pwc-archive/papers-with-abstracts huggingface dataset:
Functional approach to the catalytic site of the sarcoplasmic reticulum Ca(2+)-ATPase: binding and hydrolysis of ATP in the absence of Ca(2+)
Authors: A Lax, F Soler, F Fernandez Belda
Date: 2024-01-30 00:00:00

Isolated sarcoplasmic reticulum vesicles in the presence of Mg(2+) and absence of Ca(2+) retain significant ATP hydrolytic activity that can be attributed to the Ca(2+)-ATPase protein. At neutral pH and the presence of 5 mM Mg(2+), the dependence of the hydrolysis rate on a linear ATP concentration scale can be fitted by a single hyperbolic function. MgATP hydrolysis is inhibited by either free Mg(2+) or free ATP. The rate of ATP hydrolysis is not perturbed by vanadate, whereas the rate of p-nitrophenyl phosphate hydrolysis is not altered by a nonhydrolyzable ATP analog. ATP binding affinity at neutral pH and in a Ca(2+)-free medium is increased by Mg(2+) but decreased by vanadate when Mg(2+) is present. It is suggested that MgATP hydrolysis in the absence of Ca(2+) requires some optimal adjustment of the enzyme cytoplasmic domains. The Ca(2+)-independent activity is operative at basal levels of cytoplasmic Ca(2+) or when the Ca(2+) binding transition is impeded.
Dissecting the Hydrolytic Activities of Sarcoplasmic Reticulum ATPase in the Presence of Acetyl Phosphate
Authors: F Soler, MI Fortea, A Lax, F Fernandez Belda
Date: 2024-01-30 00:00:00

Sarcoplasmic reticulum vesicles and purified Ca$^{2+}$-ATPase hydrolyze acetyl phosphate both in the presence and absence of Ca$^{2+}$. The Ca$^{2+}$-independent activity was fully sensitive to vanadate, insensitive to thapsigargin, and proceeded without accumulation of phosphorylated enzyme. Acetyl phosphate hydrolysis in the absence of Ca$^{2+}$ was activated by dimethyl sulfoxide. The Ca$^{2+}$-dependent activity was partially sensitive to vanadate, fully sensitive to thapsigargin, and associated with steady phosphoenzyme accumulation. The Ca$^{2+}$/P(i) coupling ratio at neutral pH sustained by 10 mm acetyl phosphate was 0.57. Addition of 30% dimethyl sulfoxide completely blocked Ca$^{2+}$ transport and partially inhibited the hydrolysis rate. Uncoupling induced by dimethyl sulfoxide included the accumulation of vanadate-insensitive phosphorylated enzyme. When acetyl phosphate was the substrate, the hydrolytic pathway was dependent on experimental conditions that might or might not allow net Ca$^{2+}$ transport. The interdependence of both Ca$^{2+}$-dependent and Ca$^{2+}$-independent hydrolytic activities was demonstrated.
How Being Inside or Outside of Buildings Affects the Causal Relationship Between Weather and Pain Among People Living with Chronic Pain
Authors: Claire L. Little, David M. Schultz, Belay B. Yimer, Anna L. Beukenhorst
Date: 2024-01-31 00:00:00
Tasks: Causal Inference

Although many people believe their pain fluctuates with weather conditions, both weather and pain may be associated with time spent outside. For example, pleasant weather may mean that people spend more time outside doing physical activity and exposed to the weather, leading to more (or less) pain, and poor weather or severe pain may keep people inside, sedentary, and not exposed to the weather. We conducted a smartphone study where participants with chronic pain reported daily pain severity, as well as time spent outside. We address the relationship between four weather variables (temperature, dewpoint temperature, pressure, and wind speed) and pain by proposing a three-step approach to untangle their effects: (i) propose a set of plausible directed acyclic graphs (also known as DAGs) that account for potential roles of time spent outside (e.g., collider, effect modifier, mediator), (ii) analyze the compatibility of the observed data with the assumed model, and (iii) identify the most plausible model by combining evidence from the observed data and domain-specific knowledge. We found that the data do not support time spent outside as a collider or mediator of the relationship between weather variables and pain. On the other hand, time spent outside modifies the effect between temperature and pain, as well as wind speed and pain, with the effect being absent on days that participants spent inside and present if they spent some or all of the day outside. Our results show the utility of using directed acyclic graphs for studying causal inference.
Variation in Patterns of Metal Accumulation in Thallus Parts of Lessonia trabeculata (Laminariales; Phaeophyceae): Implications for Biomonitoring
Authors: C. A. Saez, M. G. Lobos, E. C. Macaya, D. Oliva, W. Quiroz, M. T. Brown
Date: 2024-01-24 00:00:00

Seaweeds are well known to concentrate metals from seawater and have been employed as monitors of metal pollution in coastal waters and estuaries. However, research showing that various intrinsic and extrinsic factors can influence metal accumulation, raises doubts about the basis for using seaweeds in biomonitoring programmes. The thallus of brown seaweeds of the order Laminariales (kelps) is morphologically complex but there is limited information about the variation in metal accumulation between the different parts, which might result in erroneous conclusions being drawn if not accounted for in the biomonitoring protocol. To assess patterns of individual metals in the differentiated parts of the thallus (blade, stipe, holdfast), concentrations of a wide range of essential and non-essential metals (Fe, Cr, Cu, Zn, Mn, Pb, Cd, Ni and Al) were measured in the kelp Lessonia trabeculata. Seaweeds were collected from three sampling stations located at 5, 30 and 60 m from an illegal sewage outfall close to Ventanas, Chile and from a pristine location at Faro Curaumilla. For the majority of metals the highest concentrations in bottom sediment and seaweed samples were found at the site closest to the outfall, with concentrations decreasing with distance from the outfall and at control stations; the exception was Cd, concentrations of which were higher at control stations. The patterns of metal concentrations in different thallus parts were metal specific and independent of sampling station. These results and the available literature suggest that biomonitoring of metals using seaweeds must take account of differences in the accumulation of metals in thallus parts of complex seaweeds
A Ca$^{2+}$ puff model based on integrodifferential equations
Authors: Molly Hawker, Pengxing Cao, James Sneyd, Ivo Siekmann
Date: 2024-01-29 00:00:00

The calcium (Ca$^{2+}$) signalling system is important for many cellular processes within the human body. Signals are transmitted within the cell by releasing Ca$^{2+}$ from the endoplasmic reticulum (ER) into the cytosol via clusters of Ca$^{2+}$ channels. Mathematical models of Ca$^{2+}$ release via inositol 1,4,5-trisphosphate receptors (IP$_{3}$R) help with understanding underlying Ca$^{2+}$ dynamics but data-driven modelling of stochastic Ca$^{2+}$ release events, known as Ca$^{2+}$ puffs, is a difficult challenge. Parameterising Markov models for representing the IP$_{3}$R with steady-state single channel data obtained at fixed combinations of the ligands Ca$^{2+}$ and inositol-trisphosphate (IP$_{3}$) has previously been demonstrated to be insufficient. However, by extending an IP$_{3}$R model based on steady-state data with an integral term that incorporates the delayed response of the channel to varying Ca$^{2+}$ concentrations we succeed in generating realistic Ca$^{2+}$ puffs. By interpreting the integral term as a weighted average of Ca$^{2+}$ concentrations that extend over a time interval of length $\tau$ into the past we conclude that the IP$_{3}$R requires a certain amount of memory of past ligand concentrations.
Coronary CTA and Quantitative Cardiac CT Perfusion (CCTP) in Coronary Artery Disease
Authors: Hao Wu, Yingnan Song, Ammar Hoori, Ananya Subramaniam, Juhwan Lee, Justin Kim, Tao Hu, Sadeer Al-Kindi, Wei-Ming Huang, Chun-Ho Yun, Chung-Lieh Hung, Sanjay Rajagopalan, David L. Wilson
Date: 2024-01-30 00:00:00

We assessed the benefit of combining stress cardiac CT perfusion (CCTP) myocardial blood flow (MBF) with coronary CT angiography (CCTA) using our innovative CCTP software. By combining CCTA and CCTP, one can uniquely identify a flow limiting stenosis (obstructive-lesion + low-MBF) versus MVD (no-obstructive-lesion + low-MBF. We retrospectively evaluated 104 patients with suspected CAD, including 18 with diabetes, who underwent CCTA+CCTP. Whole heart and territorial MBF was assessed using our automated pipeline for CCTP analysis that included beam hardening correction; temporal scan registration; automated segmentation; fast, accurate, robust MBF estimation; and visualization. Stenosis severity was scored using the CCTA coronary-artery-disease-reporting-and-data-system (CAD-RADS), with obstructive stenosis deemed as CAD-RADS>=3. We established a threshold MBF (MBF=199-mL/min-100g) for normal perfusion. In patients with CAD-RADS>=3, 28/37(76%) patients showed ischemia in the corresponding territory. Two patients with obstructive disease had normal perfusion, suggesting collaterals and/or a hemodynamically insignificant stenosis. Among diabetics, 10 of 18 (56%) demonstrated diffuse ischemia consistent with MVD. Among non-diabetics, only 6% had MVD. Sex-specific prevalence of MVD was 21%/24% (M/F). On a per-vessel basis (n=256), MBF showed a significant difference between territories with and without obstructive stenosis (165 +/- 61 mL/min-100g vs. 274 +/- 62 mL/min-100g, p <0.05). A significant and negative rank correlation (rho=-0.53, p<0.05) between territory MBF and CAD-RADS was seen. CCTA in conjunction with a new automated quantitative CCTP approach can augment the interpretation of CAD, enabling the distinction of ischemia due to obstructive lesions and MVD.
Labeled random finite sets vs. trajectory random finite sets
Authors: Ronald Mahler
Date: 2023-12-17 00:00:00

The paper [12] discussed two approaches for multitarget tracking (MTT): the generalized labeled multi-Bernoulli (GLMB) filter and three Poisson multi-Bernoulli mixture (PMBM) filters. The paper [13] discussed two frameworks for multitarget trajectory representation--labeled random finite set (LRFS) and set of trajectories (SoT)--and the merging of SoT and PMBM into trajectory PMBM (TPMBM) theory. This paper summarizes and augments the main findings of [12], [13]--specifcally, why SoT, PMBM, and TPMBM are physically and mathematically erroneous.
Interpolative Metric Learning for Few-Shot Specific Emitter Identification
Authors: Cheng Wang; Xue Fu; Yu Wang; Guan Gui; Haris Gacanin; Hikmet Sari; Fumiyuki Adachi
Date: 2023-07-21 00:00:00
Proceeding: IEEE Transactions on Vehicular Technology 2023 7
Tasks: Metric Learning

Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication. Radio frequency fingerprint (RFF)-based SEI is to distinguish one emitter from each other by immutable RF characteristics from electronic components. Due to the powerful ability of deep learning (DL) to extract hidden features and perform classification, it can extract highly separative features from massive signal samples, thus enabling SEI. Considering the condition of limited training samples, we propose a novel few-shot SEI (FS-SEI) method based on interpolative metric learning (InterML) which gets rid of the dependence on auxiliary dataset. Specifically, InterML is designed to mine more implicit samples in the sample space to improve generalization, and constrain the feature distance in the feature space to improve discriminability. The proposed InterML-based FS-SEI method is evaluated on a real-world Wi-Fi dataset. The simulation results show that the proposed method achieves better identification performance, higher feature discriminability and more stable performance than five latest FS-SEI methods. In the 10 shot scenario, the identification accuracy of InterML is 91.48%, compared to the comparison methods, the accuracy is improved by 0.62%–31.29%.
Overcoming Data Limitations: A Few-Shot Specific Emitter Identification Method Using Self-Supervised Learning and Adversarial Augmentation
Authors: Zhisheng Yao; Xue Fu; Lantu Guo; Yu Wang; Yun Lin; Shengnan Shi; Guan Gui
Date: 2023-09-07 00:00:00
Proceeding: IEEE Communications Letters 2023 9
Tasks: Self-Supervised Learning, Transfer Learning

Specific emitter identification (SEI) based on radio frequency fingerprinting (RFF) is a physical layer authentication method in the field of wireless network security. RFFs are unique features embedded in the electromagnetic waves, which come from the hard imperfections in the wireless devices. Deep learning has been applied to many SEI tasks due to its powerful feature extraction capabilities. However, the success of most methods hinges on massive and labeled samples, and few methods focus on a realistic scenario, where few samples are available and labeled. In this paper, to overcome data limitations, we propose a few-shot SEI (FS-SEI) method based on self-supervised learning and adversarial augmentation (SA2SEI). Specifically, to overcome the limitation of label dependence for auxiliary dataset, a novelty adversarial augmentation (Adv-Aug)-powered self-supervised learning is designed to pre-train a RFF extractor using unlabeled auxiliary dataset. Subsequently, to overcome the limitation of sample dependence, knowledge transfer is introduced to fine-tune the extractor and a classifier with target dataset including few samples (5-30 samples per emitter in this paper) and corresponding labels. In addition, auxiliary dataset and target dataset are come from different emitters. An open-source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset and a Wi-Fi dataset are used to evaluate the proposed SA2SEI method. The simulation results show that the proposed method can extract more discriminative RFF features and obtain higher identification performance in the FS-SEI. Specifically, when there are only 5 samples per Wi-Fi device, it can achieve 83.40% identification accuracy, in which 38.63% identification accuracy improvement comes from the Adv-Aug of pre-training process. The codes are available at https://github.com/LIUC-000/SA2SEI .
In-Context Learning Unlocked for Diffusion Models
Authors: Zhendong Wang, Yifan Jiang, Yadong Lu, Yelong Shen, Pengcheng He, Weizhu Chen, Zhangyang Wang, Mingyuan Zhou
Date: 2023-05-01 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: In-Context Learning, text-guided-image-editing

We present Prompt Diffusion, a framework for enabling in-context learning in diffusion-based generative models. Given a pair of task-specific example images, such as depth from/to image and scribble from/to image, and a text guidance, our model automatically understands the underlying task and performs the same task on a new query image following the text guidance. To achieve this, we propose a vision-language prompt that can model a wide range of vision-language tasks and a diffusion model that takes it as input. The diffusion model is trained jointly over six different tasks using these prompts. The resulting Prompt Diffusion model is the first diffusion-based vision-language foundation model capable of in-context learning. It demonstrates high-quality in-context generation on the trained tasks and generalizes effectively to new, unseen vision tasks with their respective prompts. Our model also shows compelling text-guided image editing results. Our framework aims to facilitate research into in-context learning for computer vision. We share our code and pre-trained models at https://github.com/Zhendong-Wang/Prompt-Diffusion.
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Authors: Pritam Sarkar, Ahmad Beirami, Ali Etemad
Date: 2023-06-03 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Open Set Learning, Representation Learning, Self-Supervised Learning

Video self-supervised learning (VSSL) has made significant progress in recent years. However, the exact behavior and dynamics of these models under different forms of distribution shift are not yet known. In this paper, we comprehensively study the behavior of six popular self-supervised methods (v-SimCLR, v-MoCo, v-BYOL, v-SimSiam, v-DINO, v-MAE) in response to various forms of natural distribution shift, i.e., (i) context shift, (ii) viewpoint shift, (iii) actor shift, (iv) source shift, (v) generalizability to unknown classes (zero-shot), and (vi) open-set recognition. To perform this extensive study, we carefully craft a test bed consisting of 17 in-distribution and out-of-distribution benchmark pairs using available public datasets and a series of evaluation protocols to stress-test the different methods under the intended shifts. Our study uncovers a series of intriguing findings and interesting behaviors of VSSL methods. For instance, we observe that while video models generally struggle with context shifts, v-MAE and supervised learning exhibit more robustness. Moreover, our study shows that v-MAE is a strong temporal learner, whereas contrastive methods, v-SimCLR and v-MoCo, exhibit strong performances against viewpoint shifts. When studying the notion of open-set recognition, we notice a trade-off between closed-set and open-set recognition performance if the pretrained VSSL encoders are used without finetuning. We hope that our work will contribute to the development of robust video representation learning frameworks for various real-world scenarios. The project page and code are available at: https://pritamqu.github.io/OOD-VSSL.
Emergency Department Decision Support using Clinical Pseudo-notes
Authors: Simon A. Lee, Sujay Jain, Alex Chen, Kyoka Ono, Jennifer Fang, Akos Rudas, Jeffrey N. Chiang
Date: 2024-01-31 00:00:00
Tasks: Text Generation

In this work, we introduce the Multiple Embedding Model for EHR (MEME), an approach that serializes multimodal EHR tabular data into text using pseudo-notes, mimicking clinical text generation. This conversion not only preserves better representations of categorical data and learns contexts but also enables the effective employment of pretrained foundation models for rich feature representation. To address potential issues with context length, our framework encodes embeddings for each EHR modality separately. We demonstrate the effectiveness of MEME by applying it to several decision support tasks within the Emergency Department across multiple hospital systems. Our findings indicate that MEME outperforms traditional machine learning, EHR-specific foundation models, and general LLMs, highlighting its potential as a general and extendible EHR representation strategy.
Evaluating Large Language Models for Generalization and Robustness via Data Compression
Authors: Yucheng Li, Yunhao Guo, Frank Guerin, Chenghua Lin
Date: 2024-02-01 00:00:00
Tasks: Articles, Data Compression

Existing methods for evaluating large language models face challenges such as data contamination, sensitivity to prompts, and the high cost of benchmark creation. To address this, we propose a lossless data compression based evaluation approach that tests how models' predictive abilities generalize after their training cutoff. Specifically, we collect comprehensive test data spanning 83 months from 2017 to 2023 and split the data into training and testing periods according to models' training data cutoff. We measure: 1) the compression performance on the testing period as a measure of generalization on unseen data; and 2) the performance gap between the training and testing period as a measure of robustness. Our experiments test 14 representative large language models with various sizes on sources including Wikipedia, news articles, code, arXiv papers, and multi-modal data. We find that the compression rate of many models reduces significantly after their cutoff date, but models such as Mistral and Llama-2 demonstrate a good balance between performance and robustness. Results also suggest that models struggle to generalize on news and code data, but work especially well on arXiv papers. We also find the context size and tokenization implementation have a big impact of on the overall compression performance.
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
Authors: Marius-Constantin Dinu, Claudiu Leoveanu-Condrei, Markus Holzleitner, Werner Zellinger, Sepp Hochreiter
Date: 2024-02-01 00:00:00
Tasks: Few-Shot Learning, In-Context Learning, Probabilistic Programming

We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. SymbolicAI enables the seamless integration of generative models with a diverse range of solvers by treating large language models (LLMs) as semantic parsers that execute tasks based on both natural and formal language instructions, thus bridging the gap between symbolic reasoning and generative AI. We leverage probabilistic programming principles to tackle complex tasks, and utilize differentiable and classical programming paradigms with their respective strengths. The framework introduces a set of polymorphic, compositional, and self-referential operations for multi-modal data that connects multi-step generative processes and aligns their outputs with user objectives in complex workflows. As a result, we can transition between the capabilities of various foundation models with in-context learning capabilities and specialized, fine-tuned models or solvers proficient in addressing specific problems. Through these operations based on in-context learning our framework enables the creation and evaluation of explainable computational graphs. Finally, we introduce a quality measure and its empirical score for evaluating these computational graphs, and propose a benchmark that compares various state-of-the-art LLMs across a set of complex workflows. We refer to the empirical score as the "Vector Embedding for Relational Trajectory Evaluation through Cross-similarity", or VERTEX score for short. The framework codebase and benchmark are linked below.
X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System
Authors: Kiymet Kaya, Elif Ak, Sumeyye Bas, Berk Canberk, sule gunduz oguducu
Date: 2024-02-01 00:00:00
Tasks: Decision Making, Intrusion Detection

The effectiveness of Intrusion Detection Systems (IDS) is critical in an era where cyber threats are becoming increasingly complex. Machine learning (ML) and deep learning (DL) models provide an efficient and accurate solution for identifying attacks and anomalies in computer networks. However, using ML and DL models in IDS has led to a trust deficit due to their non-transparent decision-making. This transparency gap in IDS research is significant, affecting confidence and accountability. To address, this paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural Networks (GNNs) to effectively process network traffic data, while also adapting a new Explainable AI (XAI) methodology. Unlike most GNN-based IDS that depend on labeled network traffic and node features, thereby overlooking critical packet-level information, our approach leverages a broader range of traffic data through network flows, including edge attributes, to improve detection capabilities and adapt to novel threats. Through empirical testing, we establish that our approach not only achieves high accuracy with 99.47% in threat detection but also advances the field by providing clear, actionable explanations of its analytical outcomes. This research also aims to bridge the current gap and facilitate the broader integration of ML/DL technologies in cybersecurity defenses by offering a local and global explainability solution that is both precise and interpretable.