Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling the Network to Surf the Internet

Few-shot learning is challenging due to the limited data and labels. Existing algorithms usually resolve this problem by pre-training the model with a considerable amount of annotated data which shares knowledge with the target domain. Nevertheless, large quantities of homogenous data samples are not always available. To tackle this issue, we develop a framework that enables the model to surf the Internet, which implies that the model can collect and annotate data without manual effort. Since the online data is virtually limitless and continues to be generated, the model can thus be empowered to constantly obtain up-to-date knowledge from the Internet. Additionally, we observe that the generalization ability of the learned representation is crucial for self-supervised learning. To present its importance, a naive yet efficient normalization strategy is proposed. Consequentially, this strategy boosts the accuracy of the model significantly (20.46% at most). We demonstrate the superiority of the proposed framework with experiments on miniImageNet, tieredImageNet and Omniglot. The results indicate that our method has surpassed previous unsupervised counterparts by a large margin (more than 10%) and obtained performance comparable with the supervised ones.
Nonlinear Invariant Risk Minimization: A Causal Approach

Due to spurious correlations, machine learning systems often fail to generalize to environments whose distributions differ from the ones used at training time. Prior work addressing this, either explicitly or implicitly, attempted to find a data representation that has an invariant causal relationship with the target. This is done by leveraging a diverse set of training environments to reduce the effect of spurious features and build an invariant predictor. However, these methods have generalization guarantees only when both data representation and classifiers come from a linear model class. We propose Invariant Causal Representation Learning (ICRL), a learning paradigm that enables out-of-distribution (OOD) generalization in the nonlinear setting (i.e., nonlinear representations and nonlinear classifiers). It builds upon a practical and general assumption: the prior over the data representation factorizes when conditioning on the target and the environment. Based on this, we show identifiability of the data representation up to very simple transformations. We also prove that all direct causes of the target can be fully discovered, which further enables us to obtain generalization guarantees in the nonlinear setting. Extensive experiments on both synthetic and real-world datasets show that our approach significantly outperforms a variety of baseline methods. Finally, in the concluding discussion, we further explore the aforementioned assumption and propose a general view, called the Agnostic Hypothesis: there exist a set of hidden causal factors affecting both inputs and outcomes. The Agnostic Hypothesis can provide a unifying view of machine learning, be it supervised, unsupervised, or reinforcement learning, in terms of representation learning. More importantly, it can inspire a new direction to explore the general theory for identifying hidden causal factors, which is key to enabling the OOD generalization guarantees in machine learning. 1 ar X iv :2 10 2. 12 35 3v 1 [ cs . L G ] 2 4 Fe b 20 21
Caracterização física e físico-química de frutos de cajá-umbu (Spondias spp.)

This work had as objective the physical and physico-chemical characterization of fruits of caja-umbu (Spondias spp.) genotypes cultivated under climatic conditions of the Zone of Forest of Pernambuco, aiming at identifying promising materials for commercial use and for works of genetic improvement. Fruits of caja-umbu from a collection of germ-plasm installed at experimental Station of Itambe-Pe were submitted to the following determinations: fruit and seed weight (FW and SW), pulp yield (PY), relation between longitudinal and transversal diameter (LD/TD), pH, total soluble solids (TSS), total titrable acidity (TTA) and the relation TSS/TTA. it was evidenced that the weight of the fruits and the seeds had presented significant variation without, however, presenting difference in the RP and relation Ld/td of the fruits. the pH, TSS, TTA and TSS/TTA for the fruits from the genotypes showed significant differences.
Measuring HTTP/3: Adoption and Performance

The third version of the Hypertext Transfer Protocol (HTTP) is in its final standardization phase by the IETF. Besides better security and increased flexibility, it promises benefits in terms of performance. HTTP/3 adopts a more efficient header compression schema and replaces TCP with QUIC, a transport protocol carried over UDP, originally proposed by Google and currently under standardization too. Although HTTP/3 early implementations already exist and some websites announce its support, it has been subject to few studies. We provide a first measurement study on HTTP/3 adoption and performance. We testify how it has been adopted by some of the leading Internet companies such as Google, Facebook and Cloudflare in 2020. We run a large-scale measurement campaign towards thousands of websites adopting HTTP/3, aiming at understanding to what extent it achieves better performance than HTTP/2. We find that adopting websites often host most web page objects on third-party servers, which support only HTTP/2 or even HTTP/1.1. As excepted, websites loading objects from a limited set of third-party domains (avoiding legacy protocols) are those experiencing larger performance gains. Our experiments however show that HTTP/3 provides sizable benefits only in scenarios with high latency or poor bandwidth.
Existence of gradient Gibbs measures on regular trees which are not translation invariant

We provide an existence theory for gradient Gibbs measures for Z-valued spin models on regular trees which are not invariant under translations of the tree, assuming only summability of the transfer operator. The gradient states we obtain are delocalized. The construction we provide for them starts from a two-layer hidden Markov model representation in a setup which is not invariant under tree-automorphisms, involving internal q-spin models. The proofs of existence and lack of translation invariance of infinite-volume gradient states are based on properties of the local pseudo-unstable manifold of the corresponding discrete dynamical systems of these internal models, around the free state, at large q.
INFLUENCE OF A BLANCHING PRETREATMENT ON COLOR, OIL UPTAKE AND WATER ACTIVITY OF POTATO STICKS, AND ITS OPTIMIZATION

The objective of this research was to study the influence of different blanching conditions on potato stick color, oil uptake and water activity (Aw ), and optimize the process by means of the desirability function. Color was expressed as properties of L*a*b*and L*C*h*spaces, some of which were fitted by Box–Behnken quadratic models. The blanching optimum conditions were as follows: a concentration of ascorbic acid of 0.2 g/100 g potato, a time of 5.5 min and a temperature of 69C. Responses obtained when using these conditions were −1.68 a*value and −86.31° h*value, which represent a hue in potatoes unaffected by the enzymatic browning. Regarding oil uptake, some of the blanched sticks absorbed 31% less oil than the control. Regarding Aw, all blanched samples presented lower values than the control, suggesting that blanching increases the shelf life of fried potatoes. PRACTICAL APPLICATIONS This article presents that the optimized blanching process conditions were able to control the enzymatic browning in potato sticks prior to frying, and thus improved their quality. Furthermore, some blanching conditions were found to decrease by 31% the oil uptake in potato sticks, leading to healthier fried potatoes. Finally, blanching significantly decreased the Aw in all fried potato sticks evaluated, suggesting that this process is also a way to increase the shelf life of fried potatoes.
On Unbiased Estimation for Discretized Models

In this article, we consider computing expectations w.r. t. probability measures which are subject to discretization error. Examples include partially observed diffusion processes or inverse problems, where one may have to discretize time and/or space, in order to practically work with the probability of interest. Given access only to these discretizations, we consider the construction of unbiased Monte Carlo estimators of expectations w.r. t. such target probability distributions. It is shown how to obtain such estimators using a novel adaptation of randomization schemes and Markov simulation methods. Under appropriate assumptions, these estimators possess finite variance and finite expected cost. There are two important consequences of this approach: (i) unbiased inference is achieved at the canonical complexity rate, and (ii) the resulting estimators can be generated independently, thereby allowing strong scaling to arbitrarily many parallel processors. Several algorithms are presented, and applied to some examples of Bayesian inference problems, with both simulated and real observed data.
UniverseMachine: Predicting Galaxy Star Formation over Seven Decades of Halo Mass with Zoom-in Simulations

We apply the empirical galaxy–halo connection model UniverseMachine to dark-matter-only zoom-in simulations of isolated Milky Way (MW)–mass halos, along with their parent cosmological simulations. This application extends UniverseMachine predictions into the ultrafaint dwarf galaxy regime (102 M ⊙ ≤ M * ≤ 105 M ⊙) and yields a well-resolved stellar mass–halo mass (SMHM) relation over the peak halo mass range of 108–1015 M ⊙. The extensive dynamic range provided by the zoom-in simulations allows us to assess specific aspects of dwarf galaxy evolution predicted by UniverseMachine. In particular, although UniverseMachine is not constrained for dwarf galaxies with M * ≲ 108 M ⊙, our predicted SMHM relation is consistent with that inferred for MW satellite galaxies at z = 0 using abundance matching. However, UniverseMachine predicts that nearly all galaxies are actively star-forming below M * ∼ 107 M ⊙ and that these systems typically form more than half of their stars at z ≲ 4, which is discrepant with the star formation histories of Local Group dwarf galaxies that favor early quenching. This indicates that the current UniverseMachine model does not fully capture galaxy quenching physics at the low-mass end. We highlight specific improvements necessary to incorporate environmental and reionization-driven quenching for dwarf galaxies, and we provide a new tool to connect dark matter accretion to star formation over the full dynamic range that hosts galaxies.
Binary Subspace Chirps

We describe in detail the interplay between binary symplectic geometry and notions from quantum computation, with the ultimate goal of constructing highly structured codebooks. The Binary Chirps (BCs) are Complex Grassmannian Lines in <inline-formula> <tex-math notation="LaTeX">$N = 2^{m}$ </tex-math></inline-formula> dimensions used in deterministic compressed sensing and random/unsourced multiple access in wireless networks. Their entries are fourth roots of unity and can be described in terms of second order Reed-Muller codes. The Binary Subspace Chirps (BSSCs) are a unique collection of BCs of ranks ranging from <inline-formula> <tex-math notation="LaTeX">$r=0$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$r = m$ </tex-math></inline-formula>, embedded in <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> dimensions according to an on-off pattern determined by a rank <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> binary subspace. This yields a codebook that is asymptotically 2.38 times larger than the codebook of BCs, has the same minimum chordal distance as the codebook of BCs, and the alphabet is minimally extended from <inline-formula> <tex-math notation="LaTeX">$\{\pm 1,\pm i\}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$\{\pm 1,\pm i, 0\}$ </tex-math></inline-formula>. Equivalently, we show that BSSCs are stabilizer states, and we characterize them as columns of a well-controlled collection of Clifford matrices. By construction, the BSSCs inherit all the properties of BCs, which in turn makes them good candidates for a variety of applications. For applications in wireless communication, we use the rich algebraic structure of BSSCs to construct a low complexity decoding algorithm that is reliable against Gaussian noise. In simulations, BSSCs exhibit an error probability comparable or slightly lower than BCs, both for single-user and multi-user transmissions.
Casimir Effect for Fermion Condensate in Conical Rings

The fermion condensate (FC) is investigated for a (2+1)-dimensional massive fermionic field confined on a truncated cone with an arbitrary planar angle deficit and threaded by a magnetic flux. Different combinations of the boundary conditions are imposed on the edges of the cone. They include the bag boundary condition as a special case. By using the generalized Abel-Plana-type summation formula for the series over the eigenvalues of the radial quantum number, the edge-induced contributions in the FC are explicitly extracted. The FC is an even periodic function of the magnetic flux with the period equal to the flux quantum. Depending on the boundary conditions, the condensate can be either positive or negative. For a massless field the FC in the boundary-free conical geometry vanishes and the nonzero contributions are purely edge-induced effects. This provides a mechanism for time-reversal symmetry breaking in the absence of magnetic fields. Combining the results for the fields corresponding to two inequivalent irreducible representations of the Clifford algebra, the FC is investigated in the parity and time-reversal symmetric fermionic models and applications are discussed for graphitic cones.
Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks

In this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to VR users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for them using VLC. Here, VR users move in real time and their movement patterns change over time according to their applications, where both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and establish THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the average number of successfully served VR users by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm that adopts a meta-learning approach is proposed. The proposed meta policy gradient (MPG) algorithm enables the trained policy to quickly adapt to new user movement patterns. In order to solve the problem of maximizing the average number of successfully served users for VR scenarios with a large number of users, a dual method based MPG algorithm (D-MPG) with a low complexity is proposed. Simulation results demonstrate that, compared to a baseline trust region policy optimization algorithm (TRPO), the proposed MPG and D-MPG algorithms yield up to 26.8% and 21.9% improvement in the average number of successfully served users as well as 81.2% and 87.5% gains in the convergence speed, respectively.
How Can Human Values be Addressed in Agile Methods? A Case Study on SAFe

Agile methods are predominantly focused on delivering business values. But can Agile methods be adapted to effectively address and deliver human values such as social justice, privacy, and sustainability in the software they produce? Human values are what an individual or a society considers important in life. Ignoring these human values in software can pose difficulties or risks for all stakeholders (e.g., user dissatisfaction, reputation damage, financial loss). To answer this question, we selected the Scaled Agile Framework (SAFe), one of the most commonly used Agile methods in the industry, and conducted a qualitative case study to identify possible intervention points within SAFe that are the most natural to address and integrate human values in software. We present five high-level empirically-justified sets of interventions in SAFe: artefacts, roles, ceremonies, practices, and culture. We elaborate how some current Agile artefacts (e.g., user story), roles (e.g., product owner), ceremonies (e.g., stand-up meeting), and practices (e.g., business-facing testing) in SAFe can be modified to support the inclusion of human values in software. Further, our study suggests new and exclusive values-based artefacts (e.g., legislative requirement), ceremonies (e.g., values conversation), roles (e.g., values champion), and cultural practices (e.g., induction and hiring) to be introduced in SAFe for this purpose. Guided by our findings, we argue that existing Agile methods can account for human values in software delivery with some evolutionary adaptations.
Propylene polymerization through supported metallocene/ MAO catalysts: Kinetic analysis and modelling

Several propylene polymerizations are carried out with supported metallocene catalyst, prepared by reaction of silica gel with methylaluminoxane and then with rac-dimethylsilanediylbis(indenyl)zirconiumdichloride. The rate-time curves obtained are analyzed to understand the influence of support and triisobutylaluminium on the reaction. As a first attempt to model this system we developed a partially new particle growth model, based on a shell by shell fragmentation hypothesis (gradual break-up from the outside to the inside of the particle), and a final multigrain structure of the particle. A kinetic scheme including the TIBA interaction with the supported catalyst is also presented. The model prediction and the experimental data are compared for the rate-time curves and polymer properties.
Identifying Untrustworthy Predictions in Neural Networks by Geometric Gradient Analysis

The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in safety-critical applications. Most existing methods either require a re-training of a given model to achieve robust identification of adversarial attacks or are limited to out-of-distribution sample detection only. In this work, we propose a geometric gradient analysis (GGA) to improve the identification of untrustworthy predictions without retraining of a given model. GGA analyzes the geometry of the loss landscape of neural networks based on the saliency maps of their respective input. To motivate the proposed approach, we provide theoretical connections between gradients' geometrical properties and local minima of the loss function. Furthermore, we demonstrate that the proposed method outperforms prior approaches in detecting OOD data and adversarial attacks, including state-of-the-art and adaptive attacks.
Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning

Abstract:In 2001, Leo Breiman wrote of a divide between "data modeling" and "algorithmic modeling" cultures. Twenty years later this division feels far more ephemeral, both in terms of assigning individuals to camps, and in terms of intellectual boundaries. We argue that this is largely due to the "data modelers" incorporating algorithmic methods into their toolbox, particularly driven by recent developments in the statistical understanding of Breiman's own Random Forest methods. While this can be simplistically described as "Breiman won", these same developments also expose the limitations of the prediction-first philosophy that he espoused, making careful statistical analysis all the more important. This paper outlines these exciting recent developments in the random forest literature which, in our view, occurred as a result of a necessary blending of the two ways of thinking Breiman originally described. We also ask what areas statistics and statisticians might currently overlook.
Optical emission from light-like and particle-like excitons in monolayer transition metal dichalcogenides

Several monolayer transition metal dichalcogenides (TMDs) are direct band gap semiconductors and potentially efficient emitters in light emitting devices. Photons are emitted when strongly bound excitons decay radiatively, and accurate models of such excitons are important for a full understanding of the emission. Importantly, photons are emitted in directions uniquely determined by the exciton center of mass momentum and with lifetimes determined by the exciton transition matrix element. The exciton band structures of two-dimensional hexagonal materials, including TMDs, are highly unusual with coexisting particleand light-like bands. The latter is non-analytic with emission selection rules essentially opposite to the particle-like states, but has been ignored in analyses of TMD light emission so far. In the present work, we analyse the temperature and angular dependence of light emission from both exciton species and point out several important consequences of the unique exciton band structure. Within a first-principles Density-Functional-Theory+BetheSalpeter-Equation framework, we compute exciton band structures and optical matrix elements for the important TMDs MoS2, MoSe2, WS2, and WSe2. At low temperature, only the particlelike band is populated and our results agree with previous work. However, at slightly elevated temperatures, a significant population of the light-like band leads to modified angular emission patterns and lifetimes. Clear experimental fingerprints are predicted and explained by a simple four-state model incorporating spin-orbit as well as intervalley exchange coupling.
Enhancement of wave transmissions in multiple radiative and convective zones

Abstract In this paper, we study wave transmission in a rotating fluid with multiple alternating convectively stable and unstable layers. We have discussed wave transmissions in two different circumstances: cases where the wave is propagative in each layer and cases where wave tunnelling occurs. We find that efficient wave transmission can be achieved by ‘resonant propagation’ or ‘resonant tunnelling’, even when stable layers are strongly stratified, and we call this phenomenon ‘enhanced wave transmission’. Enhanced wave transmission only occurs when the total number of layers is odd (embedding stable layers are alternatingly embedded within clamping convective layers, or vice versa). For wave propagation, the occurrence of enhanced wave transmission requires that the clamping layers have similar properties, the thickness of each clamping layer is close to a multiple of the half-wavelength of the corresponding propagative wave and the total thickness of the embedded layers is close to a multiple of the half-wavelength of the corresponding propagating wave (resonant propagation). For wave tunnelling, we have considered two cases: tunnelling of gravity waves and tunnelling of inertial waves. In both cases, efficient tunnelling requires that the clamping layers have similar properties, the thickness of each embedded layer is much smaller than the corresponding e-folding decay distance and the thickness of each clamping layer is close to a multiple-and-a-half of the half-wavelength (resonant tunnelling).