Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalability in Computing and Robotics

Efficient engineered systems require scalability. A scalable system has increasing performance with increasing system size. In an ideal situation, the increase in performance (e.g., speedup) corresponds to the number of units (e.g., processors, robots, users) that are added to the system (e.g., three times the number of processors in a computer would lead to three times faster computations). However, if multiple units work on the same task, then coordination among these units is required. This coordination can introduce overheads with an impact on system performance. The coordination costs can lead to sublinear improvement or even diminishing performance with increasing system size. However, there are also systems that implement efficient coordination and exploit collaboration of units to attain superlinear improvement. Modeling the scalability dynamics is key to understanding and engineering efficient systems. Known laws of scalability, such as Amdahl’s law, Gustafson’s law, and Gunther’s Universal Scalability Law, are minimalistic phenomenological models that explain a rich variety of system behaviors through concise equations. While useful to gain general insights, the phenomenological nature of these models may limit the understanding of the underlying dynamics, as they are detached from first principles that could explain coordination overheads or synergies among units. Through a decentralized system approach, we propose a general model based on generic interactions between units that is able to describe, as specific cases, any general pattern of scalability included by previously reported laws. The proposed general model of scalability has the advantage of being built on first principles, or at least on a microscopic description of interaction between units, and therefore has the potential to contribute to a better understanding of system behavior and scalability. We show that this generic model can be applied to a diverse set of systems, such as parallel supercomputers, robot swarms, or wireless sensor networks, therefore creating a unified view on interdisciplinary design for scalability.
Welfare Quality® project: from scientific research to on farm assessment of animal welfare

Abstract Welfare Quality® is the acronym of the European research project “Integration of animal welfare in the food quality chain: from public concern to improved welfare and transparent quality”. This European project is focused on the integration of animal welfare in the food quality chain. Italian researchers from University of Milan, Naples, Padua, Parma and Pisa are involved in different tasks of this project. The second sub-project of Welfare Quality® aims to develop assessment systems to evaluate and monitor the quality of animal welfare on farms or at slaughter in 7 livestock species. Researchers of the Department of Animal Science of the University of Milan worked together other European partners to find and develop the most suitable animal-based measures for dairy and beef cattle.
First lattice calculation of radiative leptonic decay rates of pseudoscalar mesons

We present a non-perturbative lattice calculation of the form factors which contribute to the amplitudes for the radiative decays $P\to \ell \bar \nu_\ell \gamma$, where $P$ is a pseudoscalar meson and $\ell$ is a charged lepton. Together with the non-perturbative determination of the corrections to the processes $P\to \ell \bar \nu_\ell$ due to the exchange of a virtual photon, this allows accurate predictions at $O(\alpha_{em})$ to be made for leptonic decay rates for pseudoscalar mesons ranging from the pion to the $D_s$ meson. We are able to separate unambiguously and non-pertubatively the point-like contribution, from the structure-dependent, infrared-safe, terms in the amplitude. The fully non-perturbative $O(a)$ improved calculation of the inclusive leptonic decay rates will lead to the determination of the corresponding Cabibbo-Kobayashi-Maskawa (CKM) matrix elements also at $O(\alpha_{em})$. Prospects for a precise evaluation of leptonic decay rates with emission of a hard photon are also very interesting, especially for the decays of heavy $D$ and $B$ mesons for which currently only model-dependent predictions are available to compare with existing experimental data.
Sublinear Algorithms and Lower Bounds for Metric TSP Cost Estimation

We consider the problem of designing sublinear time algorithms for estimating the cost of a minimum metric traveling salesman (TSP) tour. Specifically, given access to a $n \times n$ distance matrix $D$ that specifies pairwise distances between $n$ points, the goal is to estimate the TSP cost by performing only sublinear (in the size of $D$) queries. For the closely related problem of estimating the weight of a metric minimum spanning tree (MST), it is known that for any $\varepsilon > 0$, there exists an $\tilde{O}(n/\varepsilon^{O(1)})$ time algorithm that returns a $(1 + \varepsilon)$-approximate estimate of the MST cost. This result immediately implies an $\tilde{O}(n/\varepsilon^{O(1)})$ time algorithm to estimate the TSP cost to within a $(2 + \varepsilon)$ factor for any $\varepsilon > 0$. However, no $o(n^2)$ time algorithms are known to approximate metric TSP to a factor that is strictly better than $2$. On the other hand, there were also no known barriers that rule out the existence of $(1 + \varepsilon)$-approximate estimation algorithms for metric TSP with $\tilde{O}(n)$ time for any fixed $\varepsilon > 0$. In this paper, we make progress on both algorithms and lower bounds for estimating metric TSP cost. We also show that the problem of estimating metric TSP cost is closely connected to the problem of estimating the size of a maximum matching in a graph.
Provable Tradeoffs in Adversarially Robust Classification

It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs. Despite significant progress in the area, foundational open problems remain. In this paper, we address several key questions. We derive exact and approximate Bayes-optimal robust classifiers for the important setting of two- and three-class Gaussian classification problems with arbitrary imbalance, for <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\ell _{\infty} $ </tex-math></inline-formula> adversaries. In contrast to classical Bayes-optimal classifiers, determining the optimal decisions here cannot be made pointwise and new theoretical approaches are needed. We develop and leverage new tools, including recent breakthroughs from probability theory on robust isoperimetry, which, to our knowledge, have not yet been used in the area. Our results reveal fundamental tradeoffs between standard and robust accuracy that grow when data is imbalanced. We also show further results, including an analysis of classification calibration for convex losses in certain models, and finite sample rates for the robust risk.
Survey of Extremely High-velocity Outflows in Sloan Digital Sky Survey Quasars

We present a survey of extremely high-velocity outflows (EHVOs) in quasars, defined by speeds between 0.1c and 0.2c. This region of parameter space has not been included in previous surveys, but it might present the biggest challenge for theoretical models and it might be a large contributor to feedback due to the outflows’ potentially large kinetic power. Using the Sloan Digital Sky Survey, we find 40 quasar spectra with broad EHVO C iv absorption, 10 times more than the number of previously known cases. We characterize the EHVO absorption and find that in 26 cases, C iv is accompanied by N v and/or O vi absorption. We find that EHVO quasars lack He ii emission and have overall larger bolometric luminosities and black hole masses than those of their parent sample and broad absorption line quasars (BALQSOs), while we do not find significant differences in their Eddington ratios. We also report a trend toward larger black hole masses as the velocity of the outflowing gas increases in the BALQSOs in our sample. The overall larger Lbol and lack of He ii emission of EHVO quasars suggest that radiation is likely driving these outflows. We find a potential evolutionary effect as EHVO quasars seem to be more predominant at large redshifts. We estimate that the kinetic power of these outflows may be similar to or even larger than that of the outflows from BALQSOs as the velocity factor increases this parameter by 1–2.5 orders of magnitude. Further study of EHVO quasars will help improve our understanding of quasar physics.
Streaming Erasure Codes over Multi-hop Relay Network

A typical path over the internet is composed of multiple hops. When considering the transmission of a sequence of messages (streaming messages) through packet erasure channel over a three-node network, it has been shown that taking into account the erasure pattern of each segment can result in improved performance compared to treating the channel as a point-to-point link. Since rarely there is only a single relay between the sender and the destination, it calls for trying to extend this scheme to more than a single relay. In this paper, we first extend the upper bound on the rate of transmission of a sequence of messages for any number of relays. We further suggest an achievable adaptive scheme that is shown to achieve the upper bound up to the size of an additional header that is required to allow each receiver to meet the delay constraints.
AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System

User behavior and feature interactions are crucial in deep learning-based recommender systems. There has been a diverse set of behavior modeling and interaction exploration methods in the literature. Nevertheless, the design of task-aware recommender systems still requires feature engineering and architecture engineering from domain experts. In this work, we introduce AMER, namely Automatic behavior Modeling and interaction Exploration in Recommender systems with Neural Architecture Search (NAS). The core contributions of AMER include the three-stage search space and the tailored three-step searching pipeline. In the first step, AMER searches for residual blocks that incorporate commonly used operations in the block-wise search space of stage 1 to model sequential patterns in user behavior. In the second step, it progressively investigates useful low-order and high-order feature interactions in the non-sequential interaction space of stage 2. Finally, an aggregation multi-layer perceptron (MLP) with shortcut connection is selected from flexible dimension settings of stage~3 to combine features extracted from the previous steps. For efficient and effective NAS, AMER employs the one-shot random search in all three steps. Further analysis reveals that AMER's search space could cover most of the representative behavior extraction and interaction investigation methods, which demonstrates the universality of our design. The extensive experimental results over various scenarios reveal that AMER could outperform competitive baselines with elaborate feature engineering and architecture engineering, indicating both effectiveness and robustness of the proposed method.
Modern singular integral theory with mild kernel regularity

We present a framework based on modified dyadic shifts to prove multiple results of modern singular integral theory under mild kernel regularity. Using new optimized representation theorems we first revisit a result of Figiel concerning the UMD-extensions of linear Calderon-Zygmund operators with mild kernel regularity and extend our new proof to the multilinear setting improving recent UMD-valued estimates of multilinear singular integrals. Our other results include weighted bi-parameter estimates and two-weight bi-commutator estimates with modified Dini-type assumptions.
Why is Attention Not So Attentive?

Attention-based methods have played an important role in model interpretations, where the calculated attention weights are expected to highlight the critical parts of inputs (e.g., keywords in sentences). However, some recent research points out that attention-as-importance interpretations often do not work as well as we expect. For example, learned attention weights are frequently uncorrelated with other feature importance indicators like gradient-based measures, and a debate on the effectiveness of attention-based interpretations has also raised. In this paper, we reveal that one root cause of this phenomenon can be ascribed to the combinatorial shortcuts, which stand for that the models may not only obtain information from the highlighted parts by attention mechanisms but from the attention weights themselves. We design one intuitive experiment to demonstrate the existence of combinatorial shortcuts and propose two methods to mitigate this issue. Empirical studies on attention-based instance-wise feature selection interpretation models are conducted, and the results show that the proposed methods can effectively improve the interpretability of attention mechanisms on a variety of datasets.
Parasitodiplogaster species associated with Pharmacosycea figs in Panama

Parasitodiplogaster species that are associated with figs in the Ficus subsection Pharmacosycea, i.e., Ficus maxima, F. yoponensis, F. insipida and F. glabrata, were studied using morphological characters and molecular sequences. Some nematodes isolated from the fig sycones were casually observed and recorded as morphospecies according to their morphotype, and were then digested for their DNA, while others were fixed in formalin-glycerin for additional morphological study. High resolution microscopic observation of the morphological materials yielded five morphospecies including two nominal species, P. maxinema and P. pharmaconema. The morphospecies were distinguished from each other by male tail characters. By contrast, five and six genotypes were recognised by D2/D3 expansion segments of the large subunit (LSU) and near-full-length sequences of small subunit (SSU) ribosomal RNA genes, respectively. Except for two nominal species, genotypes and morphospecies were not clearly correlated because of limitations in the microscopic resolution in the initial morphotyping. Although the morphospecies and genotypes were not clearly paired, Pharmacosycea-associated Parasitodiplogaster species, which are tentatively referred to as the ‘P. maxinema group’, formed a monophyletic clade in both D2/D3 LSU and SSU analyses and are morphologically characterised by their stomatal morphology, i.e., a tube-shaped stoma with two stick-like teeth and male tail morphology, presence of nine paired papillae and relatively slender spicule.
Making Convolutions Resilient Via Algorithm-Based Error Detection Techniques

Convolutional Neural Networks (CNNs) are being increasingly used in safety-critical and high-performance computing systems. As such systems require high levels of resilience to errors, CNNs must execute correctly in the presence of hardware faults. Full duplication provides the needed assurance but incurs a prohibitive 100 percent overhead. In this article, we focus on algorithmically verifying convolutions, the most resource-demanding operations in CNNs. We use checksums to verify convolutions. We identify the feasibility and performance related challenges that arise in algorithmically detecting errors in convolutions in optimized CNN inference deployment platforms (e.g., TensorFlow or TensorRT on GPUs) that fuse multiple network layers and use reduced-precision operations, and demonstrate how to overcome them. We propose and evaluate variations of the algorithm-based error detection (ABED) techniques that offer implementation complexity, runtime overhead, and coverage trade-offs. Results show that ABED can detect all transient hardware errors that might otherwise corrupt output with low runtime overheads (6-23 percent). Only about 1.4 percent of the total computations in a CNN are not protected by ABED, which can be duplicated for full CNN protection. ABED for the compute-intensive convolutions and duplicating the rest can offer at least 1.6× throughput compared to full duplication.
An Optimal Distributionally Robust Auction

An indivisible object may be sold to one of $n$ agents who know their valuations of the object. The seller would like to use a revenue-maximizing mechanism but her knowledge of the valuations' distribution is scarce: she knows only the means (which may be different) and an upper bound for valuations. Valuations may be correlated. Using a constructive approach based on duality, we prove that a mechanism that maximizes the worst-case expected revenue among all deterministic dominant-strategy incentive compatible, ex post individually rational mechanisms takes the following form: (1) the bidders submit bids $b_i$; (2) for each bidder, a linear score $s_i=\beta_ib_i-\alpha_i$ is calculated where $\alpha_i$, $\beta_i$ are fixed parameters; (3) the object is awarded to the agent with the highest score, provided it's nonnegative; (4) the winning bidder pays the minimal amount he would need to bid to still win in the auction. The set of optimal mechanisms includes other mechanisms but all those have to be close to the optimal linear score auction in a certain sense. When means are high, all optimal mechanisms share the linearity property. Second-price auction without a reserve is an optimal mechanism when the number of symmetric bidders is sufficiently high.
Learning Functions to Study the Benefit of Multitask Learning

We study and quantify the generalization patterns of multitask learning (MTL) models for sequence labeling tasks. MTL models are trained to optimize a set of related tasks jointly. Although multitask learning has achieved improved performance in some problems, there are also tasks that lose performance when trained together. These mixed results motivate us to study the factors that impact the performance of MTL models. We note that theoretical bounds and convergence rates for MTL models exist, but they rely on strong assumptions such as task relatedness and the use of balanced datasets. To remedy these limitations, we propose the creation of a task simulator and the use of Symbolic Regression to learn expressions relating model performance to possible factors of influence. For MTL, we study the model performance against the number of tasks (T), the number of samples per task (n) and the task relatedness measured by the adjusted mutual information (AMI). In our experiments, we could empirically find formulas relating model performance with factors of sqrt(n), sqrt(T), which are equivalent to sound mathematical proofs in Maurer[2016], and we went beyond by discovering that performance relates to a factor of sqrt(AMI).
Neuron Circuits for Low-Power Spiking Neural Networks Using Time-To-First-Spike Encoding

Hardware-based Spiking Neural Networks (SNNs) are regarded as promising candidates for the cognitive computing system due to its low power consumption and highly parallel operation. In this paper, we train the SNN in which the firing time carries information using temporal backpropagation. The temporally encoded SNN with 512 hidden neurons achieved an accuracy of 96.90% for the MNIST test set. Furthermore, the effect of the device variation on the accuracy in temporally encoded SNN is investigated and compared with that of the rate-encoded network. In a hardware configuration of our SNN, NOR-type analog memory having an asymmetric floating gate is used as a synaptic device. In addition, we propose a neuron circuit including a refractory period generator for temporally encoded SNN. The performance of the 2-layer neural network composed of synapses and proposed neurons is evaluated through circuit simulation using SPICE based on the BSIM3v3 model with $0.35~\mu \text{m}$ technology. The network with 128 hidden neurons achieved an accuracy of 94.9%, a 0.1% reduction compared to that of the system simulation of the MNIST dataset. Finally, each block’s latency and power consumption constituting the temporal network is analyzed and compared with those of the rate-encoded network depending on the total time step. Assuming that the network has 256 total time steps, the temporal network consumes 15.12 times less power than the rate-encoded network and makes decisions 5.68 times faster.
Pollen flow between flowers of the same morph in invasive populations of Oxalis pes-caprae L. in the western Mediterranean region

Oxalis pes-caprae is a tristylous South African geophyte that is invasive in regions with Mediterranean climate. Given the introduction of the short-styled morph only, vegetative reproduction was, until recently, the reproductive mode known for this invaded area. The detection of new floral morphs, fruit and seed production in natural populations of the invaded range, together with the reported weakening in the heteromorphic incompatibility system, raised the question on whether the short-styled flowers are able to exchange pollen among themselves, and if so, which mechanisms account for it. Thus, flower morphometric analysis and field experiments were conducted to assess pollinator visitation rates and pollen flow in three invasive populations. Flower morphological changes suggesting the evolution from tristyly towards semi-homostyly were observed. Moreover, O. pes-caprae flowers were visited by different functional groups of pollinators, mostly hymenopteran. Dye flow experiments revealed that the short-styled flowers were able to successfully exchange pollen among themselves in natural conditions. All of this, together with the weakening of the incompatibility system, constitutes further data that might help to explain the recently reported occurrence of sexual reproduction in this invaded range, which by itself bears important implications for the invasion process of O. pes-caprae.
Colonizing ability in the Echinochloa crus-galli complex (barnyard grass). II. Seed biology

Comparisons of some seed parameters of two taxa of barnyard grass (Echinochloa crus-galli complex) were made in an effort to understand differences in habitat preference and colonizing ability. The taxa were E. crus-galli var. crus-galli, a cosmopolitan weed of wet, disturbed ground and E. crus-galli var. oryzicola, a crop mimic restricted to rice fields. In California, where all seed collections originated, the two taxa are ecologically differentiated within the rice-field ecosystem. Echinochloa crus-galli var. crus-galli is a weed of paddy banks and shallow water around the periphery of rice fields whereas E. crus-galli var. oryzicola is found primarily within permanently flooded rice fields. A survey of seed weight in 10 populations of each of the two taxa demonstrated that seeds of E. crus-galli var. oryzicola were on the average two to three times heavier than those of E. crus-galli var. crus-galli. Differences in weight were reflected in the buoyancy characteristics of fresh seeds of the two taxa. A.
Experimental study of the $e^+e^-\to n\bar n$ process at the VEPP-2000 $e^+e^-$ collider with the SND detector

The process $e^+e^-\to n\bar n$ is studied in the experiment at the VEPP-2000 $e^+e^-$ collider with the SND detector. The technique of the time measurements in the multichannel NaI(Tl) electromagnetic calorimeter is used to select nnbar events. The value of the measured cross section in the center-of-mass energy range from 1.894 to 2 GeV varies from 0.5 to 0.35 nb. The effective neutron timelike form factor is derived from the measured cross section and compared with the proton form factor. The ratio of the neutron electric and magnetic form factors is obtained from the analysis of the antineutron polar angle distribution and found to be consistent with unity.