Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiscale Modeling in Systems Biology Methods and Perspectives

Coulier, A. 2021. Multiscale Modeling in Systems Biology. Methods and Perspectives. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 2051. 60 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-513-1225-5. In the last decades, mathematical and computational models have become ubiquitous to the field of systems biology. Specifically, the multiscale nature of biological processes makes the design and simulation of such models challenging. In this thesis we offer a perspective on available methods to study and simulate such models and how they can be combined to handle biological processes evolving at different scales. The contribution of this thesis is threefold. First, we introduce Orchestral, a multiscale modular framework to simulate multicellular models. By decoupling intracellular chemical kinetics, cell-cell signaling, and cellular mechanics by means of operator-splitting, it is able to combine existing software into one massively parallel simulation. Its modular structure makes it easy to replace its components, e.g. to adjust the level of modeling details. We demonstrate the scalability of our framework on both high performance clusters and in a cloud environment. We then explore how center-based models can be used to study cellular mechanics in biological tissues. We show how modeling and numerical choices can affect the results of the simulation and mislead modelers into incorrect biological conclusions if these errors are not monitored properly. We then propose CBMOS, a Python framework specifically designed for the numerical study of such models. Finally, we study how spatial details in intracellular chemical kinetics can be efficiently approximated in a multiscale compartment-based model. We evaluate how this model compares to two other alternatives in terms of accuracy and computational cost. We then propose a computational pipeline to study and compare such models in the context of Bayesian parameter inference and illustrate its usage in three case studies.
Benchmarking Neural Topic Models: An Empirical Study

Neural topic modeling approach has been at-tracting much attention recently as it is able to leverage the advantages of both neural networks and probabilistic topic models. Previous works have proposed several models that are based on this framework and obtained impressive experimental results compared to traditional probabilistic models. However, the re-ported result is not consistent across the works, making them hard for gaining a rigorous assessment of these approaches. This work aims to address this issue by offering an extensive empirical evaluation of typical neural topic models in different aspects using large, diverse datasets as well as a thorough set of metrics. Precisely, we examine the performance of these models in three tasks, namely uncovering cohesive topics , modeling the input documents , and representing them for downstream classiﬁcation . Our results show that while the neural topic models are better in the ﬁrst and the third tasks, the traditional probabilistic models are still a strong baseline and are better in the second task in many cases. These ﬁndings give us more insights for choosing off-the-shelf topic modeling toolboxes in different contexts, as well as for designing more comprehensive evaluation for neural topic models.
CAD-based Grasp and Motion Planning for Process Automation in Fused Deposition Modelling

Planning the right grasp pose and motion into it has been a problem in the robotic community for more than 20 years. This paper presents a model-based approach for a Pick action of a robot that increases the automation of FDM based additive manufacturing by removing a produced object from the build plate. We treat grasp pose planning, motion planning and simulation-based verification as separate components to allow a high exchangeability. When testing a variety of different object geometries, feasible grasps and motions were obtained for all objects. We also found that the computation time is highly dependent on the random seed, leading us to employ a system of budgeted runs for which we report the estimated success probability and expected running time. Within the budget, some objects never found feasible picks. Thus, we rotated these objects by 90◦ which lead to a substantial improvement in success probabilities.
Contrastive Aligned Joint Learning for Multilingual Summarization

Multilingual text summarization requires the ability to understand documents in multiple languages and generate summaries in the corresponding language, which poses more challenges on current summarization systems. However, this problem has been rarely studied due to the lack of large-scale supervised summarization data in multiple languages. In this paper, we ﬁrst provide a large-scale multilingual summarization corpus MLGSum consisting of 1.1 million articles and summaries in 12 different languages. Based on it, we develop a uniﬁed summarization model to understand the document and generate summaries in different languages. We use the contrastive learning strategy to train our multilingual summarization system (CALMS), which consists of two training objectives, contrastive sentence ranking (CSR) and sentence aligned substitution (SAS). The two training objectives are designed to share salient information extractive ability and align sentence-level representation across different languages. Experimental results indicate that CALMS achieves signiﬁcant improvement over mono-lingual models in all languages. We further transfer CALMS to other languages and ﬁnd that it will also beneﬁt similar languages. Our code and dataset are available at https://github.com/brxx122/CALMS.
Joint Multi-Decoder Framework with Hierarchical Pointer Network for Frame Semantic Parsing

Current researches on frame semantic parsing include three subtasks, namely frame identiﬁ-cation, argument identiﬁcation and role clas-siﬁcation. Most of previous systems process these subtasks independently and ignore their interactions. We introduce a novel architecture based on multi-decoder strategy to handle these subtasks together. The multi-decoder strategy strengthens the interactions. More-over, we design a hierarchical pointer network for argument identiﬁcation which reduces the computational complexity. To our best knowledge, it’s the ﬁrst practice to introduce the pointer network into frame semantic parsing. The experiments show improvement over state of the art models on FrameNet dataset.
Software Task Importance Prediction based on Project Management Data

With the help of project management tools and code hosting facilities, software development has been transformed into an easy-to-decentralize business. However, determining the importance of tasks within a software engineering process in order to better prioritize and act on has always been an interesting challenge. Although several approaches on bug severity/priority prediction exist, the challenge of task importance prediction has not been sufficiently addressed in current research. Most approaches do not consider the meta-data and the temporal characteristics of the data, while they also do not take into account the ordinal characteristics of the importance/severity variable. In this work, we analyze the challenge of task importance prediction and propose a prototype methodology that extracts both textual (titles, descriptions) and meta-data (type, assignee) characteristics from tasks and employs a sliding window technique to model their time frame. After that, we evaluate three different prediction methods, a multi-class classifier, a regression algorithm, and an ordinal classification technique, in order to assess which model is the most effective for encompassing the relative ordering between different importance values. The results of our evaluation are promising, leaving room for future research.
Entheos: A Multimodal Dataset for Studying Enthusiasm

Enthusiasm plays an important role in engaging communication. It enables speakers to be distinguished and remembered, creating an emotional bond that inspires and motivates their addressees to act, listen, and co-ordinate (Bettencourt et al., 1983). Although people can easily identify enthusiasm, this is a rather difﬁcult task for machines due to the lack of resources and models that can help them understand or generate enthusiastic behavior. We introduce Entheos, the ﬁrst multimodal dataset for studying enthusiasm composed of video, audio, and text. We present several baseline models and an ablation study using different features, showing the importance of pitch, loudness, and discourse relation parsing in distinguishing enthusiastic communication.
Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling

Structural syntax knowledge has been proven effective for semantic role labeling (SRL), while existing works mostly use only one singleton syntax, such as either syntactic dependency or constituency tree. In this paper, we explore the integration of heterogeneous syntactic representations for SRL. We ﬁrst consider a TreeLSTM-based integration, collaboratively learning the phrasal boundaries from the constituency and the semantic relations from dependency. We further introduce a label-aware GCN solution for simultaneously modeling the syntactic edges and labels. Experimental results demonstrate that by effectively combining the heterogeneous syntactic representations, our methods yield task improvements on both span-based and dependency-based SRL. Also our system achieves new state-of-the-art SRL performances, meanwhile bringing explainable task improvements.
Are Rotten Apples Edible? Challenging Commonsense Inference Ability with Exceptions

Previous studies have argued that pre-trained language models encode commonsense relational knowledge (e.g. that apples are edible ). However, simultaneous work has revealed that such models are often insensitive to context, even ignoring overt contextual cues such as negations. In this paper, we investigate whether masked language models (the BERT family) can move beyond naive associative biases (e.g., apple → edible ) when the context warrants (e.g. ranking inedible higher when presented with the information that the apple is rotten ). We introduce the W INO V ENTI procedure, which adversarially exploits generic associations in masked language models to create model-speciﬁc Winograd-style entailment schemas. Using our constructed W INO V ENTI challenges set of over 2 , 000 schemas, we show that language models in the BERT family experience a steep drop in performance on prompts that require them to pick answers which require reasoning about context (e.g., from 89 . 8% to 18 . 4% for BERT LARGE ). We present evidence that language models exhibit different associative biases, suggesting a need for future work in developing and analyzing frameworks similar to W INO V ENTI that are tuned to model-speciﬁc weaknesses.
A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis

Multimodal fusion is a core problem for multi-modal sentiment analysis. Previous works usually treat all three modal features equally and implicitly explore the interactions between different modalities. In this paper, we break this kind of methods in two ways. Firstly, we observe that textual modality plays the most important role in multimodal sentiment analysis, and this can be seen from the previous works. Secondly, we observe that comparing to the textual modality, the other two kinds of non-textual modalities (visual and acoustic) can provide two kinds of semantics, shared and private semantics. The shared semantics from the other two modalities can obviously enhance the textual semantics and make the sentiment analysis model more robust, and the private semantics can be complementary to the textual semantics and meanwhile provide different views to improve the performance of sentiment analysis together with the shared semantics. Motivated by these two observations, we propose a text-centered shared-private framework (TCSP) for multimodal fusion, which consists of the cross-modal prediction and sentiment regression parts. Experiments on the MOSEI and MOSI datasets demonstrate the effectiveness of our shared-private framework, which outperforms all baselines. Furthermore, our approach provides a new way to utilize the unlabeled data for multimodal sentiment analysis.
An Investigation of Suitability of Pre-Trained Language Models for Dialogue Generation – Avoiding Discrepancies

Pre-trained language models have been widely used in response generation for open-domain dialogue. These approaches are built within 4 frameworks: Transformer-ED, Transformer-Dec, Transformer-MLM and Transformer-AR. In this study, we experimentally compare them using both large and small-scale data. This reveals that decoder-only architecture is better than stacked encoder-decoder, and both left-to-right and bi-directional attention have their own advantages. We further deﬁne two concepts of model discrepancy, which provides a new explanation to the model performance. As discrepancies may hinder performance, we propose two solutions to reduce them, which successfully improve the model performance.
Three-year Trends in YouTube Video Content and Encoding

Despite the dominance of YouTube streaming traffic, there have been few studies focusing on characterizing YouTube videos over time. Given the sheer volume of YouTube videos, we created a custom crawler which took snapshots of popular YouTube channels and ran the crawler daily for the past 3 years. This provides YouTube video trends from 2018–2020 for over 160k videos, considering media type, duration, bit rate, resolution, codec, encoding format, and popularity. Analysis of the data shows YouTube videos have increased frame rates, resolutions and durations over this time, with the biggest clips consuming over 200 Mb/s and being over 3 hours long, accompanied by corresponding changes in encoding rates and codecs. Our analysis and the resulting dataset we make public should be beneficial for traffic shaping or CDN deployment strategies.
Understanding the Interaction of Nucleobases with Chiral Semiconducting Single-Walled Carbon Nanotubes: An Alternative Theoretical Approach Based on Density Functional Reactivity Theory

The present study describes an alternative and computationally cost-effective theoretical approach to explore the interaction of nucleobases with different semiconducting chiral single-walled carbon nanotubes (SWCNTs). Implementing density functional reactivity theory (DFRT) based CDASE (comprehensive decomposition analysis of stabilization energy, Bagaria et al. Phys. Chem. Chem. Phys. 2009, 11, 8306), scheme kinetic and thermodynamic aspects of the interaction between different DNA bases as well as Watson–Crick base pairs (AT and GC) with SWCNTs are investigated and that is also without performing computationally intensive transition state optimization or thermochemistry calculation. The trend of interaction generated by reactivity parameters (based on the CDASE scheme) follows the experimentally as well as theoretically verified order, G⟩A⟩T⟩C⟩U, observed earlier. Conventional binding energy calculations on some of the chosen systems using the ONIOM QM:MM approach generate a reasonably satisfactory tre.
Electro-Fenton treatment of imidazolium-based ionic liquids: kinetics and degradation pathways

In this study, the removal of five imidazolium-based ionic liquids (ILs) from water was accomplished by a heterogeneous electro-Fenton treatment. Different ILs were selected in order to study the effect of the alkyl chain length and the nature of the anion. Initially, the effect of the catalyst (iron alginate beads) dosage and current were evaluated. The results showed that the optimum conditions were attained when operating with 4.27 g of catalyst and 0.3 A, achieving degradation yields and TOC reductions higher than 95% after 90 min and 80% after 480 min, respectively. Regarding the ILs with common cations, lower degradation rates were obtained for those with longer alkyl chains, thus 1-ethyl-3-methylimidazolium dicyanamide, exhibits faster degradation than 1-hexyl-3-methylimidazolium dicyanamide and 1-butyl-3-methylimidazolium dicyanamide. In addition, the influence of the counter anions on the oxidation rate of different ILs under an electro-Fenton treatment was demonstrated, following the order: methylsulfate > dicyanamide > acetate. The Microtox ecotoxicity tests of the initial and treated samples revealed that none of the three studied ILs with shorter alkyl chain lengths showed toxicity and only 1-hexyl-3-methylimidazolium dicyanamide and 1-butyl-3-methylimidazolium dicyanamide presented toxic levels, with EC50 values of 269.85 and 47.55 mg L−1, respectively. Nonetheless, after 480 min of heterogeneous electro-Fenton treatment at 0.3 A, the toxicity of both was completely reduced. Finally, to confirm the mineralization of these compounds, the identification of several reaction intermediates in the heterogeneous electro-Fenton treatment of three ILs was assayed and a plausible degradation pathway was proposed.
Multi-Task Sequence Tagging for Emotion-Cause Pair Extraction Via Tag Distribution Refinement

The task emotion-cause pair extraction deals with finding all emotions and the corresponding causes from emotion texts. Existing joint methods solve it as multi-task learning, which introduces two auxiliary tasks (i.e., emotion extraction and cause extraction) to make use of task correlations for their mutual benefits. However, these methods focus on capturing such correlations by sharing parameters in an implicit way, not only have a limitation of cannot explicitly model their information interaction, but also suffer from low interpretability. Towards these issues, we propose a multi-task sequence tagging framework, which can extract emotions with the associated causes simultaneously by encoding their distances into a novel tagging scheme. In addition, the output of both auxiliary tasks can be directly used as inductive bias, to refine the tag distribution for benefiting emotion-cause pair extraction, so that the information exchange between them can be more explicit and interpretable. Results show that our model achieves the best performance, outperforming a number of competitive baselines by at least 1.03% (<inline-formula><tex-math notation="LaTeX">$p< 0.01$</tex-math></inline-formula>) in <inline-formula><tex-math notation="LaTeX">$F_1$</tex-math></inline-formula> score. The comprehensive analysis further confirms the superiority and robustness of our model.
6D-Chaotic System and 2D Fractional Discrete Cosine Transform Based Encryption of Biometric Templates

A new algorithm for biometric templates using a 6D-chaotic system, and 2D fractional discrete cosine transform (FrDCT) is proposed in this paper. In this technique, the $k$ biometric templates are represented into three groups. After representation, these three groups are converted into row vectors and scrambled by using keys generated by the 6D-chaotic system and after that, these row vectors are combined into three matrices. The three matrices are then mixed horizontally and divided into two halves, with the left half serving as the real part and the right half serving as the imaginary part of a complex-valued matrix (CVM). This CVM is further subjected to 2D FrDCT. The output of 2D FrDCT is separated into three parts. The robustness of the technique is further enhanced by substitution operation using keys generated by the 6D-chaotic system. Thus, the final encrypted template is obtained. The analysis like security, statistical, and attacks are given to authenticate the reliability of the proposed technique. The experimental values also show that the proposed technique is resistant to brute force attacks.
Revisiting the Sample Adaptive Offset post-filter of VVC with Neural-Networks

The Sample Adaptive Offset (SAO) filter has been introduced in HEVC to reduce general coding and banding artefacts in the reconstructed pictures, in complement to the De-Blocking Filter (DBF) which reduces artifacts at block boundaries specifically. The new video compression standard Versatile Video Coding (VVC) reduces the BD-rate by about 36% at the same reconstruction quality compared to HEVC. It implements an additional new in-loop Adaptive Loop Filter (ALF) on top of the DBF and the SAO filter, the latter remaining unchanged compared to HEVC. However, the relative performance of SAO in VVC has been lowered significantly. In this paper, it is proposed to revisit the SAO filter using Neural Networks (NN). The general principles of the SAO are kept, but the a-priori classification of SAO is replaced with a set of neural networks that determine which reconstructed samples should be corrected and in which proportion. Similarly to the original SAO, some parameters are determined at the encoder side and encoded per CTU. The average BD-rate gain of the proposed SAO improves VVC by at least 2.3% in Random Access while the overall complexity is kept relatively small compared to other NN-based methods.
Regret-Optimal Controller for the Full-Information Problem

We consider the infinite-horizon, discrete-time full-information control problem. Motivated by learning theory, as a criterion for controller design we focus on regret, defined as the difference between the linear quadratic regulator (LQR) cost of a causal controller (that has only access to past and current disturbances) and the LQR cost of a clairvoyant one (that has also access to future disturbances). In the full-information setting, there is a unique optimal non-causal controller that in terms of LQR cost dominates all other controllers, and we focus on the regret compared to this particular controller. Since the regret itself is a function of the disturbances, we consider the worst-case regret over all possible bounded energy disturbances, and propose to find a causal controller that minimizes this worst-case regret. The resulting controller has the interpretation of guaranteeing the smallest possible regret compared to the best non-causal controller that has can see the future, no matter what the disturbances are. We show that the regret-optimal control problem can be reduced to a Nehari extension problem, i.e., to approximate an anticausal operator with a causal one in the operator norm. In the state-space setting we obtain explicit formulas for the optimal regret and for the regret-optimal controller. The regret-optimal controller is the sum of the classical H2 control law and an n-th order controller (where n is the state dimension of the plant) obtained from the Nehari problem. The controller construction simply requires the solution to the standard LQR Riccati equation, in addition to two Lyapunov equations. Simulations over a range of plants demonstrates that the regret-optimal controller interpolates nicely between the H2 and the H∞ optimal controllers, and generally has H2 and H∞ costs that are simultaneously close to their optimal values. The regret-optimal controller thus presents itself as a viable option for control system design.