pwc-archive/papers-with-abstracts huggingface dataset:
Parameter identifiability and model selection for partial differential equation models of cell invasion
Authors: Yue Liu, Kevin Suh, Philip K. Maini, Daniel J. Cohen, Ruth E. Baker
Date: 2023-09-04 00:00:00
Tasks: Experimental Design, Model Selection

When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide range of unseen scenarios, as well as for understanding the underlying mechanisms. In this work we use a profile likelihood approach to investigate parameter identifiability for four extensions of the Fisher--KPP model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and that they require more data to be practically identifiable. As a result, we suggest that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection.
RawHash2: Mapping Raw Nanopore Signals Using Hash-Based Seeding and Adaptive Quantization
Authors: Can Firtina, Melina Soysal, Joël Lindegger, Onur Mutlu
Date: 2023-09-11 00:00:00
Tasks: Quantization

Summary: Raw nanopore signals can be analyzed while they are being generated, a process known as real-time analysis. Real-time analysis of raw signals is essential to utilize the unique features that nanopore sequencing provides, enabling the early stopping of the sequencing of a read or the entire sequencing run based on the analysis. The state-of-the-art mechanism, RawHash, offers the first hash-based efficient and accurate similarity identification between raw signals and a reference genome by quickly matching their hash values. In this work, we introduce RawHash2, which provides major improvements over RawHash, including a more sensitive quantization and chaining implementation, weighted mapping decisions, frequency filters to reduce ambiguous seed hits, minimizers for hash-based sketching, and support for the R10.4 flow cell version and various data formats such as POD5 and SLOW5. Compared to RawHash, RawHash2 provides better F1 accuracy (on average by 10.57% and up to 20.25%) and better throughput (on average by 4.0x and up to 9.9x) than RawHash. Availability and Implementation: RawHash2 is available at https://github.com/CMU-SAFARI/RawHash. We also provide the scripts to fully reproduce our results on our GitHub page.
Blind Biological Sequence Denoising with Self-Supervised Set Learning
Authors: Nathan Ng, Ji Won Park, Jae Hyeon Lee, Ryan Lewis Kelly, Stephen Ra, Kyunghyun Cho
Date: 2023-09-04 00:00:00
Tasks: Denoising

Biological sequence analysis relies on the ability to denoise the imprecise output of sequencing platforms. We consider a common setting where a short sequence is read out repeatedly using a high-throughput long-read platform to generate multiple subreads, or noisy observations of the same sequence. Denoising these subreads with alignment-based approaches often fails when too few subreads are available or error rates are too high. In this paper, we propose a novel method for blindly denoising sets of sequences without directly observing clean source sequence labels. Our method, Self-Supervised Set Learning (SSSL), gathers subreads together in an embedding space and estimates a single set embedding as the midpoint of the subreads in both the latent and sequence spaces. This set embedding represents the "average" of the subreads and can be decoded into a prediction of the clean sequence. In experiments on simulated long-read DNA data, SSSL methods denoise small reads of $\leq 6$ subreads with 17% fewer errors and large reads of $>6$ subreads with 8% fewer errors compared to the best baseline. On a real dataset of antibody sequences, SSSL improves over baselines on two self-supervised metrics, with a significant improvement on difficult small reads that comprise over 60% of the test set. By accurately denoising these reads, SSSL promises to better realize the potential of high-throughput DNA sequencing data for downstream scientific applications.
Learning Disentangled Avatars with Hybrid 3D Representations
Authors: Yao Feng, Weiyang Liu, Timo Bolkart, Jinlong Yang, Marc Pollefeys, Michael J. Black
Date: 2023-09-12 00:00:00
Tasks: Disentanglement

Tremendous efforts have been made to learn animatable and photorealistic human avatars. Towards this end, both explicit and implicit 3D representations are heavily studied for a holistic modeling and capture of the whole human (e.g., body, clothing, face and hair), but neither representation is an optimal choice in terms of representation efficacy since different parts of the human avatar have different modeling desiderata. For example, meshes are generally not suitable for modeling clothing and hair. Motivated by this, we present Disentangled Avatars~(DELTA), which models humans with hybrid explicit-implicit 3D representations. DELTA takes a monocular RGB video as input, and produces a human avatar with separate body and clothing/hair layers. Specifically, we demonstrate two important applications for DELTA. For the first one, we consider the disentanglement of the human body and clothing and in the second, we disentangle the face and hair. To do so, DELTA represents the body or face with an explicit mesh-based parametric 3D model and the clothing or hair with an implicit neural radiance field. To make this possible, we design an end-to-end differentiable renderer that integrates meshes into volumetric rendering, enabling DELTA to learn directly from monocular videos without any 3D supervision. Finally, we show that how these two applications can be easily combined to model full-body avatars, such that the hair, face, body and clothing can be fully disentangled yet jointly rendered. Such a disentanglement enables hair and clothing transfer to arbitrary body shapes. We empirically validate the effectiveness of DELTA's disentanglement by demonstrating its promising performance on disentangled reconstruction, virtual clothing try-on and hairstyle transfer. To facilitate future research, we also release an open-sourced pipeline for the study of hybrid human avatar modeling.
Unveiling the potential of large language models in generating semantic and cross-language clones
Authors: Palash R. Roy, Ajmain I. Alam, Farouq Al-omari, Banani Roy, Chanchal K. Roy, Kevin A. Schneider
Date: 2023-09-12 00:00:00
Tasks: Benchmarking, Code Generation, Prompt Engineering, Text Generation

Semantic and Cross-language code clone generation may be useful for code reuse, code comprehension, refactoring and benchmarking. OpenAI's GPT model has potential in such clone generation as GPT is used for text generation. When developers copy/paste codes from Stack Overflow (SO) or within a system, there might be inconsistent changes leading to unexpected behaviours. Similarly, if someone possesses a code snippet in a particular programming language but seeks equivalent functionality in a different language, a semantic cross-language code clone generation approach could provide valuable assistance.In this study, using SemanticCloneBench as a vehicle, we evaluated how well the GPT-3 model could help generate semantic and cross-language clone variants for a given fragment.We have comprised a diverse set of code fragments and assessed GPT-3s performance in generating code variants.Through extensive experimentation and analysis, where 9 judges spent 158 hours to validate, we investigate the model's ability to produce accurate and semantically correct variants. Our findings shed light on GPT-3's strengths in code generation, offering insights into the potential applications and challenges of using advanced language models in software development. Our quantitative analysis yields compelling results. In the realm of semantic clones, GPT-3 attains an impressive accuracy of 62.14% and 0.55 BLEU score, achieved through few-shot prompt engineering. Furthermore, the model shines in transcending linguistic confines, boasting an exceptional 91.25% accuracy in generating cross-language clones
Ensemble Mask Networks
Authors: Jonny Luntzel
Date: 2023-09-12 00:00:00
Tasks: Network Pruning

Can an $\mathbb{R}^n\rightarrow \mathbb{R}^n$ feedforward network learn matrix-vector multiplication? This study introduces two mechanisms - flexible masking to take matrix inputs, and a unique network pruning to respect the mask's dependency structure. Networks can approximate fixed operations such as matrix-vector multiplication $\phi(A,x) \rightarrow Ax$, motivating the mechanisms introduced with applications towards litmus-testing dependencies or interaction order in graph-based models.
Style2Fab: Functionality-Aware Segmentation for Fabricating Personalized 3D Models with Generative AI
Authors: Faraz Faruqi, Ahmed Katary, Tarik Hasic, Amira Abdel-Rahman, Nayeemur Rahman, Leandra Tejedor, Mackenzie Leake, Megan Hofmann, Stefanie Mueller
Date: 2023-09-12 00:00:00

With recent advances in Generative AI, it is becoming easier to automatically manipulate 3D models. However, current methods tend to apply edits to models globally, which risks compromising the intended functionality of the 3D model when fabricated in the physical world. For example, modifying functional segments in 3D models, such as the base of a vase, could break the original functionality of the model, thus causing the vase to fall over. We introduce a method for automatically segmenting 3D models into functional and aesthetic elements. This method allows users to selectively modify aesthetic segments of 3D models, without affecting the functional segments. To develop this method we first create a taxonomy of functionality in 3D models by qualitatively analyzing 1000 models sourced from a popular 3D printing repository, Thingiverse. With this taxonomy, we develop a semi-automatic classification method to decompose 3D models into functional and aesthetic elements. We propose a system called Style2Fab that allows users to selectively stylize 3D models without compromising their functionality. We evaluate the effectiveness of our classification method compared to human-annotated data, and demonstrate the utility of Style2Fab with a user study to show that functionality-aware segmentation helps preserve model functionality.
Harissa: stochastic simulation and inference of gene regulatory networks based on transcriptional bursting
Authors: Ulysse Herbach
Date: 2023-09-10 00:00:00

Gene regulatory networks, as a powerful abstraction for describing complex biological interactions between genes through their expression products within a cell, are often regarded as virtually deterministic dynamical systems. However, this view is now being challenged by the fundamentally stochastic, 'bursty' nature of gene expression revealed at the single cell level. We present a Python package called Harissa which is dedicated to simulation and inference of such networks, based upon an underlying stochastic dynamical model driven by the transcriptional bursting phenomenon. As part of this tool, network inference can be interpreted as a calibration procedure for a mechanistic model: once calibrated, the model is able to capture the typical variability of single-cell data without requiring ad hoc external noise, unlike ordinary or even stochastic differential equations frequently used in this context. Therefore, Harissa can be used both as an inference tool, to reconstruct biologically relevant networks from time-course scRNA-seq data, and as a simulation tool, to generate quantitative gene expression profiles in a non-trivial way through gene interactions.
Investigation of temperature stress tolerance in Arabidopsis STTM165/166 using electrophysiology and RNA-Seq
Authors: Dongjie Zhao, Qinghui Chen, Ziyang Wang, Lucy Arbanas, Guiliang Tang
Date: 2023-09-08 00:00:00

Plant electrical signals have been shown to be generated in response to various environmental stresses, but the relationship between these signals and stress tolerance is not well understood. In this study, we used the Arabidopsis STTM165/166 mutant, which exhibits enhanced temperature tolerance, to examine this relationship. Surface recording techniques were utilized to compare the generation ratio and duration characteristics of electrical signals in the STTM165/166 mutant and wild type (WT). Patch-clamp recording was employed to assess ion channel currents, specifically those of calcium ions. The current intensity of the mutant was found to be lower than that of the WT. As calcium ions are involved in the generation of plant electrical signals, we hypothesized that the reduced calcium channel activity in the mutant increased its electrical signal threshold. RNA-Seq analysis revealed differential expression of AHA genes in the STTM165/166 mutant, which may contribute to the prolonged depolarization phenotype. Gene Ontology enrichment of differentially expressed genes (DEGs) identified associations between these DEGs and various stresses, including temperature, salt, and those related to the jasmonic acid and abscisic acid pathways. These findings provide experimental evidence for the use of plant electrical signals in characterizing stress tolerance and explore potential ion mechanisms through patch-clamp recording and DEG Gene Ontology analysis. They also emphasize the need for further research on the relationship between plant electrical signals and stress tolerance.
Grounded Language Acquisition From Object and Action Imagery
Authors: James Robert Kubricht, Zhaoyuan Yang, Jianwei Qiu, Peter Henry Tu
Date: 2023-09-12 00:00:00
Tasks: Action Recognition, Contrastive Learning, Language Acquisition, Machine Translation, Object, Object Recognition

Deep learning approaches to natural language processing have made great strides in recent years. While these models produce symbols that convey vast amounts of diverse knowledge, it is unclear how such symbols are grounded in data from the world. In this paper, we explore the development of a private language for visual data representation by training emergent language (EL) encoders/decoders in both i) a traditional referential game environment and ii) a contrastive learning environment utilizing a within-class matching training paradigm. An additional classification layer utilizing neural machine translation and random forest classification was used to transform symbolic representations (sequences of integer symbols) to class labels. These methods were applied in two experiments focusing on object recognition and action recognition. For object recognition, a set of sketches produced by human participants from real imagery was used (Sketchy dataset) and for action recognition, 2D trajectories were generated from 3D motion capture systems (MOVI dataset). In order to interpret the symbols produced for data in each experiment, gradient-weighted class activation mapping (Grad-CAM) methods were used to identify pixel regions indicating semantic features which contribute evidence towards symbols in learned languages. Additionally, a t-distributed stochastic neighbor embedding (t-SNE) method was used to investigate embeddings learned by CNN feature extractors.
MakeSBML: A tool for converting between Antimony and SBML
Authors: Bartholomew E. Jardine, Lucian P. Smith, Herbert M. Sauro
Date: 2023-09-06 00:00:00

We describe a web-based tool, MakeSBML (https://sys-bio.github.io/makesbml/), that provides an installation-free application for creating, editing, and searching the Biomodels repository for SBML-based models. MakeSBML is a client-based web application that translates models expressed in human-readable Antimony to the System Biology Markup Language (SBML) and vice-versa. Since MakeSBML is a web-based application it requires no installation on the user's part. Currently, MakeSBML is hosted on a GitHub page where the client-based design makes it trivial to move to other hosts. This model for software deployment also reduces maintenance costs since an active server is not required. The SBML modeling language is often used in systems biology research to describe complex biochemical networks and makes reproducing models much easier. However, SBML is designed to be computer-readable, not human-readable. We therefore employ the human-readable Antimony language to make it easy to create and edit SBML models.
Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning
Authors: Ole-Christoffer Granmo, Per-Arne Andersen, Lei Jiao, Xuan Zhang, Christian Blakely, Tor Tveit
Date: 2023-09-12 00:00:00
Tasks: Bayesian Inference

A set of variables is the Markov blanket of a random variable if it contains all the information needed for predicting the variable. If the blanket cannot be reduced without losing useful information, it is called a Markov boundary. Identifying the Markov boundary of a random variable is advantageous because all variables outside the boundary are superfluous. Hence, the Markov boundary provides an optimal feature set. However, learning the Markov boundary from data is challenging for two reasons. If one or more variables are removed from the Markov boundary, variables outside the boundary may start providing information. Conversely, variables within the boundary may stop providing information. The true role of each candidate variable is only manifesting when the Markov boundary has been identified. In this paper, we propose a new Tsetlin Machine (TM) feedback scheme that supplements Type I and Type II feedback. The scheme introduces a novel Finite State Automaton - a Context-Specific Independence Automaton. The automaton learns which features are outside the Markov boundary of the target, allowing them to be pruned from the TM during learning. We investigate the new scheme empirically, showing how it is capable of exploiting context-specific independence to find Markov boundaries. Further, we provide a theoretical analysis of convergence. Our approach thus connects the field of Bayesian networks (BN) with TMs, potentially opening up for synergies when it comes to inference and learning, including TM-produced Bayesian knowledge bases and TM-based Bayesian inference.
Identifying Essential Hub Genes and Protein Complexes in Malaria GO Data using Semantic Similarity Measures
Authors: Mamata Das, Selvakumar K., P. J. A. Alphonse
Date: 2023-08-09 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Hub genes play an essential role in biological systems because of their interaction with other genes. A vocabulary used in bioinformatics called Gene Ontology (GO) describes how genes and proteins operate. This flexible ontology illustrates the operation of molecular, biological, and cellular processes (Pmol, Pbio, Pcel). There are various methodologies that can be analyzed to determine semantic similarity. Research in this study, we employ the jack-knife method by taking into account 4 well-liked Semantic similarity measures namely Jaccard similarity, Cosine similarity, Pairsewise document similarity, and Levenshtein distance. Based on these similarity values, the protein-protein interaction network (PPI) of Malaria GO (Gene Ontology) data is built, which causes clusters of identical or related protein complexes (Px) to form. The hub nodes of the network are these necessary proteins. We use a variety of centrality measures to establish clusters of these networks in order to determine which node is the most important. The clusters' unique formation makes it simple to determine which class of Px they are allied to.
Analyzing and Comparing Omicron Lineage Variants Protein-Protein Interaction Network using Centrality Measure
Authors: Mamata Das, Selvakumar K., P. J. A. Alphonse
Date: 2023-08-09 00:00:00
Tasks: Clustering

The Worldwide spread of the Omicron lineage variants has now been confirmed. It is crucial to understand the process of cellular life and to discover new drugs need to identify the important proteins in a protein interaction network (PPIN). PPINs are often represented by graphs in bioinformatics, which describe cell processes. There are some proteins that have significant influences on these tissues, and which play a crucial role in regulating them. The discovery of new drugs is aided by the study of significant proteins. These significant proteins can be found by reducing the graph and using graph analysis. Studies examining protein interactions in the Omicron lineage (B.1.1.529) and its variants (BA.5, BA.4, BA.3, BA.2, BA.1.1, BA.1) are not yet available. Studying Omicron has been intended to find a significant protein. 68 nodes represent 68 proteins and 52 edges represent the relationship among the protein in the network. A few entrality measures are computed namely page rank centrality (PRC), degree centrality (DC), closeness centrality (CC), and betweenness centrality (BC) together with node degree and Local Clustering Co-efficient (LCC). We also discover 18 network clusters using Markov clustering. 8 significant proteins (candidate gene of Omicron lineage variants) were detected among the 68 proteins, including AHSG, KCNK1, KCNQ1, MAPT, NR1H4, PSMC2, PTPN11 and, UBE21 which scored the highest among the Omicron proteins. It is found that in the variant of Omicron protein-protein interaction networks, the MAPT protein's impact is the most significant.
AI4Food-NutritionFW: A Novel Framework for the Automatic Synthesis and Analysis of Eating Behaviours
Authors: Sergio Romero-Tapiador, Ruben Tolosana, Aythami Morales, Isabel Espinosa-Salinas, Gala Freixer, Julian Fierrez, Ruben Vera-Rodriguez, Enrique Carrillo de Santa Pau, Ana Ramírez de Molina, Javier Ortega-Garcia
Date: 2023-09-12 00:00:00
Tasks: Nutrition

Nowadays millions of images are shared on social media and web platforms. In particular, many of them are food images taken from a smartphone over time, providing information related to the individual's diet. On the other hand, eating behaviours are directly related to some of the most prevalent diseases in the world. Exploiting recent advances in image processing and Artificial Intelligence (AI), this scenario represents an excellent opportunity to: i) create new methods that analyse the individuals' health from what they eat, and ii) develop personalised recommendations to improve nutrition and diet under specific circumstances (e.g., obesity or COVID). Having tunable tools for creating food image datasets that facilitate research in both lines is very much needed. This paper proposes AI4Food-NutritionFW, a framework for the creation of food image datasets according to configurable eating behaviours. AI4Food-NutritionFW simulates a user-friendly and widespread scenario where images are taken using a smartphone. In addition to the framework, we also provide and describe a unique food image dataset that includes 4,800 different weekly eating behaviours from 15 different profiles and 1,200 subjects. Specifically, we consider profiles that comply with actual lifestyles from healthy eating behaviours (according to established knowledge), variable profiles (e.g., eating out, holidays), to unhealthy ones (e.g., excess of fast food or sweets). Finally, we automatically evaluate a healthy index of the subject's eating behaviours using multidimensional metrics based on guidelines for healthy diets proposed by international organisations, achieving promising results (99.53% and 99.60% accuracy and sensitivity, respectively). We also release to the research community a software implementation of our proposed AI4Food-NutritionFW and the mentioned food image dataset created with it.