Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Active Queue Management Parameters on Aggregate Traffic Performance

Active queue management (AQM) is a family of packet dropping mechanisms for FIFO queues that have been proposed to support end-to-end congestion control mechanisms in the Internet. In this paper, we examine the four possible combinations of queue size averaging vs instantaneous queue size and a sharp dropping function vs a smooth dropping function. The combinations include both the well known "Drop from Tail" (TD) and "RED" mechanisms as well as the more recently proposed "Gentle RED" and a previously unrecognized mechanism we call "Gentle RED with instantaneous queue size", or GRED-I. The results show the end-to-end performance of the four AQM mechanisms. Thus, we do not study individual TCP flow performance but instead the flow aggregation behavior through backbone nodes, and we chose experimental parameters and observed metrics accordingly. We show that if TD and RED have similar behaviors in terms of aggregate TCP throughput, RED discards more non responsive traffic than TD. On the other hand, we show that RED creates more consecutive drops than TD. We identify queue size averaging and the slope of the dropping function as the two major parameters influencing consecutive drops.
Disentangled Latent Transformer for Interpretable Monocular Height Estimation

Monocular height estimation (MHE) from remote sensing imagery has high potential in generating 3D city models efficiently for a quick response to natural disasters. Most existing works pursue higher performance. However, there is little research exploring the interpretability of MHE networks. In this paper, we target at exploring how deep neural networks predict height from a single monocular image. Towards a comprehensive understanding of MHE networks, we propose to interpret them from multiple levels: 1) Neurons: unit-level dissection. Exploring the semantic and height selectivity of the learned internal deep representations; 2) Instances: object-level interpretation. Studying the effects of different semantic classes, scales, and spatial contexts on height estimation; 3) Attribution: pixel-level analysis. Understanding which input pixels are important for the height estimation. Based on the multi-level interpretation, a disentangled latent Transformer network is proposed towards a more compact, reliable, and explainable deep model for monocular height estimation. Furthermore, a novel unsupervised semantic segmentation task based on height estimation is first introduced in this work. Additionally, we also construct a new dataset for joint semantic segmentation and height estimation. Our work provides novel insights for both understanding and designing MHE models.
Challenges in COVID-19 Chest X-Ray Classification: Problematic Data or Ineffective Approaches?

The value of quick, accurate, and confident diagnoses cannot be undermined to mitigate the effects of COVID-19 infection, particularly for severe cases. Enormous effort has been put towards developing deep learning methods to classify and detect COVID-19 infections from chest radiography images. However, recently some questions have been raised surrounding the clinical viability and effectiveness of such methods. In this work, we carry out extensive experiments on a large COVID-19 chest X-ray dataset to investigate the challenges faced with creating reliable AI solutions from both the data and machine learning perspectives. Accordingly, we offer an in-depth discussion into the challenges faced by some widely-used deep learning architectures associated with chest X-Ray COVID-19 classification. Finally, we include some possible directions and considerations to improve the performance of the models and the data for use in clinical settings.
SN 2012ij: A Low-luminosity Type Ia Supernova and Evidence for a Continuous Distribution from a 91bg-like Explosion to Normal Ones

In this paper, we present photometric and spectroscopic observations of the subluminous Type Ia supernova (SN Ia) 2012ij, which has an absolute B-band peak magnitude MB,max=−17.95±0.15 mag. The B-band light curve exhibits a fast postpeak decline with Δm 15(B) = 1.86 ± 0.05 mag. All the R- and I/i-band light curves show a weak secondary peak/shoulder feature at about 3 weeks after the peak, like some transitional subclass of SNe Ia, which could result from an incomplete merger of near-infrared (NIR) double peaks. The spectra are characterized by Ti ii and strong Si ii λ5972 absorption features that are usually seen in low-luminosity objects like SN 1999by. The NIR spectrum before maximum light reveals weak carbon absorption features, implying the existence of unburned materials. We compare the observed properties of SN 2012ij with those predicted by the sub-Chandrasekhar-mass and the Chandrasekhar-mass delayed-detonation models and find that both optical and NIR spectral properties can be explained to some extent by these two models. By comparing the secondary maximum features in the I and i bands, we suggest that SN 2012ij is a transitional object linking normal SNe Ia to typical 91bg-like ones. From the published sample of SNe Ia from the Carnegie Supernova Project II, we estimate that the fraction of SN 2012ij–like SNe Ia is not lower than ∼2%.
Distributions of Hook lengths in integer partitions

Motivated by the many roles that hook lengths play in mathematics, we study the distribution of the number of t -hooks in the partitions of n . We prove that the limiting distribution is normal with mean µ t ( n ) ∼ √ 6 nπ − t 2 and variance σ 2 t ( n ) ∼ ( π 2 − 6) √ 6 n 2 π 3 . Furthermore, we prove that the distribution of the number of hook lengths that are multiples of a ﬁxed t ≥ 4 in partitions of n converge to a shifted Gamma distribution with parameter k = ( t − 1) / 2 and scale θ = (cid:112) 2 / ( t − 1) .
b
-hadron spectroscopy study based on the similarity of double bottom baryon and bottom meson

(Dated: The dynamical similarity which exists between the λ -mode excited bbq baryons ( q refers to the u , d , and s quarks) and the ¯ bq mesons inspires us to carry out a combined study of their spectroscopy. In this work, the masses and strong decays of these low-lying b ¯ q and bbq states are studied by the same theoretical methods, and the dynamical similarity which is implied in their mass spectra and strong decays are also discussed. The recent discovered ¯ bq states, including the B J (5840), B J (5970), B sJ (6064), and B sJ (6114), are analyzed. According to our result, the B J (5840) could be assigned as a 2 S state, while the B J (5970) could be regarded as a member of the 1 D (2 − , 3 − ) j q = 5 / 2 doublet. The B sJ (6064) and B sJ (6114) are probably the D -wave states. Especially, they could be explained as the members of the 1 D (1 − , 2 − ) j q = 3 / 2 and 1 D (2 − , 3 − ) j q = 5 / 2 doublets, respectively. The predicted masses and decay properties of other unknown ¯ bq / bbq states may provide useful clues to the future experiment.
On a Conjecture Concerning Shuffle-Compatible Permutation Statistics

The notion of shuffle-compatible permutation statistics was implicit in Stanley's work on P-partitions and was first explicitly studied by Gessel and Zhuang. The aim of this paper is to prove that the triple ${\rm (udr, pk, des)}$ is shuffle-compatible as conjectured by Gessel and Zhuang, where ${\rm udr}$ denotes the number of up-down runs, ${\rm pk}$ denotes the peak number, and ${\rm des}$ denotes the descent number. This is accomplished by establishing an ${\rm (udr, pk, des)}$-preserving bijection in the spirit of Baker-Jarvis and Sagan's bijective proofs of the shuffle compatibility property of permutation statistics.
Bullshark: DAG BFT Protocols Made Practical

We present Bullshark, the first directed acyclic graph (DAG) based asynchronous Byzantine Atomic Broadcast protocol that is optimized for the common synchronous case. Like previous DAG-based BFT protocols [19, 25], Bullshark requires no extra communication to achieve consensus on top of building the DAG. That is, parties can totally order the vertices of the DAG by interpreting their local view of the DAG edges. Unlike other asynchronous DAG-based protocols, Bullshark provides a practical low latency fast-path that exploits synchronous periods and deprecates the need for notoriously complex view-change and view-synchronization mechanisms. Bullshark achieves this while maintaining all the desired properties of its predecessor DAG-Rider [25]. Namely, it has optimal amortized communication complexity, it provides fairness and asynchronous liveness, and safety is guaranteed even under a quantum adversary. In order to show the practicality and simplicity of our approach, we also introduce a standalone partially synchronous version of Bullshark, which we evaluate against the state of the art. The implemented protocol is embarrassingly simple (200 LOC on top of an existing DAG-based mempool implementation). It is highly efficient, achieving for example, 125,000 transactions per second with a 2 seconds latency for a deployment of 50 parties. In the same setting, the state of the art pays a steep 50% latency increase as it optimizes for asynchrony.
Discrete curvature on graphs from the effective resistance

This article introduces a new approach to discrete curvature based on the concept of effective resistances. We propose a curvature on the nodes and links of a graph and present the evidence for their interpretation as a curvature. Notably, we find a relation to a number of well-established discrete curvatures (Ollivier, Forman, combinatorial curvature) and show evidence for convergence to continuous curvature in the case of Euclidean random graphs. Being both efficient to approximate and highly amenable to theoretical analysis, these resistance curvatures have the potential to shed new light on the theory of discrete curvature and its many applications in mathematics, network science, data science and physics.
Convergence of a nonlocal to a local diffuse interface model for two-phase flow with unmatched densities

We prove convergence of suitable subsequences of weak solutions of a diffuse interface model for the two-phase flow of incompressible fluids with different densities with a nonlocal Cahn-Hilliard equation to weak solutions of the corresponding system with a standard "local" Cahn-Hilliard equation. The analysis is done in the case of a sufficiently smooth bounded domain with no-slip boundary condition for the velocity and Neumann boundary conditions for the Cahn-Hilliard equation. The proof is based on the corresponding result in the case of a single Cahn-Hilliard equation and compactness arguments used in the proof of existence of weak solutions for the diffuse interface model.
Cultural Heritage Documentation and Conservation: Three-Dimensional (3D) Laser Scanning and Geographical Information System (GIS) Techniques for Thematic Mapping of Facade Stonework of St. Nicholas Church (Pisa, Italy)

ABSTRACT This article reports the mapping of the stones, including marbles and brick masonry, used for building the facade of the medieval Church of St. Nicholas (XI century ad), one of the most interesting churches in Pisa, due also to the nearby famous octagonal bell tower. Mapping of stone materials was performed using a computer-aided design (CAD)/geographical information system (GIS) software package for storing and processing spatial information of the ashlars, obtained using three-dimensional (3D) laser-scanning data, combined with high-resolution images, and stone-to-stone observations. Based on collected data, the facade of the Church of St. Nicholas appeared mostly composed of rocks belonging to the metamorphic Tuscan sequence, quartzites, and marbles from Mt. Pisano area. Other types of rocks were also observed, as black limestones quarried at some kilometers northwest from Pisa in the Monti d’Oltre Serchio area, white Apuan marbles and Macigno sandstones. Conversely, intarsia appeared mainly made up of serpentinite and, subordinately, red limestones laid in white Apuan and Mt. Pisano marbles.
Cassini, d'Ocagne, and Vajda identities for n-step Fibonacci numbers

Results of this paper concern n-determinants which we defined in the paper [2]. In the paper [1], 2-determinants are considered. In this paper, we extend results from [1] on n-determinants by proving that Cassini, d’Ocagne, Catalan and Vajda identities may be extended to hold for n-step Fibonacci numbers.
Universal Hydrogen Bond Symmetrisation Dynamics Under Extreme Conditions

Thomas Meier, ∗ Florian Trybel, Saiana Khandarkhaeva, Dominique Laniel, Takayuki Ishii, Alena Aslandukova, Natalia Dubrovinskaia, 2 and Leonid Dubrovinsky Center for High Pressure Science and Technology Advance Research, Beijing, China Department of Physics, Chemistry and Biology (IFM), Linköping University, SE-581 83, Linköping, Sweden Bayerisches Geoinstitut, University of Bayreuth, Bayreuth, Germany Material Physics and Technology at Extreme Conditions, Laboratory of Crystallography, University of Bayreuth, Bayreuth, Germany
Infinite multidimensional scaling for metric measure spaces

For a given metric measure space $(X,d,\mu)$ we consider finite samples of points, calculate the matrix of distances between them and then reconstruct the points in some finite-dimensional space using the multidimensional scaling (MDS) algorithm with this distance matrix as an input. We show that this procedure gives a natural limit as the number of points in the samples grows to infinity and the density of points approaches the measure $\mu$. This limit can be viewed as ``infinite MDS'' embedding of the original space, now not anymore into a finite-dimensional space but rather into an infinite-dimensional Hilbert space. We further show that this embedding is stable with respect to the natural convergence of metric measure spaces. However, contrary to what is usually believed in applications, we show that in many cases it does not preserve distances, nor is even bi-Lipschitz, but may provide snowflake (Assouad-type) embeddings of the original space to a Hilbert space (this is, for instance, the case of a sphere and a flat torus equipped with their geodesic distances).
FeynGrav: FeynCalc extension for gravity amplitudes

Package ‘FeynGrav’ which provides a framework to operate with Feynman rules for gravity within ‘FeynCalc’ is presented. We present a framework to deal with Feynman rules for general relativity and non-supersymmetric matter minimally coupled to gravity. Applicability of the package is tested with 2 → 2 on-shell tree level graviton scattering, polarization operators, and one-loop scalar-gravitational interaction structure.
The high-velocity clouds above the disk of the outer Milky Way: misty precipitating gas in a region roiled by stellar streams

The high-velocity clouds (HVCs) in the outer Milky Way at 20 ◦ < l < 190 ◦ have similar spatial locations, metallicities, and kinematics. Moreover, their locations and kinematics are coincident with several extraplanar stellar streams. The HVC origins may be connected to the stellar streams, either stripped directly from them or precipitated by the aggregate dynamical roiling of the region by the stream progenitors. This paper suggests that these HVCs are “misty” precipitation in the stream wakes based on the following observations. New high-resolution (2.6 km s − 1 ) ultraviolet spectroscopy of the QSO H1821+643 resolves what appears to be a single HVC absorption cloud (at 7 km s − 1 resolution) into ﬁve components with T (cid:46) 3 × 10 4 K. Photoionization models can explain the low-ionization components but require some depletion of refractory elements by dust, and model degeneracies allow a large range of metallicity. High-ionization absorption lines (Si iv , C iv , and O vi ) are kinematically aligned with the lower-ionization lines and cannot be easily explained with photoionization or equilibrium collisional ionization; these lines are best matched by non-equilibrium rapidly cooling models, i.e., condensing/precipitating gas, with high metallicity and a signiﬁcant amount of H i . Both the low- and high-ionization phases have low ratios of cooling time to freefall time and cooling time to sound-crossing time, which enables fragmentation and precipitation. The H1821+643 results are corroborated by spectroscopy of six other nearby targets that likewise show kinematically correlated low- and high-ionization absorption lines with evidence of dust depletion and rapid cooling.
VELTAIR: towards high-performance multi-tenant deep learning services via adaptive compilation and scheduling

Deep learning (DL) models have achieved great success in many application domains. As such, many industrial companies such as Google and Facebook have acknowledged the importance of multi-tenant DL services. Although the multi-tenant service has been studied in conventional workloads, it is not been deeply studied on deep learning service, especially on general-purpose hardware. In this work, we systematically analyze the opportunities and challenges of providing multi-tenant deep learning services on the general-purpose CPU architecture from the aspects of scheduling granularity and code generation. We propose an adaptive granularity scheduling scheme to both guarantee resource usage efficiency and reduce the scheduling conflict rate. We also propose an adaptive compilation strategy, by which we can dynamically and intelligently pick a program with proper exclusive and shared resource usage to reduce overall interference-induced performance loss. Compared to the existing works, our design can serve more requests under the same QoS target in various scenarios (e.g., +71%, +62%, +45% for light, medium, and heavy workloads, respectively), and reduce the averaged query latency by 50%.
Recursive Least Squares Advantage Actor-Critic Algorithms

As an important algorithm in deep reinforcement learning, advantage actor critic (A2C) has been widely succeeded in both discrete and continuous control tasks with raw pixel inputs, but its sample efficiency still needs to improve more. In traditional reinforcement learning, actor-critic algorithms generally use the recursive least squares (RLS) technology to update the parameter of linear function approximators for accelerating their convergence speed. However, A2C algorithms seldom use this technology to train deep neural networks (DNNs) for improving their sample efficiency. In this paper, we propose two novel RLS-based A2C algorithms and investigate their performance. Both proposed algorithms, called RLSSA2C and RLSNA2C, use the RLS method to train the critic network and the hidden layers of the actor network. The main difference between them is at the policy learning step. RLSSA2C uses an ordinary first-order gradient descent algorithm and the standard policy gradient to learn the policy parameter. RLSNA2C uses the Kronecker-factored approximation, the RLS method and the natural policy gradient to learn the compatible parameter and the policy parameter. In addition, we analyze the complexity and convergence of both algorithms, and present three tricks for further improving their convergence speed. Finally, we demonstrate the effectiveness of both algorithms on 40 games in the Atari 2600 environment and 11 tasks in the MuJoCo environment. From the experimental results, it is shown that our both algorithms have better sample efficiency than the vanilla A2C on most games or tasks, and have higher computational efficiency than other two state-of-the-art algorithms.
Protecting Fiber-Optic Quantum Key Distribution Sources against Light-Injection Attacks

A well-protected and characterised source in a quantum key distribution system is needed for its security. Unfortunately, the source is vulnerable to light-injection attacks, such as Trojan-horse, laser-seeding, and laser-damage attacks, in which an eavesdropper actively injects bright light to hack the source unit. The hacking laser could be a high-power one that can modify properties of components via the laser-damage attack and also further help the Trojan-horse and other light-injection attacks. Here we propose a countermeasure against the light-injection attacks, consisting of an additional sacrificial component placed at the exit of the source. This component should either withstand high-power incoming light while attenuating it to a safe level that cannot modify the rest of the source, or get destroyed into a permanent high-attenuation state that breaks up the line. We demonstrate experimentally that off-the-shelf fiber-optic isolators and circulators have these desired properties, at least under attack by a continuous-wave high-power laser.
Generalization of the Extended Minimal Excludant of Andrews and Newman

In a recent pioneering work, Andrews and Newman defined an extended function $p_{A,a}(n)$ of their minimal excludant or"mex"of a partition function. By considering the special cases $p_{k,k}(n)$ and $p_{2k,k}(n)$, they unearthed connections to the rank and crank of partitions and some restricted partitions. In this paper, we build on their work and obtain more general results associating the extended mex function with the number of partitions of an integer with arbitrary bound on the rank and crank. We also derive a new result expressing the smallest parts function of Andrews as a finite sum of the extended mex function in consideration with a curious coefficient. We also obtain a few restricted partition identities with some reminiscent of shifted partition identities. Finally, we define and explore a new minimal excludant for overpartitions.
Kohler-Jobin meets Ehrhard: the sharp lower bound for the Gaussian principal frequency while the Gaussian torsional rigidity is fixed, via rearrangements

Abstract. In this note, we provide an adaptation of the Kohler-Jobin rearrangement technique to the setting of the Gauss space. As a result, we prove the Gaussian analogue of the Kohler-Jobin’s resolution of a conjecture of PólyaSzegö: when the Gaussian torsional rigidity of a (convex) domain is fixed, the Gaussian principal frequency is minimized for the half-space. At the core of this rearrangement technique is the idea of considering a “modified” torsional rigidity, with respect to a given function, and rearranging its layers to half-spaces, in a particular way; the Rayleigh quotient decreases with this procedure. We emphasize that the analogy of the Gaussian case with the Lebesgue case is not to be expected here, as in addition to some soft symmetrization ideas, the argument relies on the properties of some special functions; the fact that this analogy does hold is somewhat of a miracle.