Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of regional Northern Hemisphere mid-latitude anthropogenic sulfur dioxide emissions on local and remote tropospheric oxidants

Abstract. The unintended consequences of reductions in regional anthropogenic sulfur dioxide (SO2) emissions implemented to protect human health are poorly understood. SO2 decreases began in the 1970s in the US and Europe and are expected to continue into the future, while recent emissions decreases in China are also projected to continue. In addition to the well documented climate effects (warming) from reducing aerosols, tropospheric oxidation is impacted via aerosol modification of photolysis rates and radical sinks. Impacts on the hydroxyl radical and other trace constituents directly affect climate and air quality metrics such as surface ozone levels. We use the Geophysical Fluid Dynamics Laboratory Atmospheric Model version 3 nudged towards National Centers for Environmental Prediction (NCEP) reanalysis wind velocities to estimate the impact of SO2 emissions from the United States, Europe, and China by differencing a control simulation with an otherwise identical simulation in which 2015 anthropogenic SO2 emissions are set to zero over one of the regions. Springtime sulfate aerosol changes occur both locally to the emission region and also throughout the Northern Hemispheric troposphere, including remote oceanic regions and the Arctic. The presence of sulfate aerosol strongly reduces hydroxyl (OH) and hydroperoxy (HO2) radicals by up to 10 % year-round throughout most of the troposphere north of 30° N latitude, the latter of which is directly removed via heterogeneous chemistry on aerosol surfaces, including sulfate, in the model. Regional SO2 emissions significantly increase nitrogen oxides (NOx) by about 5–8 % throughout most of the free troposphere in the Northern hemisphere by increasing the NOx lifetime as the heterogeneous sink on sulfate aerosol declines. Despite the NOx increases, tropospheric ozone decreases at northern mid-latitudes by 1–4 % zonally averaged and by up to 5 ppbv in surface air over China, as its response is dominated by the larger decreases (up to 2x) in HO2 and OH. Since 2015 anthropogenic SO2 emissions in China exceed those in the US or Europe, the oxidative response is greatest for the China perturbation simulation. Chemical effects of aerosols on oxidation (reactive uptake) dominate over radiative effects (photolysis rates), the latter of which are only statistically significant locally for the large perturbation over China. We find that the emissions decrease in China, which has yet to be fully realized, will have the largest impact on oxidants and related species in the Northern Hemisphere free troposphere compared to changes in Europe or the USA. Our results bolster previous calls for a multipollutant strategy for air pollution mitigation, to avoid the unintended consequence of aerosol removal leading to surface ozone increases that offset or mask surface ozone gains achieved by regulation of other pollutants, especially in countries where current usage of high-sulfur emitting fuels may be phased out in the future.
High Cell Density Cultivation for the Production of Industrially Important Engineered Bi-functional Recombinant Staphylokinase Variant from Salt Inducible Escherichia coli GJ1158

Most of the bacterial and other simple non glycosylated recombinant proteins were conventionally produced from IPTG inducible Escherichia coli BL21(DE3). Considering the factors like cost and toxic nature of IPTG, as an alternative, salt inducible Escherichia coli GJ1158 was used in this study for the over production of staphylokinase variant (sak – hirulog) using fed batch fermentation, cost effectively. Optimization of physico chemical factors viz., dissolved oxygen (DO), carbon, nitrogen and phosphate sources on bacterial growth for the production of recombinant sak – hirulog using batch and fed batch fermentation was studied. In batch culture, increased DO at more than 30 % did not influence the enhanced expression of sak – hirulog, but significant improvement was observed in fed batch cultivation. Supplementation of production medium with different nutrient sources like dextrose, yeast extract and dipotassium hydrogen phosphate (K2HPO4) enhanced the sak – hirulog expression in fed batch fermentation process even without disturbing the cell growth by providing 50 % DO. Approximately 1178 mg/L of specific protein was obtained using cost effective modified glucose yeast exgtract (GYE) media devoid of sodium chloride. This study also reports the highest concentration of recombinant protein from salt inducible expression host till to date, which manages to satisfy the production of bifunctional staphaphylokinase variant using economically feasible bacterial expression host Escherichia coli GJ1158.
Shard scheduler: object placement and migration in sharded account-based blockchains

We propose Shard Scheduler, a system for object placement and migration in account-based sharded blockchains. Our system calculates optimal placement and decides on object migrations across shards. It supports complex multi-account transactions caused by smart contracts. Placement and migration decisions made by Shard Scheduler are fully deterministic, verifiable, and can be made part of the consensus protocol. Shard Scheduler reduces the number of costly cross-shard transactions, ensures balanced load distribution and maximizes the number of processed transactions for the blockchain as a whole. To this end, it leverages a novel incentive model motivating miners to maximize the global throughput of the entire blockchain rather than the throughput of a specific shard. In our simulations, Shard Scheduler can reduce the number of costly cross-shard transactions by half while ensuring equal load and increasing throughput more than 2 fold when using 60 shards. We also implement and evaluate Shard Scheduler on Chainspace, more than doubling its throughput and reducing user-perceived latency by 70% when using 10 shards.
A64FX – Your Compiler You Must Decide!

The current number one of the TOP500 list, Supercomputer Fugaku, has demonstrated that CPU-only HPC systems aren’t dead and CPUs can be used for more than just being the host controller for a discrete accelerators. While the specifications of the chip and overall system architecture, and benchmarks submitted to various lists, like TOP500 and Green500, etc., are clearly highlighting the potential, the proliferation of Arm into the HPC business is rather recent and hence the software stack might not be fully matured and tuned, yet. We test 3 state-of-the-art compiler suite against a broad set of benchmarks. Our measurements show that orders of magnitudes in performance can be gained by deviating from the recommended usage model of the A64FX compute nodes.
Relativistic Langevin equation derived from a particle-bath Lagrangian

We show how a relativistic Langevin equation can be derived from a Lorentz-covariant version of the Caldeira–Leggett particle-bath Lagrangian. In one of its limits, we identify the obtained equation with the Langevin equation used in contemporary extensions of statistical mechanics to the near-light-speed motion of a tagged particle in non-relativistic dissipative fluids. The proposed framework provides a more rigorous and first-principles form of the weakly-relativistic and partially-relativistic Langevin equations often quoted or postulated as ansatz in previous works. We then refine the aforementioned results to obtain a generalized Langevin equation valid for the case of both fully-relativistic particle and bath, using an analytical approximation obtained from numerics where the Fourier modes of the bath are systematically replaced with covariant plane-wave forms with a length-scale relativistic correction that depends on the space-time trajectory in a parabolic way. A new relativistic force term appears in this fully-relativistic limit, which has been derived here for the first time. We discuss the implications of the apparent breaking of space-time translation and parity invariance, showing that these effects are not necessarily in contradiction with the assumptions of statistical mechanics. The intrinsically non-Markovian character of the fully relativistic generalised Langevin equation derived here, and of the associated fluctuation–dissipation theorem, is also discussed.
Non-classical polynomials and the inverse theorem

Abstract In this paper we characterize when non-classical polynomials are necessary in the inverse theorem for the Gowers $U^k$ -norm. We give a brief deduction of the fact that a bounded function on $\mathbb F_p^n$ with large $U^k$ -norm must correlate with a classical polynomial when $k\le p+1$ . To the best of our knowledge, this result is new for $k=p+1$ (when $p>2$ ). We then prove that non-classical polynomials are necessary in the inverse theorem for the Gowers $U^k$ -norm over $\mathbb F_p^n$ for all $k\ge p+2$ , completely characterising when classical polynomials suffice.
Mitigating Memorization in Sample Selection for Learning with Noisy Labels

Because deep learning is vulnerable to noisy labels, sample selection techniques, which train networks with only clean labeled data, have attracted a great attention. However, if the labels are dominantly corrupted by few classes, these noisy samples are called dominant-noisy-labeled samples, the network also learns dominant-noisy-labeled samples rapidly via content-aware optimization. In this study, we propose a compelling criteria to penalize dominant-noisy-labeled samples intensively through class-wise penalty labels. By averaging prediction confidences for the each observed label, we obtain suitable penalty labels that have high values if the labels are largely corrupted by some classes. Experiments were performed using benchmarks (CIFAR-10, CIFAR-100, Tiny-ImageNet) and real-world datasets (ANIMAL-10N, Clothing1M) to evaluate the proposed criteria in various scenarios with different noise rates. Using the proposed sample selection, the learning process of the network becomes significantly robust to noisy labels compared to existing methods in several noise types.
A Deep Search for Five Molecules in the 49 Ceti Debris Disk

Surprisingly strong CO emission has been observed from more than a dozen debris disks around nearby main-sequence stars. The origin of this CO is unclear, in particular whether it is left over from the protoplanetary disk phase or is second-generation material released from collisions between icy bodies like debris dust. The primary unexplored avenue for distinguishing the origin of the material is understanding its molecular composition. Here we present a deep search for five molecules (CN, HCN, HCO+, SiO, and CH3OH) in the debris disk around 49 Ceti. We take advantage of the high sensitivity of the Atacama Large Millimeter/submillimeter Array at Band 7 to integrate for 3.2 hr at modest spatial (1″) and spectral (0.8 km s−1) resolution. Our search yields stringent upper limits on the flux of all surveyed molecular lines, which imply abundances relative to CO that are orders of magnitude lower than those observed in protoplanetary disks and solar system comets, and also those predicted in outgassing models of second-generation material. However, if C i shielding is responsible for extending the lifetime of any CO produced in second-generation collisions as proposed by Kral et al., then the line ratios do not reflect true ice phase chemical abundances but rather imply that CO is shielded by its own photodissociation product, C i, and other molecules are rapidly photodissociated by the stellar and interstellar radiation field.
Predicting market inflation expectations with news topics and sentiment

This study presents a novel approach to incorporating news topics and their associated sentiment into predictions of breakeven inflation rate (BEIR) movements for eight countries with mature bond markets. We calibrate five classes of machine learning models including narrative-based features for each country, and find that they generally outperform corresponding benchmarks that do not include such features. We find Logistic Regression and XGBoost classifiers to deliver the best performance across countries. We complement these results with a feature importance analysis, showing that economic and financial topics are the key performance drivers in our predictions, with additional contributions from topics related to health and government. We examine cross-country spillover effects of news narrative on BEIR via Graphical Granger Causality and confirm their existence for the US and Germany, while five other countries considered in our study are only influenced by local narrative.
Personalized and Reliable Decision Sets: Enhancing Interpretability in Clinical Decision Support Systems

In this study, we present a novel clinical decision support system and discuss its interpretability-related properties. It combines a decision set of rules with a machine learning scheme to offer global and local interpretability. More specifically, machine learning is used to predict the likelihood of each of those rules to be correct for a particular patient, which may also contribute to better predictive performances. Moreover, the reliability analysis of individual predictions is also addressed, contributing to further personalized interpretability. The combination of these several elements may be crucial to obtain the clinical stakeholders' trust, leading to a better assessment of patients' conditions and improvement of the physicians' decision-making.
Cross-Modal Variational Auto-Encoder for Content-Based Micro-Video Background Music Recommendation

In this paper, we propose a cross-modal variational auto-encoder (CMVAE) for content-based micro-video background music recommendation. CMVAE is a hierarchical Bayesian generative model that matches relevant background music to a micro-video by projecting these two multimodal inputs into a shared low-dimensional latent space, where the alignment of two corresponding embeddings of a matched video-music pair is achieved by cross-generation. Moreover, the multimodal information is fused by the product-of-experts (PoE) principle, where the semantic information in visual and textual modalities of the micro-video are weighted according to their variance estimations such that the modality with a lower noise level is given more weights. Therefore, the micro-video latent variables contain less irrelevant information that results in a more robust model generalization. Furthermore, we establish a large-scale content-based micro-video background music recommendation dataset, TT-150k, composed of extracted features from approximately 3,000 different background music clips associated to 150,000 micro-videos from different users. Extensive experiments on the established TT-150k dataset demonstrate the effectiveness of the proposed method. A qualitative assessment of CMVAE by visualizing some recommendation results is also included.
Predicting the near-wall region of turbulence through convolutional neural networks

Modelling the near-wall region of wall-bounded turbulent flows is a widespread practice to reduce the computational cost of large-eddy simulations (LESs) at high Reynolds number. As a first step towards a data-driven wall-model, a neural-network-based approach to predict the near-wall behaviour in a turbulent open channel flow is investigated. The fully-convolutional network (FCN) proposed by Guastoni et al. [preprint, arXiv:2006.12483] is trained to predict the two-dimensional velocity-fluctuation fields at $y^{+}_{\rm target}$, using the sampled fluctuations in wall-parallel planes located farther from the wall, at $y^{+}_{\rm input}$. The data for training and testing is obtained from a direct numerical simulation (DNS) at friction Reynolds numbers $Re_{\tau} = 180$ and $550$. The turbulent velocity-fluctuation fields are sampled at various wall-normal locations, i.e. $y^{+} = \{15, 30, 50, 80, 100, 120, 150\}$. At $Re_{\tau}=550$, the FCN can take advantage of the self-similarity in the logarithmic region of the flow and predict the velocity-fluctuation fields at $y^{+} = 50$ using the velocity-fluctuation fields at $y^{+} = 100$ as input with less than 20% error in prediction of streamwise-fluctuations intensity. These results are an encouraging starting point to develop a neural-network based approach for modelling turbulence at the wall in numerical simulations.
Neural Architecture Search using Covariance Matrix Adaptation Evolution Strategy

Evolution-based neural architecture search methods have shown promising results but they require high computational resources since these methods involve training each candidate architecture from scratch and then evaluating its fitness which results in long search time. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has shown promising results in tuning hyperparameters of neural networks but has not been used for neural architecture search. In this work, we propose a framework called CMANAS which applies the faster convergence property of CMA-ES to the deep neural architecture search problem. Instead of training each individual architecture seperately, we used the accuracy of a trained one shot model (OSM) on the validation data as a prediction of the fitness of the architecture resulting in reduced search time. We also used an architecture-fitness table (AF table) for keeping record of the already evaluated architecture, thus further reducing the search time. The architectures are modelled using a normal distribution, which is updated using CMA-ES based on the fitness of the sampled population. Experimentally, CMANAS achieves better results than previous evolution-based methods while reducing the search time significantly. The effectiveness of CMANAS is shown on 2 different search spaces for datasets: CIFAR-10, CIFAR-100, ImageNet and ImageNet16-120. All the results show that CMANAS is a viable alternative to previous evolution-based methods and extends the application of CMA-ES to the deep neural architecture search field.
Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments

Today's large-scale algorithms have become immensely influential, as they recommend and moderate the content that billions of humans are exposed to on a daily basis. They are the de-facto regulators of our societies' information diet, from shaping opinions on public health to organizing groups for social movements. This creates serious concerns, but also great opportunities to promote quality information. Addressing the concerns and seizing the opportunities is a challenging, enormous and fabulous endeavor, as intuitively appealing ideas often come with unwanted {\it side effects}, and as it requires us to think about what we deeply prefer. Understanding how today's large-scale algorithms are built is critical to determine what interventions will be most effective. Given that these algorithms rely heavily on {\it machine learning}, we make the following key observation: \emph{any algorithm trained on uncontrolled data must not be trusted}. Indeed, a malicious entity could take control over the data, poison it with dangerously manipulative fabricated inputs, and thereby make the trained algorithm extremely unsafe. We thus argue that the first step towards safe and ethical large-scale algorithms must be the collection of a large, secure and trustworthy dataset of reliable human judgments. To achieve this, we introduce \emph{Tournesol}, an open source platform available at \url{https://tournesol.app}. Tournesol aims to collect a large database of human judgments on what algorithms ought to widely recommend (and what they ought to stop widely recommending). We outline the structure of the Tournesol database, the key features of the Tournesol platform and the main hurdles that must be overcome to make it a successful project. Most importantly, we argue that, if successful, Tournesol may then serve as the essential foundation for any safe and ethical large-scale algorithm.
MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators

We propose a generative adversarial network with multiple discriminators, where each discriminator is specialized to distinguish the subset of a real dataset. This approach facilitates learning a generator coinciding with the underlying data distribution and thus mitigates the chronic mode collapse problem. From the inspiration of multiple choice learning, we guide each discriminator to have expertise in the subset of the entire data and allow the generator to find reasonable correspondences between the latent and real data spaces automatically without supervision for training examples and the number of discriminators. Despite the use of multiple discriminators, the backbone networks are shared across the discriminators and the increase of training cost is minimized. We demonstrate the effectiveness of our algorithm in the standard datasets using multiple evaluation metrics.
Towards Natural Brain-Machine Interaction using Endogenous Potentials based on Deep Neural Networks

Human-robot collaboration has the potential to maximize the efficiency of the operation of autonomous robots. Brain-machine interface (BMI) would be a desirable technology to collaborate with robots since the intention or state of users can be translated from the neural activities. However, the electroencephalogram (EEG), which is one of the most popularly used non-invasive BMI modalities, has low accuracy and a limited degree of freedom (DoF) due to a low signal-to-noise ratio. Thus, improving the performance of multi-class EEG classification is crucial to develop more flexible BMI-based human-robot collaboration. In this study, we investigated the possibility for inter-paradigm classification of multiple endogenous BMI paradigms, such as motor imagery (MI), visual imagery (VI), and speech imagery (SI), to enhance the limited DoF while maintaining robust accuracy. We conducted the statistical and neurophysiological analyses on MI, VI, and SI and classified three paradigms using the proposed temporal information-based neural network (TINN). We confirmed that statistically significant features could be extracted on different brain regions when classifying three endogenous paradigms. Moreover, our proposed TINN showed the highest accuracy of 0.93 compared to the previous methods for classifying three different types of mental imagery tasks (MI, VI, and SI).
What and When to Look?: Temporal Span Proposal Network for Video Visual Relation Detection

—Identifying relations between objects is central to understanding the scene. While several works have been proposed for relation modeling in the image domain, there have been many constraints in the video domain due to challenging dynamics of spatio-temporal interactions ( e.g. , Between which objects are there an interaction? When do relations occur and end? ). To date, two representative methods have been proposed to tackle Video Visual Relation Detection (VidVRD): segment-based and window-based. We ﬁrst point out the limitations these two methods have and propose Temporal Span Proposal Network (TSPN), a novel method with two advantages in terms of efﬁciency and effectiveness. 1) TSPN tells what to look : it sparsiﬁes relation search space by scoring relationness ( i.e. , conﬁdence score for the existence of a relation between pair of objects) of object pair. 2) TSPN tells when to look : it leverages the full video context to simultaneously predict the temporal span and categories of the entire relations. TSPN demonstrates its effectiveness by achieving new state-of-the-art by a signiﬁcant margin on two VidVRD benchmarks (ImageNet-VidVDR and VidOR) while also showing lower time complexity than existing methods – in particular, twice as efﬁcient as a popular segment-based approach.