–semiconductor (MIS) contact, and an innovative device architecture is designed as a platform to reveal a novel diode‐like selective enhancement of the carrier transport through the MIS contact. The contact resistance is significantly reduced when the electrons are transported from the semiconductor to the metal, but is barely affected when the electrons are transported oppositely. A concept of carrier collection barrier is proposed to interpret this intriguing phenomenon as well as a negative Schottky barrier height obtained from temperature‐dependent measurements, and the critical role of the collection barrier at the drain end is shown for the overall transistor performance.
Evidence for Proton Acceleration up to TeV Energies Based on VERITAS and Fermi-LAT Observations of the Cas A SNR

We present a study of γ-ray emission from the core-collapse supernova remnant Cas A in the energy range from 0.1 GeV to 10 TeV. We used 65 hr of the Very Energetic Radiation Imaging Telescope Array System (VERITAS) data to cover 200 GeV–10 TeV, and 10.8 yr of Fermi-Large Area Telescope (LAT) data to cover 0.1–500 GeV. The spectral analysis of Fermi-LAT data shows a significant spectral curvature around 1.3 ± 0.4stat GeV that is consistent with the expected spectrum from pion decay. Above this energy, the joint spectrum from Fermi-LAT and VERITAS deviates significantly from a simple power law, and it is best described by a power law with a spectral index of 2.17 ± 0.02stat and a cutoff energy of 2.3 ± 0.5stat TeV. These results, along with radio, X-ray, and γ-ray data, are interpreted in the context of leptonic and hadronic models. Assuming a one-zone model, we exclude a purely leptonic scenario and conclude that proton acceleration up to at least 6 TeV is required to explain the observed γ-ray spectrum. From modeling of the entire multiwavelength spectrum, a minimum magnetic field inside the remnant of Bmin ≈ 150 μG is deduced.
Improved Gradient based Adversarial Attacks for Quantized Networks

Neural network quantization has become increasingly popular due to efficient memory consumption and faster computation resulting from bitwise operations on the quantized networks. Even though they exhibit excellent generalization capabilities, their robustness properties are not well-understood. In this work, we systematically study the robustness of quantized networks against gradient based adversarial attacks and demonstrate that these quantized models suffer from gradient vanishing issues and show a fake sense of robustness. By attributing gradient vanishing to poor forward-backward signal propagation in the trained network, we introduce a simple temperature scaling approach to mitigate this issue while preserving the decision boundary. Despite being a simple modification to existing gradient based adversarial attacks, experiments on multiple image classification datasets with multiple network architectures demonstrate that our temperature scaled attacks obtain near-perfect success rate on quantized networks while outperforming original attacks on adversarially trained models as well as floating-point networks.
Harmonic Decompositions of Convolutional Networks

We present a description of the function space and the smoothness class associated with a convolutional network using the machinery of reproducing kernel Hilbert spaces. We show that the mapping associated with a convolutional network expands into a sum involving elementary functions akin to spherical harmonics. This functional decomposition can be related to the functional ANOVA decomposition in nonparametric statistics. Building off our functional characterization of convolutional networks, we obtain statistical bounds highlighting an interesting trade-off between the approximation error and the estimation error.
Serialized Output Training for End-to-End Overlapped Speech Recognition

This paper proposes serialized output training (SOT), a novel framework for multi-speaker overlapped speech recognition based on an attention-based encoder-decoder approach. Instead of having multiple output layers as with the permutation invariant training (PIT), SOT uses a model with only one output layer that generates the transcriptions of multiple speakers one after another. The attention and decoder modules take care of producing multiple transcriptions from overlapped speech. SOT has two advantages over PIT: (1) no limitation in the maximum number of speakers, and (2) an ability to model the dependencies among outputs for different speakers. We also propose a simple trick that allows SOT to be executed in $O(S)$, where $S$ is the number of the speakers in the training sample, by using the start times of the constituent source utterances. Experimental results on LibriSpeech corpus show that the SOT models can transcribe overlapped speech with variable numbers of speakers significantly better than PIT-based models. We also show that the SOT models can accurately count the number of speakers in the input audio.
Challenges and Opportunities of Nuclear Cardiology in India

Nuclear Cardiology in India is underutilized. Contributing factors include high cost of starting Nuclear medicine facility, stringent regulations, small pool of trained nuclear physicians and technologists, high cost of radio pharmaceuticals and supply issues, poor penetrance of nuclear medicine in rural areas, inadequate exposure of cardiologists during training to the advantages of nuclear cardiology. India is poised to see an epidemic of coronary artery disease (CAD) and diabetes by end of few decades and nuclear cardiology would make significant contributions to handle this epidemic. Government and insurance companies advocacy is necessary for nuclear Cardiology to get its important rightful place in the management of CAD in India.
Neutron star cooling in modified gravity theories

We study thermal evolution of isolated neutron stars in scalar-tensor theories for the first time. Whether the rapid cooling due to the direct Urca process occurs or not is an interesting question in the viewpoint of the temperature observation of isolated neutron stars. The cooling effect is typically influenced by the proton fraction and the central density. If a fifth force is mediated due to modification of gravity, the relation between the central density and mass of neutron stars differs from one in general relativity, and the cooling curve is also naively expected to be varied. We find that an unscreened fifth force near the surface of neutron stars changes mass-central density relation, and the direct Urca process can be triggered even for neutron stars with smaller mass. This might enable us to test gravitational theories with neutron star cooling.
Polynomial-time efficient position

Suppose that $S$ is a surface of positive complexity and $N \subset S$ is a tie neighbourhood of a large train track $\tau$ in $S$. Suppose that $\alpha$ is a properly immersed, essential, and non-peripheral arc or curve in $S$. We present a polynomial-time algorithm that, given $S$, $N$, and $\alpha$, homotopes $\alpha$ into efficient position with respect to $N$. Proofs for the existence of efficient position were previously given in [Takarajima,2000] and [MasurMosherSchleimer,2012]. In [Takarajima,2000], a constructive proof for the existence of efficient position is given for immersed curves on closed surfaces of genus greater than or equal to two. There is no discussion of the complexity of the implied algorithm. In [MasurMosherSchleimer,2012], the existence of efficient position is proved for embedded curves with respect to birecurrent train tracks on surfaces of positive complexity. The implied algorithm operates via an exhaustive search. No time bounds can be deduced. We note that the algorithm presented in this thesis and the algorithm suggested by a careful reading of [Takarajima,2000] coincide in the case of closed surfaces. However, this thesis constitutes more than a time-complexity analysis of Takarajima's constructive proof. Firstly, we are more general as we allow surfaces with boundary, whereas Takarajima only considers closed surfaces. Secondly, our combinatorial set-up uses arcs and curves with transverse self-intersection, whereas the barycentric subdivision of complementary regions carried out in [Takarajima,2000] forces non-transverse self-intersection even for curves which are initially embedded. Thirdly, the algorithm in this thesis is formulated purely in terms of local homotopies, whereas [Takarajima,2000] requires semi-local arguments. Thus, we can, and do, give pseudocode for our algorithm as well as prove its correctness.
PointGMM: A Neural GMM Network for Point Clouds

Point clouds are a popular representation for 3D shapes. However, they encode a particular sampling without accounting for shape priors or non-local information. We advocate for the use of a hierarchical Gaussian mixture model (hGMM), which is a compact, adaptive and lightweight representation that probabilistically defines the underlying 3D surface. We present PointGMM, a neural network that learns to generate hGMMs which are characteristic of the shape class, and also coincide with the input point cloud. PointGMM is trained over a collection of shapes to learn a class-specific prior. The hierarchical representation has two main advantages: (i) coarse-to-fine learning, which avoids converging to poor local-minima; and (ii) (an unsupervised) consistent partitioning of the input shape. We show that as a generative model, PointGMM learns a meaningful latent space which enables generating consistent interpolations between existing shapes, as well as synthesizing novel shapes. We also present a novel framework for rigid registration using PointGMM, that learns to disentangle orientation from structure of an input shape.
Maker Breaker on digraphs

We study two biased Maker–Breaker games played on the complete digraph K → n . In the strong connectivity game, Maker wants to build a strongly connected subgraph. We determine the asymptotic optimal bias for this game viz. n log n . In the Hamiltonian game, Maker wants to build a Hamiltonian subgraph. We determine the asymptotic optimal bias for this game up to a constant factor.
AriEL: volume coding for sentence generation

Mapping sequences of discrete data to a point in a continuous space makes it difficult to retrieve those sequences via random sampling. Mapping the input to a volume would make it easier to retrieve at test time, and that's the strategy followed by the family of approaches based on Variational Autoencoder. However the fact that they are at the same time optimizing for prediction and for smoothness of representation, forces them to trade-off between the two. We improve on the performance of some of the standard methods in deep learning to generate sentences by uniformly sampling a continuous space. We do it by proposing AriEL, that constructs volumes in a continuous space, without the need of encouraging the creation of volumes through the loss function. We first benchmark on a toy grammar, that allows to automatically evaluate the language learned and generated by the models. Then, we benchmark on a real dataset of human dialogues. Our results indicate that the random access to the stored information is dramatically improved, and our method AriEL is able to generate a wider variety of correct language by randomly sampling the latent space. VAE follows in performance for the toy dataset while, AE and Transformer follow for the real dataset. This partially supports to the hypothesis that encoding information into volumes instead of into points, can lead to improved retrieval of learned information with random sampling. This can lead to better generators and we also discuss potential disadvantages.
Educational Case: Ectopic Pregnancy

The following fictional case is intended as a learning tool within the Pathology Competencies for Medical Education (PCME), a set of national standards for teaching pathology. These are divided into three basic competencies: Disease Mechanisms and Processes, Organ System Pathology, and Diagnostic Medicine and Therapeutic Pathology. For additional information, and a full list of learning objectives for all three competencies, see http://journals.sagepub. com/doi/10.1177/2374289517715040.1
Is Energy Delivery Guided by Indirect Calorimetry Associated With Improved Clinical Outcomes in Critically Ill Patients? A Systematic Review and Meta-analysis

Background: Indirect calorimetry (IC) is recommended to guide energy delivery over predictive equations in critical illness due to its precision. However, the impact of using IC to measure energy expenditure on clinical outcomes is uncertain. Objective: To evaluate whether using IC to measure energy expenditure to inform energy delivery reduced hospital mortality and improved other important outcomes compared to using predictive equations in critically ill adults. Methods: A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline. Medline, Embase, CINAHL, and the Cochrane Library were searched for studies using IC to guide energy delivery compared to a predictive equation in adult critically ill patients with the primary outcome (hospital mortality) or any of the secondary outcomes reported (including but not limited to hospital and intensive care unit (ICU) length of stay (LOS) and duration mechanical ventilation (MV). Risk of bias within studies was assessed using the Cochrane “Risk of Bias” 1 tool. Random-effect meta-analyses were used when heterogeneity between studies existed (I2 > 50%). Data are reported as median (interquartile range [IQR]), binomial outcomes as odds ratio (OR), 95% confidence interval (CI), and continuous outcomes as mean difference (MD). Results: Of 4060 articles, 4 randomized controlled trials were identified with 396 patients included in analysis. Three studies were considered low risk of bias and 1 as high risk. Two studies reported hospital mortality (n = 130 and 40 participants, respectively). When combined, no association between IC-guided energy delivery and hospital mortality was found (OR = 0.81, 95% CI = [0.25, 2.67], P = 0.73, I2 = 52). No differences were reported with ICU mortality and hospital LOS between groups, but ICU LOS and duration of MV varied across all studies. According to the meta-analysis, no differences were observed in ICU LOS (MD = 1.39, 95% CI = [–5.01, 7.79], P = 0.67, I2 = 81%), although the duration of MV was increased when energy delivery was guided by IC (MD = 2.01, 95% CI = [0.45, 3.57], P = 0.01, I2 = 26%). In all 4 studies, prescribed energy targets were more closely met when energy delivery was informed by IC compared to a predictive equation. Three studies reported the percentage delivered versus the prescribed energy target, with the median (IQR) delta between the IC and predictive equation arms 19% (10%-32%). Conclusion: Limited data exist to assess the impact of using IC to inform energy delivery in comparison to predictive equations on hospital mortality. The association of IC use with other important outcomes, including duration of MV, needs to be further explored before definitive conclusions can be made.
Slow slip source characterized by lithological and geometric heterogeneity

Slow slip events on subduction megathrusts are linked to subduction of rough crust and heterogeneous fault rock properties. Slow slip events (SSEs) accommodate a significant proportion of tectonic plate motion at subduction zones, yet little is known about the faults that actually host them. The shallow depth (<2 km) of well-documented SSEs at the Hikurangi subduction zone offshore New Zealand offers a unique opportunity to link geophysical imaging of the subduction zone with direct access to incoming material that represents the megathrust fault rocks hosting slow slip. Two recent International Ocean Discovery Program Expeditions sampled this incoming material before it is entrained immediately down-dip along the shallow plate interface. Drilling results, tied to regional seismic reflection images, reveal heterogeneous lithologies with highly variable physical properties entering the SSE source region. These observations suggest that SSEs and associated slow earthquake phenomena are promoted by lithological, mechanical, and frictional heterogeneity within the fault zone, enhanced by geometric complexity associated with subduction of rough crust.
MicroRNA-129-2-3p directly targets Wip1 to suppress the proliferation and invasion of intrahepatic cholangiocarcinoma

Accumulated studies showed that numerous microRNAs (miRNAs) were aberrantly expressed in human intrahepatic cholangiocarcinoma (ICC) and contributed to the tumorigenic processes. However, whether miR-129-2-3p is implicated in the ICC initiation and progression is still limited. Here, the results revealed that miR-129-2-3p expression was notably decreased in ICC tissues and cell lines, and that a low miR-129-2-3p expression was obviously associated with distant metastasis and clinical stage. Exogenous miR-129-2-3p expression evidently repressed the proliferative and invasive abilities of ICC cells. Mechanistic studies indicated that Wild-type p53-induced phosphatase 1 (Wip1) was a direct target gene for miR-129-2-3p in ICC cells. Furthermore, silencing Wip1 expression mimicked the suppressive effects of miR-129-2-3p upregulation on ICC cells. Interestingly, reintroduction of Wip1 expression partially abolished the miR-129-2-3p -reduced cell proliferation and invasion in ICC. Moreover, ectopic miR-129-2-3p expression hindered the ICC tumor growth in vivo. To the best of our knowledge, it is the first time to reveal that miR-129-2-3p plays a crucial role in tumor suppression in ICC pathogenesis through directly targeting Wip1. These results will aid in elucidating the roles of miR-129-2-3p in ICC, and suggest that this miRNA may provide a potential target for the treatment of ICC
Auditory Working Memory Explains Variance in Speech Recognition in Older Listeners Under Adverse Listening Conditions

Introduction Older listeners have difficulty understanding speech in unfavorable listening conditions. To compensate for acoustic degradation, cognitive processing skills, such as working memory, need to be engaged. Despite prior findings on the association between working memory and speech recognition in various listening conditions, it is not yet clear whether the modality of stimuli presentation for working memory tasks should be auditory or visual. Given the modality-specific characteristics of working memory, we hypothesized that auditory working memory capacity could predict speech recognition performance in adverse listening conditions for older listeners and that the contribution of auditory working memory to speech recognition would depend on the task and listening condition. Methods Seventy-six older listeners and twenty younger listeners completed four kinds of auditory working memory tasks, including digit and speech span tasks, and sentence recognition tasks in four different listening conditions having multi-talker noise and time-compression. For older listeners, cognitive function was screened using the Mini-Mental Status Examination, and audibility was assured. Results Auditory working memory, as measured by listening span, significantly predicted speech recognition performance in adverse listening conditions for older listeners. A linear regression model showed speech recognition performance for older listeners could be explained by auditory working memory whilst controlling for the impact of age and hearing sensitivity. Discussion Measuring working memory in the auditory modality facilitated explaining the variance in speech recognition in adverse listening conditions for older listeners. The linguistic features and the complexity of the auditory stimuli may affect the association between working memory and speech recognition performance. Conclusion We demonstrated the contribution of auditory working memory to speech recognition in unfavorable listening conditions in older populations. Taking the modality-specific characteristics of working memory into account may be a key to better understand the difficulty in speech recognition in daily listening conditions for older listeners.
Streptavidin/biotin: Tethering geometry defines unbinding mechanics

Single-molecule force spectroscopy reveals how the mechanical stability of a widely used biomolecular complex varies fourfold. Macromolecules tend to respond to applied forces in many different ways. Chemistry at high shear forces can be intriguing, with relatively soft bonds becoming very stiff in specific force-loading geometries. Largely used in bionanotechnology, an important case is the streptavidin (SA)/biotin interaction. Although SA’s four subunits have the same affinity, we find that the forces required to break the SA/biotin bond depend strongly on the attachment geometry. With AFM-based single-molecule force spectroscopy (SMFS), we measured unbinding forces of biotin from different SA subunits to range from 100 to more than 400 pN. Using a wide-sampling approach, we carried out hundreds of all-atom steered molecular dynamics (SMD) simulations for the entire system, including molecular linkers. Our strategy revealed the molecular mechanism that causes a fourfold difference in mechanical stability: Certain force-loading geometries induce conformational changes in SA’s binding pocket lowering the energy barrier, which biotin has to overcome to escape the pocket.