pwc-archive/papers-with-abstracts huggingface dataset:
Modular Golden Gate Assembly of Linear DNA Templates for Cell-free Prototyping
Authors: François-Xavier Lehr, Aukse Gaizauskaite, Katarzyna Elżbieta Lipińska, Sara Gilles, Arpita Sahoo, René Inckemann, Henrike Niederholtmeyer
Date: 2023-10-20 00:00:00

Cell-free transcription and translation (TXTL) systems have emerged as a powerful tool for testing genetic regulatory elements and circuits. Cell-free prototyping can dramatically accelerate the design-build-test cycle of new functions in synthetic biology, in particular when linear DNA templates are used. Here we describe a Golden Gate assisted workflow to rapidly produce linear DNA templates for TXTL reactions by assembling transcriptional units from basic genetic parts of a modular cloning toolbox. Functional DNA templates composed of multiple parts such as promoter, ribosomal binding site (RBS), coding sequence, and terminator are produced in vitro in a one-pot Golden Gate assembly reaction followed by PCR amplification. By eliminating lengthy transformation and cloning steps in cells and by taking advantage of modular cloning toolboxes, our cell-free prototyping workflow can produce data for large numbers of new constructs within a single day.
Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening
Authors: Kun Li, Yong Luo, Xiantao Cai, Wenbin Hu, Bo Du
Date: 2023-10-05 00:00:00
Tasks: Domain Adaptation, Drug Discovery, Drug Response Prediction, Zero-Shot Learning

Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds, often with unknown drug responses. This presents a challenge, rendering supervised deep learning methods unsuitable for such scenarios. In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening. Specifically, we propose a Multi-branch Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can be seamlessly integrated with conventional DRP methods, learning invariant features from the prior response data of similar drugs to enhance real-time predictions of unlabeled compounds. We conducted experiments using the GDSCv2 and CellMiner datasets. The results demonstrate that MSDA efficiently predicts drug responses for novel compounds, leading to a general performance improvement of 5-10\% in the preclinical drug screening phase. The significance of this solution resides in its potential to accelerate the drug discovery process, improve drug candidate assessment, and facilitate the success of drug discovery.
The neural signature of inner peace: morphometric differences between high and low accepters
Authors: Alessandro Grecucci, Parisa Ahmadi Ghomroudi, Bianca Monachesi, Irene Messina
Date: 2023-10-20 00:00:00
Tasks: Emotional Intelligence

Acceptance is an adaptive emotion regulation strategy characterized by an open and non-judgmental attitude toward mental and sensory experiences. While a few studies have investigated the neural correlates of acceptance in task-based fMRI studies, a gap remains in the scientific literature in dispositional use of acceptance, and how this is sedimented at a structural level. Therefore, the aim of the present study is to investigate the neural and psychological differences between infrequent acceptance users (i.e., low accepters) and frequent users (i.e., high accepters). Another question is whether high and low accepters differ in personality traits and emotional intelligence. To this aim, we applied, for the first time, a data fusion unsupervised machine learning approach (mCCA-jICA) to the gray matter (GM) and white matter (WM) of high accepters (N = 50), and low accepters (N = 78) to possibly find joint GM-WM differences in both modalities. Our results show that two covarying GM-WM networks separate high from low accepters. The first network showed decreased GM-WM concentration in a fronto-temporal-parietal circuit largely overlapping with the Default Mode Network, while the second network showed increased GM-WM concentration in portions of the orbito-frontal, temporal, and parietal areas, related to a Central Executive Network. At the psychological level, the high accepters display higher openness to experience compared to low accepters. Overall, our findings suggest that high accepters compared to low accepters differ in neural and psychological mechanisms. These findings confirm and extend previous studies on the relevance of acceptance as a strategy associated with well-being.
Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence
Authors: Kerem Oktar, Ilia Sucholutsky, Tania Lombrozo, Thomas L. Griffiths
Date: 2023-10-03 00:00:00

The increasing prevalence of artificial agents creates a correspondingly increasing need to manage disagreements between humans and artificial agents, as well as between artificial agents themselves. Considering this larger space of possible agents exposes an opportunity for furthering our understanding of the nature of disagreement: past studies in psychology have often cast disagreement as two agents forming diverging evaluations of the same object, but disagreement can also arise from differences in how agents represent that object. AI research on human-machine alignment and recent work in computational cognitive science have focused on this latter kind of disagreement, and have developed tools that can be used to quantify the extent of representational overlap between agents. Understanding how divergence and misalignment interact to produce disagreement, and how resolution strategies depend on this interaction, is key to promoting effective collaboration between diverse types of agents.
Design and expression of TolC as a recombinant protein vaccine against Shigella flexneri and evaluation of immunogenic response in mice
Authors: Razieh Veisi, Nahal Hadi, Shahram Nazarian, Javad Fathi
Date: 2023-10-20 00:00:00

Background: Shigella is one of the major cause of dysenteric diarrhea, which is known shigelosis. Shigelosis causes 160,000 deaths annually of diarrheal disease in a global scale especially children less than 5 years old. No licensed vaccine is available against shigelosis, therefore, efforts for develop an effective and safe vaccine against shigella as before needed. The reverse vaccinology (RV) is a novel strategy that evaluate genome or proteome of an organism to find a new promising vaccine candidate. In this study, immunogenicity of a designed-recombinant antigen is evaluated through the insilico studies and animal experiments to predict a new immunogenic candidate against Shigella. Methods: In the first step, proteome of shigella flexneri was obtained from UniProtKB and then the outer membrane and extracellular were predicted. TolC as an outer membrane protein was selected and confirmed. In next steps, pre-selected protein was evaluated for transmembrane domains, homology, conservation, antigenicity, solubility, and B- and T-cell prediction by different online servers. Result: TolC as a conserved outer membrane protein, using different immunoinformatics tools had acceptable scores and was selected as an immunogenic antigen for animal experimenta studies. Recombinant TolC protein after expression and purification, was administered to BALB/c mice intraperitoneally over three routes. The sera of mice was used to evaluate the IgG1 production assay by indirect-ELISA. The immunized mice depicted effective protection against 2 LD50 of shigella flexneri ATCC12022 (challenge study). Conclusion: Therefore, the reverse vaccinology approach and experimental test results demonstrated that TolC as a novel effective and immunogenic antigen is capable for protection against shigellosis.
Standardised workflow for mass spectrometry-based single-cell proteomics data processing and analysis using the scp package
Authors: Samuel Grégoire, Christophe Vanderaa, Sébastien Pyr dit Ruys, Gabriel Mazzucchelli, Christopher Kune, Didier Vertommen, Laurent Gatto
Date: 2023-10-20 00:00:00
Tasks: Benchmarking

Mass spectrometry (MS) based single-cell proteomics (SCP) explores cellular heterogeneity by focusing on the functional effectors of the cells - proteins. However, extracting meaningful biological information from MS data is far from trivial, especially with single cells. Currently, data analysis workflows are substantially different from one research team to another. Moreover,it is difficult to evaluate pipelines as ground truths are missing. Our team has developed the R/Bioconductor package called scp to provide a standardised framework for SCP data analysis. It relies on the widely used QFeatures and SingleCellExperiment data structures. In addition, we used a design containing cell lines mixed in known proportions to generate controlled variability for data analysis benchmarking. In this work, we provide a flexible data analysis protocol for SCP data using the scp package together with comprehensive explanations at each step of the processing. Our main steps are quality control on the feature and cell level, aggregation of the raw data into peptides and proteins, normalisation and batch correction. We validate our workflow using our ground truth data set. We illustrate how to use this modular, standardised framework and highlight some crucial steps.
DavIR: Data Selection via Implicit Reward for Large Language Models
Authors: Haotian Zhou, Tingkai Liu, Qianli Ma, Yufeng Zhang, Jianbo Yuan, PengFei Liu, Yang You, Hongxia Yang
Date: 2023-10-16 00:00:00
Tasks: Causal Language Modeling, GSM8K, Language Modeling, Language Modelling, Mathematical Reasoning

We introduce DavIR, a model-based data selection method for post-training Large Language Models. DavIR generalizes Reducible Holdout Loss to core-set selection problem of causal language modeling, and quantifies the learnability of a given datum with respect to a pre-trained LLM based on relative reduction in loss during fine-tuning, a metric we show to be closely related to the implicit reward model described in Direct Preference Optimization (DPO). We show that 6% of Alpaca dataset selected with DavIR can steer both the LLaMA and Gemma model family to produce superior performance compared to the same models trained on the full 52K dataset. We also show that Alpaca dataset compressed with DavIR can be combined with GSM8K dataset to effectively balance open-domain freeform QA and mathematical reasoning capabilities. Finally, we apply the DavIR objective to DPO and develop a normalized DavIR-DPO objective which improves alignment performance of Zephyr-7B-SFT model by 8% (relative) on AlpacaEval, compared against training on vanilla DPO objective.
Wave-informed dictionary learning for high-resolution imaging in complex media
Authors: Miguel Moscoso, Alexei Novikov, George Papanicolaou, Chrysoula Tsogka
Date: 2023-09-22 00:00:00
Tasks: Dictionary Learning

We propose an approach for imaging in scattering media when large and diverse data sets are available. It has two steps. Using a dictionary learning algorithm the first step estimates the true Green's function vectors as columns in an unordered sensing matrix. The array data comes from many sparse sets of sources whose location and strength are not known to us. In the second step, the columns of the estimated sensing matrix are ordered for imaging using Multi-Dimensional Scaling with connectivity information derived from cross-correlations of its columns, as in time reversal. For these two steps to work together we need data from large arrays of receivers so the columns of the sensing matrix are incoherent for the first step, as well as from sub-arrays so that they are coherent enough to obtain the connectivity needed in the second step. Through simulation experiments, we show that the proposed approach is able to provide images in complex media whose resolution is that of a homogeneous medium.
Compositional preference models for aligning LMs
Authors: Dongyoung Go, Tomasz Korbak, Germán Kruszewski, Jos Rozen, Marc Dymetman
Date: 2023-10-17 00:00:00

As language models (LMs) become more capable, it is increasingly important to align them with human preferences. However, the dominant paradigm for training Preference Models (PMs) for that purpose suffers from fundamental limitations, such as lack of transparency and scalability, along with susceptibility to overfitting the preference dataset. We propose Compositional Preference Models (CPMs), a novel PM framework that decomposes one global preference assessment into several interpretable features, obtains scalar scores for these features from a prompted LM, and aggregates these scores using a logistic regression classifier. Through these simple steps, CPMs allow to control which properties of the preference data are used to train the preference model and to build it based on features that are believed to underlie the human preference judgment. Our experiments show that CPMs not only improve generalization and are more robust to overoptimization than standard PMs, but also that best-of-n samples obtained using CPMs tend to be preferred over samples obtained using conventional PMs. Overall, our approach demonstrates the benefits of endowing PMs with priors about which features determine human preferences while relying on LM capabilities to extract those features in a scalable and robust way.
Enhancing Health Data Interoperability with Large Language Models: A FHIR Study
Authors: Yikuan Li, Hanyin Wang, Halid Yerebakan, Yoshihisa Shinagawa, Yuan Luo
Date: 2023-09-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

In this study, we investigated the ability of the large language model (LLM) to enhance healthcare data interoperability. We leveraged the LLM to convert clinical texts into their corresponding FHIR resources. Our experiments, conducted on 3,671 snippets of clinical text, demonstrated that the LLM not only streamlines the multi-step natural language processing and human calibration processes but also achieves an exceptional accuracy rate of over 90% in exact matches when compared to human annotations.
Are Large Language Models Geospatially Knowledgeable?
Authors: Prabin Bhandari, Antonios Anastasopoulos, Dieter Pfoser
Date: 2023-10-09 00:00:00
Tasks: Decision Making

Despite the impressive performance of Large Language Models (LLM) for various natural language processing tasks, little is known about their comprehension of geographic data and related ability to facilitate informed geospatial decision-making. This paper investigates the extent of geospatial knowledge, awareness, and reasoning abilities encoded within such pretrained LLMs. With a focus on autoregressive language models, we devise experimental approaches related to (i) probing LLMs for geo-coordinates to assess geospatial knowledge, (ii) using geospatial and non-geospatial prepositions to gauge their geospatial awareness, and (iii) utilizing a multidimensional scaling (MDS) experiment to assess the models' geospatial reasoning capabilities and to determine locations of cities based on prompting. Our results confirm that it does not only take larger, but also more sophisticated LLMs to synthesize geospatial knowledge from textual information. As such, this research contributes to understanding the potential and limitations of LLMs in dealing with geospatial information.
A Critical Survey on Fairness Benefits of Explainable AI
Authors: Luca Deck, Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl
Date: 2023-10-15 00:00:00
Tasks: Articles, Fairness, Survey, Systematic Literature Review

In this critical survey, we analyze typical claims on the relationship between explainable AI (XAI) and fairness to disentangle the multidimensional relationship between these two concepts. Based on a systematic literature review and a subsequent qualitative content analysis, we identify seven archetypal claims from 175 scientific articles on the alleged fairness benefits of XAI. We present crucial caveats with respect to these claims and provide an entry point for future discussions around the potentials and limitations of XAI for specific fairness desiderata. Importantly, we notice that claims are often (i) vague and simplistic, (ii) lacking normative grounding, or (iii) poorly aligned with the actual capabilities of XAI. We suggest to conceive XAI not as an ethical panacea but as one of many tools to approach the multidimensional, sociotechnical challenge of algorithmic fairness. Moreover, when making a claim about XAI and fairness, we emphasize the need to be more specific about what kind of XAI method is used, which fairness desideratum it refers to, how exactly it enables fairness, and who is the stakeholder that benefits from XAI.
Enabling energy-Efficient object detection with surrogate gradient descent in spiking neural networks
Authors: Jilong Luo, Shanlin Xiao, Yinsheng Chen, Zhiyi Yu
Date: 2023-09-07 00:00:00
Tasks: Object, object-detection, Object Detection

Spiking Neural Networks (SNNs) are a biologically plausible neural network model with significant advantages in both event-driven processing and spatio-temporal information processing, rendering SNNs an appealing choice for energyefficient object detection. However, the non-differentiability of the biological neuronal dynamics model presents a challenge during the training of SNNs. Furthermore, a suitable decoding strategy for object detection in SNNs is currently lacking. In this study, we introduce the Current Mean Decoding (CMD) method, which solves the regression problem to facilitate the training of deep SNNs for object detection tasks. Based on the gradient surrogate and CMD, we propose the SNN-YOLOv3 model for object detection. Our experiments demonstrate that SNN-YOLOv3 achieves a remarkable performance with an mAP of 61.87% on the PASCAL VOC dataset, requiring only 6 time steps. Compared to SpikingYOLO, we have managed to increase mAP by nearly 10% while reducing energy consumption by two orders of magnitude.
A survey of manifold learning and its applications for multimedia
Authors: Hannes Fassold
Date: 2023-09-08 00:00:00
Tasks: Survey

Manifold learning is an emerging research domain of machine learning. In this work, we give an introduction into manifold learning and how it is employed for important application fields in multimedia.
Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models
Authors: Sumit Pandey, Kuan-Fu Chen, Erik B. Dam
Date: 2023-10-04 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

This paper introduces a comprehensive approach for segmenting regions of interest (ROI) in diverse medical imaging datasets, encompassing ultrasound, CT scans, and X-ray images. The proposed method harnesses the capabilities of the YOLOv8 model for approximate boundary box detection across modalities, alongside the Segment Anything Model (SAM) and High Quality (HQ) SAM for fully automatic and precise segmentation. To generate boundary boxes, the YOLOv8 model was trained using a limited set of 100 images and masks from each modality. The results obtained from our approach are extensively computed and analyzed, demonstrating its effectiveness and potential in medical image analysis. Various evaluation metrics, including precision, recall, F1 score, and Dice Score, were employed to quantify the accuracy of the segmentation results. A comparative analysis was conducted to assess the individual and combined performance of the YOLOv8, YOLOv8+SAM, and YOLOv8+HQ-SAM models. The results indicate that the SAM model performs better than the other two models, exhibiting higher segmentation accuracy and overall performance. While HQ-SAM offers potential advantages, its incremental gains over the standard SAM model may not justify the additional computational cost. The YOLOv8+SAM model shows promise for enhancing medical image segmentation and its clinical implications.
Parking Spot Classification based on surround view camera system
Authors: Andy Xiao, Deep Doshi, Lihao Wang, Harsha Gorantla, Thomas Heitzmann, Peter Groth
Date: 2023-10-05 00:00:00
Tasks: Classification, object-detection, Object Detection

Surround-view fisheye cameras are commonly used for near-field sensing in automated driving scenarios, including urban driving and auto valet parking. Four fisheye cameras, one on each side, are sufficient to cover 360{\deg} around the vehicle capturing the entire near-field region. Based on surround view cameras, there has been much research on parking slot detection with main focus on the occupancy status in recent years, but little work on whether the free slot is compatible with the mission of the ego vehicle or not. For instance, some spots are handicap or electric vehicles accessible only. In this paper, we tackle parking spot classification based on the surround view camera system. We adapt the object detection neural network YOLOv4 with a novel polygon bounding box model that is well-suited for various shaped parking spaces, such as slanted parking slots. To the best of our knowledge, we present the first detailed study on parking spot detection and classification on fisheye cameras for auto valet parking scenarios. The results prove that our proposed classification approach is effective to distinguish between regular, electric vehicle, and handicap parking spots.
Spec-NeRF: Multi-spectral Neural Radiance Fields
Authors: Jiabao Li, Yuqi Li, Ciliang Sun, Chong Wang, Jinhui Xiang
Date: 2023-09-14 00:00:00
Tasks: NeRF, Spectral Reconstruction

We propose Multi-spectral Neural Radiance Fields(Spec-NeRF) for jointly reconstructing a multispectral radiance field and spectral sensitivity functions(SSFs) of the camera from a set of color images filtered by different filters. The proposed method focuses on modeling the physical imaging process, and applies the estimated SSFs and radiance field to synthesize novel views of multispectral scenes. In this method, the data acquisition requires only a low-cost trichromatic camera and several off-the-shelf color filters, making it more practical than using specialized 3D scanning and spectral imaging equipment. Our experiments on both synthetic and real scenario datasets demonstrate that utilizing filtered RGB images with learnable NeRF and SSFs can achieve high fidelity and promising spectral reconstruction while retaining the inherent capability of NeRF to comprehend geometric structures. Code is available at https://github.com/CPREgroup/SpecNeRF-v2.
Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain
Authors: Marcus J. Min, Yangruibo Ding, Luca Buratti, Saurabh Pujar, Gail Kaiser, Suman Jana, Baishakhi Ray
Date: 2023-10-21 00:00:00
Tasks: Code Generation, Code Summarization

Code Large Language Models (Code LLMs) are being increasingly employed in real-life applications, so evaluating them is critical. While the conventional accuracy evaluates the performance of Code LLMs on a set of individual tasks, their self-consistency across different tasks is overlooked. Intuitively, a trustworthy model should be self-consistent when generating natural language specifications for its own code and generating code for its own specifications. Failure to preserve self-consistency reveals a lack of understanding of the shared semantics underlying natural language and programming language, and therefore undermines the trustworthiness of a model. In this paper, we first formally define the self-consistency of Code LLMs and then design a framework, IdentityChain, which effectively and efficiently evaluates the self-consistency and conventional accuracy of a model at the same time. We study eleven Code LLMs and show that they fail to preserve self-consistency, which is indeed a distinct aspect from conventional accuracy. Furthermore, we show that IdentityChain can be used as a model debugging tool to expose weaknesses of Code LLMs by demonstrating three major weaknesses that we identify in current models using IdentityChain. Our code is available at https://github.com/marcusm117/IdentityChain.