Miracle-dz/UniFAR-FTUs huggingface dataset:
Trace metal optimization in CHO cell culture through statistical design of experiments

A majority of the biotherapeutics industry today relies on the manufacturing of monoclonal antibodies from Chinese hamster ovary (CHO) cells, yet challenges remain with maintaining consistent product quality from high‐producing cell lines. Previous studies report the impact of individual trace metal supplemental on CHO cells, and thus, the combinatorial effects of these metals could be leveraged to improve bioprocesses further. A three‐level factorial experimental design was performed in fed‐batch shake flasks to evaluate the impact of time wise addition of individual or combined trace metals (zinc and copper) on CHO cell culture performance. Correlations among each factor (experimental parameters) and response variables (changes in cell culture performance) were examined based on their significance and goodness of fit to a partial least square's regression model. The model indicated that zinc concentration and time of addition counter‐influence peak viable cell density and antibody production. Meanwhile, early copper supplementation influenced late‐stage ROS activity in a dose‐dependent manner likely by alleviating cellular oxidative stress. Regression coefficients indicated that combined metal addition had less significant impact on titer and specific productivity compared to zinc addition alone, although titer increased the most under combined metal addition. Glycan analysis showed that combined metal addition reduced galactosylation to a greater extent than single metals when supplemented during the early growth phase. A validation experiment was performed to confirm the validity of the regression model by testing an optimized setpoint of metal supplement time and concentration to improve protein productivity.
Consistent SDNs through Network State Fuzzing

The conventional wisdom is that a software-defined network (SDN) operates under the premise that the logically centralized control plane has an accurate representation of the actual data plane state. Nevertheless, bugs, misconfigurations, faults or attacks can introduce inconsistencies that undermine correct operation. Previous work in this area, however, lacks a holistic methodology to tackle this problem and thus, addresses only certain parts of the problem. Yet, the consistency of the overall system is only as good as its least consistent part. Motivated by an analogy of network consistency checking with program testing, we propose to add active probe-based network state fuzzing to our consistency check repertoire. Hereby, our system, PAZZ, combines production traffic with active probes to continuously test if the actual forwarding path and decision elements (on the data plane) correspond to the expected ones (on the control plane). Our insight is that active traffic covers the inconsistency cases beyond the ones identified by passive traffic. PAZZ prototype was built and evaluated on topologies of varying scale and complexity. Our results show that PAZZ requires minimal network resources to detect persistent data plane faults through fuzzing and localize them quickly.
Complex-Valued Embedding Models for Knowledge Graphs

The explosion of widely available relational datain the form of knowledge graphsenabled many applications, including automated personalagents, recommender systems and enhanced web search results.The very large size and notorious incompleteness of these data basescalls for automatic knowledge graph completion methods to make these applicationsviable. Knowledge graph completion, also known as link-prediction,deals with automatically understandingthe structure of large knowledge graphs---labeled directed graphs---topredict missing entries---labeled edges. An increasinglypopular approach consists in representing knowledge graphs as third-order tensors,and using tensor factorization methods to predict their missing entries.State-of-the-art factorization models propose different trade-offs between modelingexpressiveness, and time and space complexity. We introduce a newmodel, ComplEx---for Complex Embeddings---to reconcile both expressivenessand complexity through the use of complex-valued factorization, and exploreits link with unitary diagonalization.We corroborate our approach theoretically and show that all possibleknowledge graphs can be exactly decomposed by the proposed model.Our approach based on complex embeddings is arguably simple,as it only involves a complex-valued trilinear product,whereas other methods resort to more and more complicated compositionfunctions to increase their expressiveness. The proposed ComplEx model isscalable to large data sets as it remains linear in both space and time, whileconsistently outperforming alternative approaches on standardlink-prediction benchmarks. We also demonstrateits ability to learn useful vectorial representations for other tasks,by enhancing word embeddings that improve performanceson the natural language problem of entailment recognitionbetween pair of sentences.In the last part of this thesis, we explore factorization models abilityto learn relational patterns from observed data.By their vectorial nature, it is not only hard to interpretwhy this class of models works so well,but also to understand where they fail andhow they might be improved. We conduct an experimentalsurvey of state-of-the-art models, not towardsa purely comparative end, but as a means to get insightabout their inductive abilities.To assess the strengths and weaknesses of each model, we create simple tasksthat exhibit first, atomic properties of knowledge graph relations,and then, common inter-relational inference through synthetic genealogies.Based on these experimental results, we propose new researchdirections to improve on existing models, including ComplEx.
Interactions between mitochondrial dysfunction and other hallmarks of aging: Paving a path toward interventions that promote healthy old age

Current research on human aging has largely been guided by the milestone paper “hallmarks of aging,” which were first proposed in the seminal 2013 paper by Lopez‐Otin et al. Most studies have focused on one aging hallmark at a time, asking whether the underlying molecular perturbations are sufficient to drive the aging process and its associated phenotypes. More recently, researchers have begun to investigate whether aging phenotypes are driven by concurrent perturbations in molecular pathways linked to not one but to multiple hallmarks of aging and whether they present different patterns in organs and systems over time. Indeed, preliminary results suggest that more complex interactions between aging hallmarks must be considered and addressed, if we are to develop interventions that successfully promote healthy aging and/or delay aging‐associated dysfunction and diseases. Here, we summarize some of the latest work and views on the interplay between hallmarks of aging, with a specific focus on mitochondrial dysfunction. Indeed, this represents a significant example of the complex crosstalk between hallmarks of aging and of the effects that an intervention targeted to a specific hallmark may have on the others. A better knowledge of these interconnections, of their cause‐effect relationships, of their spatial and temporal sequence, will be very beneficial for the whole aging research field and for the identification of effective interventions in promoting healthy old age.
Hydro mechanical coupling using a hybrid dimensional approach and interface element formulations

Characteristics of underground fluid storage and properties related to heat transport and underground matter are of high interest in the community of porous and fractures media. Especially once hydro‐mechanical phenomena in fluid filled fractures are of interest, such as inverse waterlevel fluctuations during well stimulations (Noordbergum effect), the numerical coupling of the fluid and the porous domain is challenging. Existing strategies such as direct numerical simulations or coarse‐grained approaches (Biot's theory) either fail due to technical issues (number of Degrees of Freedom (DoF)) or are not capable of intuitively taking permeability changes based on the deformation state into account. This work proposes a fully coupled scheme using hybrid‐dimensional element formulations and zero‐thickness interface elements that naturally describes flow in deformable fractures surrounded by porous media.
Estimation of the Burr type III distribution with application in unified hybrid censored sample of fracture toughness

In this paper, the statistical inference of the unknown parameters of a Burr Type III (BIII) distribution based on the unified hybrid censored sample is studied. The maximum likelihood estimators of the unknown parameters are obtained using the Expectation–Maximization algorithm. It is observed that the Bayes estimators cannot be obtained in explicit forms, hence Lindley's approximation and the Markov Chain Monte Carlo (MCMC) technique are used to compute the Bayes estimators. Further the highest posterior density credible intervals of the unknown parameters based on the MCMC samples are provided. The new model selection test is developed in discriminating between two competing models under unified hybrid censoring scheme. Finally, the potentiality of the BIII distribution to analyze the real data is illustrated by using the fracture toughness data of the three different materials namely silicon nitride (Si3N4), Zirconium dioxide (ZrO2) and sialon (Si6−xAlxOxN8−x). It is observed that for the present data sets, the BIII distribution has the better fit than the Weibull distribution which is frequently used in the fracture toughness data analysis.
High-resolution human brain functional χ mapping reveals focal and bidirectional BOLD responses

A blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) study produces a four-dimensional (4D) complex-valued data set. Conventional magnitude-based brain functional mapping is an indirect measure of brain activity due to a dataflow in a cascade of MRI transformations (e.g., a dipole convolution). The MRI transformations also impose a MRI parameter dependence to BOLD fMRI data (e.g. the dependence on B0 and TE). By solving an inverse MRI problem, we may reconstruct a brain magnetic susceptibility source distribution (denoted by χ) from a brain MR image, thereby achieving a more direct representation of the vascular origin than the MRI data representation. For a complex-valued BOLD fMRI dataset, we can extract net BOLD phase responses (δP) through a complex division, and then reconstruct BOLD susceptibility responses (δχ) therefrom. In this paper, we compare the functional maps depicted in different dataspaces (magnitude image, δP image, δχ source) based on numeric characterizations of activation blobs by pattern analysis. Through an experimental demonstration with high-field (7 T) and high-resolution (0.5 mm in-plane) finger-tapping experiment, we show that the δχ-depicted functional map reveals task-evoked bidirectional BOLD δχ responses at the motor cortex region. The positive and negative activations are spatially separated and balanced, indicating conservation of concurrent excitations and inhibitions. We also observe that the source-depicted activation spots are more compact than the image-depicted spots, which is attributed to the dipole effect removal by inverse MRI.
pth moment exponential input‐to‐state stability of nonlinear discrete‐time impulsive stochastic delay systems

This paper investigates the pth moment exponential input‐to‐state stability (ISS) of nonlinear discrete‐time impulsive stochastic delay systems. By employing Lyapunov functionals, some pth moment exponential ISS criteria are provided. The obtained results show that a discrete‐time stochastic delay system can become pth moment exponential input‐to‐state stable by impulsive controls even if it may be not input‐to‐state stable itself. On the other hand, the original system without impulses can retain its ISS property with appropriate destabilizing impulses. As an application, the theoretical results are used to test the ISS for a class of recurrent neural networks under stochastic perturbations. Finally, a numerical example is presented to illustrate the effectiveness of the results.
Information fraction estimation: Strategies for a phase 3 non‐inferiority maximum duration design with time to event outcome

There is considerable debate surrounding the choice of methods to estimate information fraction for futility monitoring in a randomized non‐inferiority maximum duration trial. This question was motivated by a pediatric oncology study that aimed to establish non‐inferiority for two primary outcomes. While non‐inferiority was determined for one outcome, the futility monitoring of the other outcome failed to stop the trial early, despite accumulating evidence of inferiority. For a one‐sided trial design for which the intervention is inferior to the standard therapy, futility monitoring should provide the opportunity to terminate the trial early. Our research focuses on the Total Control Only (TCO) method, which is defined as a ratio of observed events to total events exclusively within the standard treatment regimen. We investigate its properties in stopping a trial early in favor of inferiority. Simulation results comparing the TCO method with alternative methods, one based on the assumption of an inferior treatment effect (TH0), and the other based on a specified hypothesis of a non‐inferior treatment effect (THA), were provided under various pediatric oncology trial design settings. The TCO method is the only method that provides unbiased information fraction estimates regardless of the hypothesis assumptions and exhibits a good power and a comparable type I error rate at each interim analysis compared to other methods. Although none of the methods is uniformly superior on all criteria, the TCO method possesses favorable characteristics, making it a compelling choice for estimating the information fraction when the aim is to reduce cancer treatment‐related adverse outcomes.
Stellar Neutron Sources and s-Process in Massive Stars

by Rashi Talwar The s-process or the slow neutron capture process is a nucleosynthesis process that occurs at relatively low neutron densities and intermediate stellar temperatures. It runs along the valley of beta stability since the neutron capture rate is much slower compared to the beta decay rate. The s-process occurs mostly at the end of the helium and carbon burning phase in massive stars and during the core helium burning phase in low mass asymptotic giant-branch stars. In massive stars, the potential stellar neutron source for the s-process is associated with α-capture reactions on light nuclei. The capture-reaction rates provide the reaction flow for the build-up of Ne neutron source during the helium-burning phase in stars. The low energy Mg resonances at stellar energies below 800 keV are predicted to have a critical influence on the α-capture rates on Ne. Some of these resonances may also correspond to pronounced alpha cluster structure near the α-threshold. However, these resonances have remained elusive during direct alpha capture measurements owing to the high Coulomb barrier. Hence, in the present work, α-inelastic scattering and α-transfer measurements have been performed to probe the level structure of Mg nucleus in order to determine the α-capture rates.
Leverage, Endogenous Unbalanced Growth, and Asset Price Bubbles

We present a general equilibrium macro-finance model with a positive feedback loop between capital investment and land price. As leverage is relaxed beyond a critical value, through the financial accelerator, a phase transition occurs from balanced growth where land prices reflect fundamentals (present value of rents) to unbalanced growth where land prices grow faster than rents, generating land price bubbles. Unbalanced growth dynamics and bubbles are associated with financial loosening and technological progress. In an analytically tractable two-sector large open economy model with unique equilibria, financial loosening simultaneously leads to low interest rates, asset overvaluation, and top-end wealth concentration.
Central Extensions and Cohomology

Let G be a group which is topologically a CW-complex, BG a classifying space for G, and A a discrete abelian group. To a central extension of G by A, one can associate a cohomology class in $H^2(BG,A)$. We show this association is injective, and bijective in many cases. A homomorphism to G lifts to the extension iff the pullback of the associated cohomology class vanishes.
Identifiability and Falsifiability: Two Challenges for Bayesian Model Expansion

We study the identifiability of model parameters and falsifiability of model predictions under conditions of model expansion in a Bayesian setting. We present results and examples suggesting a tendency for identifiability and falsifiability to decrease in this context and for the severity of these problems to trade-off against one another. Additionally, we present two extended examples that demonstrate how these difficulties can be partially overcome by inferential methods that leverage the joint structure of the posterior distribution.
Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior

We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. Our framework invokes low-level controllers - either learned or implicit in position-command control - to stabilize imitation around expert demonstrations. We show that with (a) a suitable low-level stability guarantee and (b) a powerful enough generative model as our imitation learner, pure supervised behavior cloning can generate trajectories matching the per-time step distribution of essentially arbitrary expert trajectories in an optimal transport cost. Our analysis relies on a stochastic continuity property of the learned policy we call"total variation continuity"(TVC). We then show that TVC can be ensured with minimal degradation of accuracy by combining a popular data-augmentation regimen with a novel algorithmic trick: adding augmentation noise at execution time. We instantiate our guarantees for policies parameterized by diffusion models and prove that if the learner accurately estimates the score of the (noise-augmented) expert policy, then the distribution of imitator trajectories is close to the demonstrator distribution in a natural optimal transport distance. Our analysis constructs intricate couplings between noise-augmented trajectories, a technique that may be of independent interest. We conclude by empirically validating our algorithmic recommendations, and discussing implications for future research directions for better behavior cloning with generative modeling.
Attention for Robot Touch: Tactile Saliency Prediction for Robust Sim-to-Real Tactile Control

High-resolution tactile sensing can provide accurate information about local contact in contact-rich robotic tasks. However, the deployment of such tasks in unstructured environments remains under-investigated. To improve the robustness of tactile robot control in unstructured environments, we propose and study a new concept: tactile saliency for robot touch, inspired by the human touch attention mechanism from neuroscience and the visual saliency prediction problem from computer vision. In analogy to visual saliency, this concept involves identifying key information in tactile images captured by a tactile sensor. While visual saliency datasets are commonly annotated by humans, manually labelling tactile images is challenging due to their counterintuitive patterns. To address this challenge, we propose a novel approach comprised of three interrelated networks: 1) a Contact Depth Network (ConDepNet), which generates a contact depth map to localize deformation in a real tactile image that contains target and noise features; 2) a Tactile Saliency Network (TacSalNet), which predicts a tactile saliency map to describe the target areas for an input contact depth map; 3) and a Tactile Noise Generator (TacNGen), which generates noise features to train the TacSalNet. Experimental results in contact pose estimation and edge-following in the presence of distractors showcase the accurate prediction of target features from real tactile images. Overall, our tactile saliency prediction approach gives robust sim-to-real tactile control in environments with unknown distractors. Project page: https://sites.google. com/view/tactile-saliency/.
Exploring Annotation-free Image Captioning with Retrieval-augmented Pseudo Sentence Generation

Training an image captioner without annotated image-sentence pairs has gained traction in recent years. Previous approaches can be categorized into two strategies: crawling sentences from mismatching corpora and aligning them with the given images as pseudo annotations, or pre-training the captioner using external image-text pairs. However, the aligning setting seems to reach its performance limit due to the quality problem of pairs, and pre-training requires significant computational resources. To address these challenges, we propose a new strategy ``LPM + retrieval-augmented learning"where the prior knowledge from large pre-trained models (LPMs) is leveraged as supervision, and a retrieval process is integrated to further reinforce its effectiveness. Specifically, we introduce Retrieval-augmented Pseudo Sentence Generation (RaPSG), which adopts an efficient approach to retrieve highly relevant short region descriptions from the mismatching corpora and use them to generate a variety of pseudo sentences with distinct representations as well as high quality via LPMs. In addition, a fluency filter and a CLIP-guided training objective are further introduced to facilitate model optimization. Experimental results demonstrate that our method surpasses the SOTA pre-training model (Flamingo3B) by achieving a CIDEr score of 78.1 (+5.1) while utilizing only 0.3% of its trainable parameters (1.3B VS 33M). Importantly, our approach eliminates the need of computationally expensive pre-training processes on external datasets (e.g., the requirement of 312M image-text pairs for Flamingo3B). We further show that with a simple extension, the generated pseudo sentences can be deployed as weak supervision to boost the 1% semi-supervised image caption benchmark up to 93.4 CIDEr score (+8.9) which showcases the versatility and effectiveness of our approach.
Microbial Engineering to Mitigate Methane Emissions in Ruminant Livestock -- A Review

The most recent and promising strategies for mitigating methane emissions in ruminants are reviewed highlighting the potential of reductive acetogenesis as a viable alternative to methanogenesis. The emergence of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology, and its exceptional precision in genome editing, further enhances the prospects of exploring this avenue. Indeed, research in ruminant methane mitigation has been extensive, and over the years has resulted in the development of a wide variety of mitigation strategies. There is no doubt that the concepts of meat alternatives like lab-meat, microbial proteins and plant proteins may produce equivalent emissions. Reducing methane intensity through breeding and diet has been limited by our inability to phenotype ruminants in a high-throughput manner and the intensification of feed-food competition. Although chemical inhibitors have demonstrated effectiveness in manipulating the rumen microbiota to reduce net emissions, their success is constrained in terms of duration and feasibility in grazing system. Progress in making acetogenesis the dominant hydrogen sink in the rumen has been hampered by the thermodynamic cost of the reaction and the limited hydrogen levels in the rumen environment. We believe that CRISPR may allow the dominance of acetogenesis by converting methanogens into acetogens. We propose Methanobrevibacter ruminantium to be targeted because it is the dominant methane producer in the rumen.