Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Biomanufacturing Harvesting Decisions under Limited Historical Data

In biopharmaceutical manufacturing, fermentation processes play a critical role in productivity and proﬁt. A fermentation process uses living cells with complex biological mechanisms, and this leads to high variability in the process outputs, namely, the protein and impurity levels. By building on the biological mechanisms of protein and impurity growth, we introduce a stochastic model to characterize the accumulation of the protein and impurity levels in the fermentation process. However, a common challenge in the industry is the availability of only a very limited amount of data, especially in the development and early stage of production. This adds an additional layer of uncertainty, referred to as model risk, due to the difﬁculty of estimating the model parameters with limited data. In this paper, we study the harvesting decision for a fermentation process under model risk (i.e., when to stop the fermentation and collect the production reward). We adopt a Bayesian approach to update the unknown parameters of the growth-rate distributions, and use the resulting posterior distributions to characterize the impact of model risk on fermentation output variability. The harvesting problem is formulated as a Markov decision process model with knowledge states that summarize the posterior distributions and hence incorporate the model risk in decision-making. The resulting model is solved by using a reinforcement learning algorithm based on Bayesian sparse sampling. We provide analytical results on the structure of the optimal policy and its objective function, and explicitly study the impact of model risk on harvesting decisions. Our case studies at MSD Animal Health demonstrate that the proposed model and solution approach improve the harvesting decisions in real life by achieving substantially higher average output from a fermentation batch along with lower batch-to-batch variability.
Prediction of surface tension and surface concentration of binary refrigerant system (R290/R600a) at various temperatures and pressures

ABSTRACT The surface tension of a binary refrigerant mixture of R290(propane: C3H8) and R600a(isobutane:i-C4H10) has been calculated by using critical constants (Pc, Vc and Tc) and acentric factor (ω) at three isotherms of 278 K, 300 K and 320 K over the pressure range from 187.7 to 1540.2 kPa. In this paper, new formalism has been made by using simple mixing rule for modifying the predictive models: Brock–Bird, Pitzer, Hakim et al., Bolotin, Sastri–Rao and Zuo–Stenby. On comparing the computed values of surface tension with experimental data, satisfactory results have been observed. The average absolute deviation (AAD) obtained from the comparison of experimental and calculated surface tension values for six models is less than 1.9%. Finally, in a new approach, the extended Langmuir model (EL) was used to finding more information about the surface structure and surface concentration of binary refrigerant mixtures.
Conditional and Adversarial Euler-based Generators for Time Series

We introduce three new generative models for time series. Based on Euler discretization and Wasserstein metrics, they are able to capture time marginal distributions and temporal dynamics. Two of these methods rely on the adaptation of generative adversarial networks (GANs) to time series. Both of them outperform state-of-the-art benchmarks by capturing the underlying temporal structure on synthetic time series. The third algorithm, called Conditional Euler Generator (CEGEN), minimizes a dedicated distance between the transition probability distributions over all time steps. In the context of Itô processes, we provide theoretical guarantees that minimizing this criterion implies accurate estimations of the drift and volatility parameters. We demonstrate empirically that CEGEN outperforms state-of-the-art and GAN generators on both marginal and temporal dynamics metrics. Besides, it identiﬁes accurate correlation structures in high dimension. When few data points are available, we verify the eﬀectiveness of CEGEN, when combined with transfer learning methods on Monte Carlo simulations. Finally, we illustrate the robustness of our method on various real-world datasets.
Proof methods for robust low-rank matrix recovery

Low-rank matrix recovery problems arise naturally as mathematical formulations of various inverse problems, such as matrix completion, blind deconvolution, and phase retrieval. Over the last two decades, a number of works have rigorously analyzed the reconstruction performance for such scenarios, giving rise to a rather general understanding of the potential and the limitations of low-rank matrix models in sensing problems. In this article, we compare the two main proof techniques that have been paving the way to a rigorous analysis, discuss their potential and limitations, and survey their successful applications. On the one hand, we review approaches based on descent cone analysis, showing that they often lead to strong guarantees even in the presence of adversarial noise, but face limitations when it comes to structured observations. On the other hand, we discuss techniques using approximate dual certificates and the golfing scheme, which are often better suited to deal with practical measurement structures, but sometimes lead to weaker guarantees. Lastly, we review recent progress towards analyzing descent cones also for structured scenarios -- exploiting the idea of splitting the cones into multiple parts that are analyzed via different techniques.
Expressivity of Emergent Language is a Trade-off between Contextual Complexity and Unpredictability

Researchers are using deep learning models to explore the emergence of language in various language games, where agents interact and develop an emergent language to solve tasks. We focus on the factors that determine the expressivity of emergent languages, which reflects the amount of information about input spaces those languages are capable of encoding. We measure the expressivity of emergent languages based on the generalisation performance across different games, and demonstrate that the expressivity of emergent languages is a trade-off between the complexity and unpredictability of the context those languages emerged from. Another contribution of this work is the discovery of message type collapse, i.e. the number of unique messages is lower than that of inputs. We also show that using the contrastive loss proposed by Chen et al. (2020) can alleviate this problem.
Equations of state of new boron-rich selenides B6Se and B12Se

ABSTRACT Two novels boron-rich selenides, orthorhombic B6Se and rhombohedral B12Se, have been recently synthesized at high pressures and high temperatures. Room-temperature compressibilities of these phases were studied in a diamond anvil cell using synchrotron powder X-ray diffraction. A fit of experimental p–V data by the third-order Birch–Murnaghan equation of state yielded the bulk moduli of 155(2) GPa for B12Se and 144(3) GPa for B6Se. No pressure-induced phase transitions have been observed in the studied pressure range, that is, up to 35 GPa.
Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation

Deep neural networks have been widely used for feature learning in facial expression recognition systems. However, small datasets and large intra-class variability can lead to overfitting. In this paper, we propose a method which learns an optimized compact network topology for real-time facial expression recognition utilizing localized facial landmark features. Our method employs a spatio-temporal bilinear layer as backbone to capture the motion of facial landmarks during the execution of a facial expression effectively. Besides, it takes advantage of Monte Carlo Dropout to capture the model’s uncertainty which is of great importance to analyze and treat uncertain cases. The performance of our method is evaluated on three widely used datasets and it is comparable to that of video-based state-of-the-art methods while it has much less complexity.
SN 2015bf: A fast declining type II supernova with flash-ionized signatures

We present optical and ultraviolet photometry, as well as optical spectra, for the type II supernova (SN) 2015bf. Our observations cover the phases from ∼ 2 to ∼ 200 d after explosion. The first spectrum is characterised by a blue continuum with a blackbody temperature of∼ 24,000 K and flash-ionised emission lines. After about one week, the spectra of SN 2015bf evolve like those of a regular SN II. From the luminosity of the narrow emission component of Hα , we deduce that the mass-loss rate is larger than ∼ 3.7×10−3 M yr−1. The disappearance of the flash features in the first week after explosion indicates that the circumstellar material is confined within ∼ 6× 1014 cm. Thus, we suggest that the progenitor of SN 2015bf experienced violent mass loss shortly before the supernova explosion. The multiband light curves show that SN 2015bf has a high peak luminosity with an absolute visual magnitude MV =−18.11±0.08 mag and a fast post-peak decline with a V -band decay of 1.22±0.09 mag within ∼ 50 d after maximum light. Moreover, the R-band tail luminosity of SN 2015bf is fainter than that of SNe II with similar peak by 1–2 mag, suggesting a small amount of 56Ni (∼ 0.009M ) synthesised during the explosion. Such a low nickel mass indicates that the progenitor of SN 2015bf could be a super-asymptotic-giant-branch star that collapsed owing to electron capture.
News consumption and social media regulations policy

Users online tend to consume information adhering to their system of beliefs and to ignore dissenting information. During the COVID-19 pandemic, users get exposed to a massive amount of information about a new topic having a high level of uncertainty. In this paper, we analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation concerning COVID-19. We compare the two platforms on about three million pieces of content analyzing user interaction with respect to news articles. We first describe users' consumption patterns on the two platforms focusing on the political leaning of news outlets. Finally, we characterize the echo chamber effect by modeling the dynamics of users' interaction networks. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content, with a consequent affiliation towards reliable sources in terms of engagement and comments. Conversely, the lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior. Twitter users show segregation towards reliable content with a uniform narrative. Gab, instead, offers a more heterogeneous structure where users, independently of their leaning, follow people who are slightly polarized towards questionable news.
Coresets for Classification - Simplified and Strengthened

We give relative error coresets for training linear classifiers with a broad class of loss functions, including the logistic loss and hinge loss. Our construction achieves $(1\pm \epsilon)$ relative error with $\tilde O(d \cdot \mu_y(X)^2/\epsilon^2)$ points, where $\mu_y(X)$ is a natural complexity measure of the data matrix $X \in \mathbb{R}^{n \times d}$ and label vector $y \in \{-1,1\}^n$, introduced in by Munteanu et al. 2018. Our result is based on subsampling data points with probabilities proportional to their $\ell_1$ $Lewis$ $weights$. It significantly improves on existing theoretical bounds and performs well in practice, outperforming uniform subsampling along with other importance sampling methods. Our sampling distribution does not depend on the labels, so can be used for active learning. It also does not depend on the specific loss function, so a single coreset can be used in multiple training scenarios.
Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty

We show that a single softmax neural net with minimal changes can beat the uncertainty predictions of Deep Ensembles and other more complex single-forward-pass uncertainty approaches. Standard softmax neural nets suffer from feature collapse and extrapolate arbitrarily for OoD points. This results in arbitrary softmax entropies for OoD points which can have high entropy, low, or anything in between, thus cannot capture epistemic uncertainty reliably. We prove that this failure lies at the core of "why" Deep Ensemble Uncertainty works well. Instead of using softmax entropy, we show that with appropriate inductive biases softmax neural nets trained with maximum likelihood reliably capture epistemic uncertainty through their feature-space density. This density is obtained using simple Gaussian Discriminant Analysis, but it cannot represent aleatoric uncertainty reliably. We show that it is necessary to combine feature-space density with softmax entropy to disentangle uncertainties well. We evaluate the epistemic uncertainty quality on active learning and OoD detection, achieving SOTA ∼ 98 AUROC on CIFAR-10 vs SVHN without ﬁne-tuning on OoD data.
Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions

In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said. In education, teachers’ uptake of student contributions has been linked to higher student achievement. Yet measuring and improving teachers’ uptake at scale is challenging, as existing methods require expensive annotation by experts. We propose a framework for computationally measuring uptake, by (1) releasing a dataset of student-teacher exchanges extracted from US math classroom transcripts annotated for uptake by experts; (2) formalizing uptake as pointwise Jensen-Shannon Divergence (pJSD), estimated via next utterance classification; (3) conducting a linguistically-motivated comparison of different unsupervised measures and (4) correlating these measures with educational outcomes. We find that although repetition captures a significant part of uptake, pJSD outperforms repetition-based baselines, as it is capable of identifying a wider range of uptake phenomena like question answering and reformulation. We apply our uptake measure to three different educational datasets with outcome indicators. Unlike baseline measures, pJSD correlates significantly with instruction quality in all three, providing evidence for its generalizability and for its potential to serve as an automated professional development tool for teachers.
Electrostatic Interaction in Stochastic Electrodynamics

Abstract In this paper, the expression of the electrostatic interaction force between two charged particles is derived in the framework of Stochastic Electrodynamics. The fundamental assumption is that the electrically charged particle can be modeled as a two-dimensional oscillator that scatters the classical zero point field background radiation. The correct expression of the electrostatic force is obtained if the natural pulsation of the oscillator is equal to the Zitterbewegung angular velocity.
Searching for consistent associations with a multi-environment knockoff filter

This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across diverse environments, controlling the false discovery rate. The motivation for this problem is that large data sets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations consistently replicated under different conditions may be more interesting. In fact, consistency sometimes provably leads to valid causal inferences even if conditional associations do not. While the proposed method is flexible and can be deployed in a wide range of applications, this paper highlights its relevance to genome-wide association studies, in which consistency across populations with diverse ancestries mitigates confounding due to unmeasured variants. The effectiveness of this approach is demonstrated by simulations and applications to the UK Biobank data.
DisTop: Discovering a Topological Representation to Learn Diverse and Rewarding Skills

An efficient way for a deep reinforcement learning (RL) agent to explore in sparse-rewards settings can be to learn a set of skills that achieves a uniform distribution of terminal states. We introduce DisTop, a new model that simultaneously learns diverse skills and focuses on improving rewarding skills. DisTop progressively builds a discrete topology of the environment using an unsupervised contrastive loss, a growing network, and a goal-conditioned policy. Using this topology, a state-independent hierarchical policy can select which skill to execute and learn. In turn, the new set of visited states allows an improved learned representation. Our experiments emphasize that DisTop is agnostic to the ground state representation and that the agent can discover the topology of its environment whether the states are high-dimensional binary data, images, or proprioceptive inputs. We demonstrate that this paradigm is competitive on MuJoCo benchmarks with state-of-the-art (SOTA) algorithms on both single-task dense rewards and diverse skill discovery without rewards. By combining these two aspects, we show that DisTop outperforms a SOTA hierarchical RL algorithm when rewards are sparse. We believe DisTop opens new perspectives by showing that bottom-up skill discovery combined with dynamic-aware representation learning can tackle different complex state spaces and reward settings.
The slope of the low-energy spectrum of prompt gamma-ray burst emission

Gamma-ray Bursts (GRBs) prompt emission spectra are often fitted with the empirical “Band" function, namely two power laws smoothly connected. The typical slope of the low energy (sub-MeV) power law is αBand ' −1. In a small fraction of long GRBs this power law splits into two components such that the spectrum presents, in addition to the typical ∼MeV νFν peak, a break at the order of a few keV or hundreds keV. The typical power law slopes below and above the break are –0.6 and –1.5 respectively. If the break is a common feature, the value of αBand could be an “average” of the spectral slopes below and above the break in GRBs fitted with Band function. We analyze the spectra of 27 (9) bright long (short) GRBs detected by the Fermi satellite finding a low energy break between 80 keV and 280 keV in 12 long GRBs, but in none of the short events. Through spectral simulations we show that if the break is moved closer (farther) to the peak energy a relatively harder (softer) αBand is found by fitting the simulated spectra with the Band function. The hard average slope αBand ' −0.38 found in short GRBs suggests that the break is close to the peak energy. We show that for 15 long GRBs best fitted by the Band function only, the break could be present, but it is not identifiable in the Fermi/GBM spectrum, because either at low energies, close to the detector limit for relatively soft αBand . −1, or in the proximity of the energy peak for relatively hard αBand & −1. A spectrum with two breaks could be typical of GRB prompt emission, though hard to identify with current detectors. Instrumental design such that conceived for the THESEUS space mission, extending from 0.3 keV to several MeV and featuring a larger effective area with respect to Fermi/GBM, can reveal a larger fraction of GRBs with a spectral energy
NISQ Algorithm for Semidefinite Programming

Semideﬁnite programs (SDPs) are convex optimization programs with vast applications in control theory, quantum information, combinatorial optimization and operational research. Noisy intermediate-scale quantum (NISQ) algorithms aim to make an eﬃcient use of the current generation of quantum hardware. However, optimizing variational quantum algorithms is a challenge as it is an NP-hard problem that in general requires an exponential time to solve and can contain many far from optimal local minima. Here, we present a current term NISQ algorithm for solving SDPs. The classical optimization program of our NISQ solver is another SDP over a lower dimensional ansatz space. We harness the SDP based formulation of the Hamiltonian ground state problem to design a NISQ eigensolver. Unlike variational quantum eigensolvers, the classical optimization program of our eigensolver is convex, can be solved in polynomial time with the number of ansatz parameters and every local minimum is a global minimum. We ﬁnd numeric evidence that NISQ SDP can improve the estimation of ground state energies in a scalable manner. Further, we eﬃciently solve constrained problems to calculate the excited states of Hamiltonians, ﬁnd the lowest energy of symmetry constrained Hamiltonians and determine the optimal measurements for quantum state discrimination. We demonstrate the potential of our approach by ﬁnding the largest eigenvalue of up to 2 1000 dimensional matrices and solving graph problems related to quantum contextuality. We also discuss NISQ algorithms for rank-constrained SDPs. Our work extends the application of NISQ computers onto one of the most successful algorithmic frameworks of the past few decades.
Trade liberalization and state aid in the European Union

The prospective WTO Millennium Round of negotiations will highlight critical economic issues regarding the application and implementation of the WTO rules to international trade in goods and services. In this book, a distinguished group of academic experts considers the agenda and areas of interest for the next Round in light of Seattle, the functions of the WTO and competition policy issues arising from trade liberalization.
Cyclical aspiration using a novel mechanical thrombectomy device is associated with a high TICI 3 first pass effect in large‐vessel strokes

Complete reperfusion (TICI 3) after the first thrombectomy attempt or first pass effect (FPE) is associated with best clinical outcomes in large‐vessel occlusion (LVO) acute ischemic stroke. While endovascular therapy techniques have improved substantially, FPE remains low (24–30%), and new methods to improve reperfusion efficiency are needed.