Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressing RNNs for IoT devices by 15-38x using Kronecker Products

Recurrent Neural Networks (RNN) can be large and compute-intensive, making them hard to deploy on resource constrained devices. As a result, there is a need for compression technique that can significantly compress recurrent neural networks, without negatively impacting task accuracy. This paper introduces a method to compress RNNs for resource constrained environments using Kronecker products. We call the RNNs compressed using Kronecker products as Kronecker product Recurrent Neural Networks (KPRNNs). KPRNNs can compress the LSTM[22], GRU [9] and parameter optimized FastRNN [30] layers by 15 - 38x with minor loss in accuracy and can act as in-place replacement of most RNN cells in existing applications. By quantizing the Kronecker compressed networks to 8 bits, we further push the compression factor to 50x. We compare the accuracy and runtime of KPRNNs with other state-of-the-art compression techniques across 5 benchmarks spanning 3 different applications, showing its generality. Additionally, we show how to control the compression factors achieved by Kronecker products using a novel hybrid decomposition technique. We call the RNN cells compressed using Kronecker products with this control mechanism as hybrid Kronecker product RNNs (HKPRNN). Using HKPRNN, we compress RNN Cells in 2 benchmarks by 10x and 20x achieving better accuracy than other state-of-the-art compression techniques.
A tale of two omegas

We consider $\omega(n)$ and $\Omega(n)$, which respectively count the number of distinct and total prime factors of $n$. We survey a number of similarities and differences between these two functions, and study the summatory functions $L(x)=\sum_{n\leq x} (-1)^{\Omega(n)}$ and $H(x)=\sum_{n\leq x} (-1)^{\omega(n)}$ in particular. Questions about oscillations in both of these functions are connected to the Riemann hypothesis and other questions concerning the Riemann zeta function. We show that even though $\omega(n)$ and $\Omega(n)$ have the same parity approximately 73.5\% of the time, these summatory functions exhibit quite different behaviors: $L(x)$ is biased toward negative values, while $H(x)$ is unbiased. We also prove that $H(x)>1.7\sqrt{x}$ for infinitely many integers $x$, and $H(x)<-1.7\sqrt{x}$ infinitely often as well. These statements complement results on oscillations for $L(x)$.
Allocation of biomass and photoassimilates in juvenile plants of six Patagonian species in response to five water supply regimes.

BACKGROUND AND AIMS The growth-differentiation balance hypothesis (GDBH) states that there is a physiological trade-off between growth and secondary metabolism and predicts a parabolic effect of resource availability (such as water or nutrients) on secondary metabolite production. To test this hypothesis, the response of six Patagonian Monte species (Jarava speciosa, Grindelia chiloensis, Prosopis alpataco, Bougainvillea spinosa, Chuquiraga erinacea and Larrea divaricata) were investigated in terms of total biomass and resource allocation patterns in response to a water gradient. METHODS One-month-old seedlings were subjected to five water supply regimes (expressed as percentage dry soil weight: 13 %, 11 %, 9 %, 7 % or 5 % - field water capacity being 15 %). After 150 d, plants were harvested, oven-dried and partitioned into root, stem and leaf. Allometric analysis was used to correct for size differences in dry matter partitioning. Determinations of total phenolics (TP), condensed tannins (CT), nitrogen (N) and total non-structural carbohydrates (TNC) concentrations were done on each fraction. Based on concentrations and biomass data, contents of TP and CT were estimated for whole plants, and graphical vector analysis was applied to interpret drought effect. KEY RESULTS Four species (J. speciosa, G. chiloensis, P. alpataco and B. spinosa) showed a decrease in total biomass in the 5 % water supply regime. Differences in dry matter partitioning among treatments were mainly due to size variation. Concentrations of TP, CT, N and TNC varied little and the effect of drought on contents of TP and CT was not adequately predicted by the GDBH, except for G. chiloensis. CONCLUSIONS Water stress affected growth-related processes (i.e. reduced total biomass) rather than defence-related secondary metabolism or allocation to different organs in juvenile plants. Therefore, the results suggest that application of the GDBH to plants experiencing drought-stress should be done with caution, at least for Patagonian Monte species.
Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality

Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data. We posit that this phenomenon is caused by a mismatch between the model's typical set and its areas of high probability density. In-distribution inputs should reside in the former but not necessarily in the latter, as previous work has presumed. To determine whether or not inputs reside in the typical set, we propose a statistically principled, easy-to-implement test using the empirical distribution of model likelihoods. The test is model agnostic and widely applicable, only requiring that the likelihood can be computed or closely approximated. We report experiments showing that our procedure can successfully detect the out-of-distribution sets in several of the challenging cases reported by Nalisnick et al. (2019).
Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments

We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal. Our approach, Myopic Posterior Sampling (MPS), is inspired by the classical posterior (Thompson) sampling algorithm for multi-armed bandits and leverages the flexibility of probabilistic programming and approximate Bayesian inference to address a broad set of problems. Empirically, this general-purpose strategy is competitive with more specialised methods in a wide array of DOE tasks, and more importantly, enables addressing complex DOE goals where no existing method seems applicable. On the theoretical side, we leverage ideas from adaptive submodularity and reinforcement learning to derive conditions under which MPS achieves sublinear regret against natural benchmark policies.
Fatigue, alopecia and stomatitis among patients with breast cancer receiving cyclin-dependent kinase 4 and 6 inhibitors: a systematic review and meta-analysis

ABSTRACT Background: Cyclin-dependent kinase (CDK) inhibitors emerge as efficacious agents in hormone positive metastatic breast cancer with more acceptable toxicity profiles than cytotoxic chemotherapy. However, some adverse effects such as fatigue, alopecia and stomatitis, vastly concern patients. Methods: The search was conducted in PubMed, American Society of Clinical Oncology meeting library, European Society for Medical Oncology meeting abstract, and the San Antonio meeting abstract databases. We identified phase 2 or 3 trials recruiting patients with breast cancer, randomized to receive hormonal treatment plus either CDK4/6 inhibitors or placebo. We considered studies providing incidence of fatigue, alopecia and stomatitis relevant. Results: One thousand records were screened. 34 studies were considered relevant. Four studies were found to be eligible for meta-analysis with a total of 2007 patients. The relative risk for all grade fatigue was 1.34 [95% CI: 1.17–1.54, p < 0.0001], for all grade alopecia was 2.14 [95% CI: 1.23–3.73, p = 0.007], and for all grade stomatitis 4.87 [95% CI: 2.11–11.24, p = 0.0002]. In addition, the relative risk for high grade fatigue was 2.40 [95% CI: 1.10–5.26, p = 0.03]. Conclusion: CDK4/6 inhibitors were associated with an increased risk of fatigue, alopecia and stomatitis. Further studies with self-reported questionnaires may elucidate the impact of the increased risk of these selected adverse effects on the patients’ quality of life.
Growth rates of follicles in the ovary of the cow.

Follicular growth rates were studied in 5 Hereford-Holstein cross heifers on Day 14 of the oestrous cycle. The granulosa cell mitotic index (MI) was measured in non-atretic antral follicles of various diameters (0.13-8.57 mm) from Bouin-fixed ovaries collected before (199, control) and 2 h after colchicine treatment (189, treated). In control ovaries, follicles of 0.68-1.52 mm had a higher MI than those of other size classes (P less than 0.05). In colchicine-treated ovaries, the MI of follicles ranging from 0.68 to 8.57 mm increased more than that of other sized follicles, so that the mitotic time was shorter (0.78 h vs 1.32 h) in medium and large sized follicles (0.68-8.57 mm) than in smaller follicles (0.13-0.67 mm). Calculations based on the number of granulosa cells in follicles of various classes and from the time required to double the number of cells within a follicle indicate that a follicle takes 27 days to grow from 0.13 to 0.67 mm, 6.8 days from 0.68 to 3.67 mm and 7.8 days from 3.68 to 8.56 mm, indicating that growth rates varied with the size of the follicle. A period equivalent to 2 oestrous cycles would therefore be required for a follicle to grow through the antral phase, i.e. from 0.13 mm to preovulatory size. Increased MI, decreased mitotic time and increased atresia found in follicles larger than 0.68 mm could indicate a change in the follicular metabolism during its maturation.
Analyzing Social Media Data to Understand Consumer Information Needs on Dietary Supplements

Despite the high consumption of dietary supplements (DS), few reliable, relevant, and comprehensive online resources could satisfy information seekers. This research study aims to understand consumer information needs on DS using topic modeling, and to evaluate accuracy in correctly identifying topics from social media. We retrieved 16,095 unique questions posted on Yahoo! Answers relating to 438 unique DS ingredients mentioned in sub-section, “Alternative medicine” under the section, “Health”. We implemented an unsupervised topic modeling method, Correlation Explanation (CorEx) to unveil the various topics in which consumers are most interested. We manually reviewed the keywords of all the 200 topics generated by CorEx and assigned them to 38 health-related categories, corresponding to 12 higher-level groups. We found high accuracy (90–100%) in identifying questions that correctly align with the selected topics. The results could guide us to generate a more comprehensive and structured DS resource based on consumers’ information needs.
Non-Differentiable Supervised Learning with Evolution Strategies and Hybrid Methods

In this work we show that Evolution Strategies (ES) are a viable method for learning non-differentiable parameters of large supervised models. ES are black-box optimization algorithms that estimate distributions of model parameters; however they have only been used for relatively small problems so far. We show that it is possible to scale ES to more complex tasks and models with millions of parameters. While using ES for differentiable parameters is computationally impractical (although possible), we show that a hybrid approach is practically feasible in the case where the model has both differentiable and non-differentiable parameters. In this approach we use standard gradient-based methods for learning differentiable weights, while using ES for learning non-differentiable parameters - in our case sparsity masks of the weights. This proposed method is surprisingly competitive, and when parallelized over multiple devices has only negligible training time overhead compared to training with gradient descent. Additionally, this method allows to train sparse models from the first training step, so they can be much larger than when using methods that require training dense models first. We present results and analysis of supervised feed-forward models (such as MNIST and CIFAR-10 classification), as well as recurrent models, such as SparseWaveRNN for text-to-speech.
Renormalization group flow of holographic entanglement entropy and generalized temperature

In this work we study the renormalization group flow of holographic entanglement entropy in dimensions $d \geq 3$ in the gauge/gravity duality set up. We observe that a generalized temperature $T_g$ can be defined which produces the entanglement temperature $T_{ent}$ in the UV region and the Hawking temperature $T_H$ in the IR region. Furthermore, In the IR limit, $T_g$ produces the Hawking temperature $T_H$ along with some quantum corrections which bears the signature of short distance correlations along the entangling surface. This generalized temperature $T_g$ firmly captures the quantum mechanical to thermal crossover in the dual field theory by pointing out a critical value $l_c$ of the subsystem size at the boundary which we graphically represent for $AdS_{3+1}$ and $AdS_{4+1}$ black holes.
Devolatilization of Subducting Slabs, Part I: Thermodynamic Parameterization and Open System Effects

The amount of H2O and CO2 that is carried into deep mantle by subduction beyond subarc depths is of fundamental importance to the deep volatile cycle but remains debated. Given the large uncertainties surrounding the spatio‐temporal pattern of fluid flow and the equilibrium state within subducting slabs, a model of H2O and CO2 transport in slabs should be balanced between model simplicity and capability. We construct such a model in a two‐part contribution. In this Part I of our contribution, thermodynamic parameterization is performed for the devolatilization of representative subducting materials—sediments, basalts, gabbros, peridotites. The parameterization avoids reproducing the details of specific devolatilization reactions, but instead captures the overall behaviors of coupled (de)hydration and (de)carbonation. Two general, leading‐order features of devolatilization are captured: (1) the released volatiles are H2O‐rich near the onset of devolatilization; (2) increase of the ratio of bulk CO2 over H2O inhibits overall devolatilization and thus lessens decarbonation. These two features play an important role in simulation of volatile fractionation and infiltration in thermodynamically open systems. When constructing the reactive fluid flow model of slab H2O and CO2 transport in the companion paper Part II, this parameterization can be incorporated to efficiently account for the open‐system effects of H2O and CO2 transport.
Karyotyping of Saccharomyces strains with different temperature profiles

This study examined the karyotype, the fermentation performance and the optimum growth temperature (Topt) of 28 yeast strains all identified as species belonging to Saccharomyces sensu stricto. The strains were isolated from fermented musts, which had not been inoculated, at two temperature ranges: 20–40 °C and approximately 0–6 °C. The results demonstrated a correlation between the Topt and the chromosome organization. In particular, strains with Topt of less than 30 °C showed only two bands in the region between 365 and 225 kb, while those with a Topt greater than 30 °C had three bands in this size range. From a taxonomic viewpoint, the Topt is a better indicator for the Saccharomyces sp. than the ceiling temperature of 37 °C currently used to differentiate cryotolerant Saccharomyces bayanus and S. pastorianus from non‐cryotolerant S. cerevisiae and S. paradoxus strains.
A simplicial complex spliting associativity

We introduce a simplicial object $(\{ \Dy^m\}_{m\geq 0}, {\mathbb F}_i, {\mathbb S}_j)$ in the category of non-symmetric algebraic operads, satisfying that $\Dy^0$ is the operad of associative algebras and $\Dy^1$ is J.-L. Loday\rq s operad of dendriform algebras. The dimensions of the operad $\Dy^m$ are given by the Fuss-Catalan numbers. Given a family of partially ordered sets ${\bold P}=\{P_n\}_{n\geq 1}$ we show that, under certain conditions, the vector space spanned by the set of $m$-simpleces of ${\bold P}$ is a $\Dy^m$ algebra. This construction, applied to certain combinatorial Hopf algebras, whose associative product comes from a dendriform structure, provides examples of $\Dy^m$ algebras.
Classification and Construction of Topological Phases of Quantum Matter

We develop a theoretical framework for the classification and construction of symmetry protected topological (SPT) phases, which are a special class of zero-temperature phases of strongly interacting gapped quantum many-body systems that exhibit topological properties. The framework unifies various proposals for the classification of SPT phases, including the group (super-)cohomology proposal, the (spin-)cobordism proposal, the Freed-Hopkins proposal, and the Kitaev proposal. The power of the framework is demonstrated in a number of applications: (1) the classification and construction of 3D fermionic SPT phases in Wigner-Dyson classes A and AII with glide symmetry, (2) the classification and construction of 3D bosonic SPT phases with space-group symmetries for all 230 space groups, (3) the derivation of a Mayer-Vietoris sequence relating the classification of SPT phases with and without reflection symmetry, and (4) an interpretation of the structure of general crystalline SPT phases via the Atiyah-Hirzebruch spectral sequence. The framework is based on Kitaev's idea that short-range entangled states form what are known as $\Omega$-spectra in the sense of algebraic topology, and models the classification of SPT phases by what are called generalized cohomology theories.
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP

The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process (Frankle & Carbin, 2019). Intriguingly, this phenomenon suggests that initialization strategies for DNNs can be improved substantially, but the lottery ticket hypothesis has only previously been tested in the context of supervised learning for natural image tasks. Here, we evaluate whether "winning ticket" initializations exist in two different domains: natural language processing (NLP) and reinforcement learning (RL). For NLP, we examined both recurrent LSTM models and large-scale Transformer models (Vaswani et al., 2017). For RL, we analyzed a number of discrete-action space tasks, including both classic control and pixel control. Consistent with workin supervised image classification, we confirm that winning ticket initializations generally outperform parameter-matched random initializations, even at extreme pruning rates for both NLP and RL. Notably, we are able to find winning ticket initializations for Transformers which enable models one-third the size to achieve nearly equivalent performance. Together, these results suggest that the lottery ticket hypothesis is not restricted to supervised learning of natural images, but rather represents a broader phenomenon in DNNs.
Nonlinear gyrokinetic Coulomb collision operator

A gyrokinetic Coulomb collision operator is derived, which is particularly useful to describe the plasma dynamics at the periphery region of magnetic confinement fusion devices. The derived operator is able to describe collisions occurring in distribution functions arbitrarily far from equilibrium with variations on spatial scales at and below the particle Larmor radius. A multipole expansion of the Rosenbluth potentials is used in order to derive the dependence of the full Coulomb collision operator on the particle gyroangle. The full Coulomb collision operator is then expressed in gyrocentre phase-space coordinates, and a closed formula for its gyroaverage in terms of the moments of the gyrocentre distribution function in a form ready to be numerically implemented is provided. Furthermore, the collision operator is projected onto a Hermite–Laguerre velocity space polynomial basis and expansions in the small electron-to-ion mass ratio are provided.
Coding Theorems for Asynchronous Slepian–Wolf Coding Systems

The Slepian-Wolf (SW) coding system is a source coding system with two encoders and a decoder, where these encoders independently encode source sequences from two correlated sources into codewords, and the decoder reconstructs both source sequences from the codewords. In this paper, we consider the situation in which the SW coding system is asynchronous, i.e., each encoder samples a source sequence with some unknown delay. We assume that delays are unknown but maximum and minimum values of possible delays are known to encoders and the decoder. We also assume that sources are discrete stationary memoryless and the probability mass function (PMF) of the sources is unknown but the system knows that it belongs to a certain set of PMFs. For this asynchronous SW coding system, we clarify the achievable rate region which is the set of rate pairs of encoders such that the decoding error probability vanishes as the blocklength tends to infinity. We show that this region does not always coincide with that of the synchronous SW coding system in which each encoder samples a source sequence without any delay.
Cosmological constraints with deep learning from KiDS-450 weak lensing maps

Convolutional neural networks (CNNs) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular, they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density Ωm, the fluctuation amplitude σ8, and the intrinsic alignment amplitude AIA. We use a grid of N-body simulations to generate a training set of tomographic weak lensing maps. We test the robustness of the expected constraints to various effects, such as baryonic feedback, simulation accuracy, a different value of H0, or the light cone projection technique. We train a set of ResNet-based CNNs with varying depths to analyze sets of tomographic KiDS mass maps divided into 20 flat regions, with applied Gaussian smoothing of σ=2.34 arc min. The uncertainties on shear calibration and n(z) error are marginalized in the likelihood pipeline. Following a blinding scheme, we derive constraints on S8=σ8(Ωm/0.3)0.5=0.777-0.036+0.038 with our CNN analysis, with AIA=1.398-0.724+0.779. We compare this result to the power spectrum analysis on the same maps and likelihood pipeline and find an improvement of about 30% for the CNN. We discuss how our results offer excellent prospects for the use of deep learning in future cosmological data analysis.
Thermal Field Theory of the Tsallis statistics

Classical and quantum Tsallis distributions have been widely used in many branches of natural and social sciences. But, the quantum field theory of the Tsallis distributions is relatively a less explored arena. In this article we derive the expression for the thermal two-point functions for the Tsallis statistics with the help of the corresponding statistical mechanical formulations. We show that the quantum Tsallis distributions used in the literature appear in the thermal part of the propagator much in the same way the Boltzmann-Gibbs distributions appear in the conventional thermal field theory. As an application of our findings, thermal mass of the real scalar bosons subjected to phi^4 interaction has been calculated in the Tsallis statistics.