Miracle-dz/UniFAR-FTUs huggingface dataset:
Memory signatures in path curvature of self-avoidant model particles are revealed by time delayed self mutual information

Emergent behavior in active systems is a complex byproduct of local, often pairwise, interactions. One such interaction is self-avoidance, which experimentally can arise as a response to self-generated environmental signals; such experiments have inspired non-Markovian mathematical models. In previous work, we set out to find ``hallmarks of self-avoidant memory"in a particle model for environmentally responsive swimming droplets. In our analysis, we found that transient self-trapping was a spatial hallmark of the particle's self-avoidant memory response. The self-trapping results from the combined effects of behaviors at multiple scales: random reorientations, which occur on the diffusion scale, and the self-avoidant memory response, which occurs on the ballistic (and longer) timescales. In this work, we use the path curvature as it encodes the self-trapping response to estimate an ``effective memory lifetime"by analyzing the decay of its time-delayed mutual information and subsequently determining the longevity of significant nonlinear correlations. This effective memory lifetime (EML) is longer in systems where the curvature is a product of both self-avoidance and random reorientations as compared to systems without self-avoidance.
Exploring Co‐training strategies for opinion detection

For the last decade or so, sentiment analysis, which aims to automatically identify opinions, polarities, or emotions from user‐generated content (e.g., blogs, tweets), has attracted interest from both academic and industrial communities. Most sentiment analysis strategies fall into 2 categories: lexicon‐based and corpus‐based approaches. While the latter often requires sentiment‐labeled data to build a machine learning model, both approaches need sentiment‐labeled data for evaluation. Unfortunately, most data domains lack sufficient quantities of labeled data, especially at the subdocument level. Semisupervised learning (SSL), a machine learning technique that requires only a few labeled examples and can automatically label unlabeled data, is a promising strategy to deal with the issue of insufficient labeled data. Although previous studies have shown promising results of applying various SSL algorithms to solve a sentiment‐analysis problem, co‐training, an SSL algorithm, has not attracted much attention for sentiment analysis largely due to its restricted assumptions. Therefore, this study focuses on revisiting co‐training in depth and discusses several co‐training strategies for sentiment analysis following a loose assumption. Results suggest that co‐training can be more effective than can other currently adopted SSL methods for sentiment analysis.
Discovery of cancer common and specific driver gene sets

Abstract Cancer is known as a disease mainly caused by gene alterations. Discovery of mutated driver pathways or gene sets is becoming an important step to understand molecular mechanisms of carcinogenesis. However, systematically investigating commonalities and specificities of driver gene sets among multiple cancer types is still a great challenge, but this investigation will undoubtedly benefit deciphering cancers and will be helpful for personalized therapy and precision medicine in cancer treatment. In this study, we propose two optimization models to de novo discover common driver gene sets among multiple cancer types (ComMDP) and specific driver gene sets of one certain or multiple cancer types to other cancers (SpeMDP), respectively. We first apply ComMDP and SpeMDP to simulated data to validate their efficiency. Then, we further apply these methods to 12 cancer types from The Cancer Genome Atlas (TCGA) and obtain several biologically meaningful driver pathways. As examples, we construct a common cancer pathway model for BRCA and OV, infer a complex driver pathway model for BRCA carcinogenesis based on common driver gene sets of BRCA with eight cancer types, and investigate specific driver pathways of the liquid cancer lymphoblastic acute myeloid leukemia (LAML) versus other solid cancer types. In these processes more candidate cancer genes are also found.
Trade-off between spectral efficiency increase and PAPR reduction when using FTN signaling: Impact of non linearities

Faster-than-Nyquist (FTN) signaling appears as an attractive method to improve spectral efficiency at the price of an increased complexity at the receiver. The receiver generally implements a turbo-equalization/detection scheme to benefit from all the promises of the FTN signaling. However, this is not the only limitation we have to deal with. Indeed, compressing in the time domain impact the emitted signal and it usually results in an increase of the envelope fluctuations. This leads to an inherent multi-objectives trade-off between performance, targeted spectral efficiency and limited Peak to Average Power Ratio (PAPR). The last aspect is crucial when considering a satellite communication link due to non-linear amplification effects that can occur on-board the satellite. Usually, FTN studies focus on spectral efficiency increase for a fixed modulation order, trying to trade-off between performance and PAPR properties. In this paper, we show that, for a given asymptotic spectral efficiency, we can compress low order modulations to increase the spectral efficiency of these schemes while controlling the PAPR increase to achieve a better PAPR than the non compressed scheme with a higher modulation order. Thus, for the same asymptotic spectral efficiency, we can achieve 1 dB gain in terms of PAPR and 2 dB gain in Bit Error Rate (BER) performances for a coded 8-PSK FTN system compared to a coded 16-APSK. For the same BER performances, the asymptotic spectral efficiency gain obtained in linear context is over 20 %, higher when non-linearities are taken into account.
A Public and Reproducible Assessment of the Topics API on Real Data

The Topics API for the web is Google's privacy-enhancing alternative to replace third-party cookies. Results of prior work have led to an ongoing discussion between Google and research communities about the capability of Topics to trade off both utility and privacy. The central point of contention is largely around the realism of the datasets used in these analyses and their reproducibility; researchers using data collected on a small sample of users or generating synthetic datasets, while Google's results are inferred from a private dataset. In this paper, we complement prior research by performing a reproducible assessment of the latest version of the Topics API on the largest and publicly available dataset of real browsing histories. First, we measure how unique and stable real users' interests are over time. Then, we evaluate if Topics can be used to fingerprint the users from these real browsing traces by adapting methodologies from prior privacy studies. Finally, we call on web actors to perform and enable reproducible evaluations by releasing anonymized distributions. We find that 46%, 55%, and 60% of the 1207 users in the dataset are uniquely re-identified across websites after only 1, 2, and 3 observations of their topics by advertisers, respectively. This paper shows on real data that Topics does not provide the same privacy guarantees to all users, further highlighting the need for public and reproducible evaluations of the claims made by new web proposals.
Safety-Critical Planning and Control for Dynamic Obstacle Avoidance Using Control Barrier Functions

Dynamic obstacle avoidance is a challenging topic for optimal control and optimization-based trajectory planning problems, especially when in a tight environment. Many existing works use control barrier functions (CBFs) to enforce safety constraints within control systems. Inside these works, CBFs are usually formulated under model predictive control (MPC) framework to anticipate future states and make informed decisions, or integrated with path planning algorithms as a safety enhancement tool. However, these approaches usually require knowledge of the obstacle boundary equations or have very slow computational efficiency. In this paper, we propose a novel framework to the iterative MPC with discrete-time CBFs (DCBFs) to generate a collision-free trajectory. The DCBFs are obtained from convex polyhedra generated in sequential grid maps, without the need to know the boundary equations of obstacles. Additionally, a path planning algorithm is incorporated into this framework to ensure the global optimality of the generated trajectory. We demonstrate through numerical examples that our framework enables a unicycle robot to safely and efficiently navigate through tight and dynamically changing environments, tackling both convex and nonconvex obstacles with remarkable computing efficiency and reliability in control and trajectory generation.
AIC-UNet: Anatomy-informed Cascaded UNet for Robust Multi-Organ Segmentation

Imposing key anatomical features, such as the number of organs, their shapes, sizes, and relative positions, is crucial for building a robust multi-organ segmentation model. Current attempts to incorporate anatomical features include broadening effective receptive fields (ERF) size with resource- and data-intensive modules such as self-attention or introducing organ-specific topology regularizers, which may not scale to multi-organ segmentation problems where inter-organ relation also plays a huge role. We introduce a new approach to impose anatomical constraints on any existing encoder-decoder segmentation model by conditioning model prediction with learnable anatomy prior. More specifically, given an abdominal scan, a part of the encoder spatially warps a learnable prior to align with the given input scan using thin plate spline (TPS) grid interpolation. The warped prior is then integrated during the decoding phase to guide the model for more anatomy-informed predictions. Code is available at \hyperlink{https://anonymous.4open. science/r/AIC-UNet-7048}{https://anonymous.4open. science/r/AIC-UNet-7048}.
Disentangling degree and tie strength heterogeneity in egocentric social networks

The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few close, emotionally intense relationships and a larger number of weaker ties. Recent results indicate this feature is universal across communication channels. Within this general pattern, there is a substantial and persistent inter-individual variation that is also similarly distributed among channels. The reason for the observed universality is yet unclear -- one possibility is that people's traits determine their personal network features on any channel. To address this hypothesis, we need to compare an individual's personal networks across channels, which is a non-trivial task: while we are interested in measuring the differences in tie strength heterogeneity, personal network size is also expected to vary a lot across channels. Therefore, for any measure that compares personal networks, one needs to understand the sensitivity with respect to network size. Here, we study different measures of personal network similarity and show that a recently introduced alter-preferentiality parameter and the Gini coefficient are equally suitable measures for tie strength heterogeneity, as they are fairly insensitive to differences in network size. With these measures, we show that the earlier observed individual-level persistence of personal network structure cannot be attributed to network size stability alone, but that the tie strength heterogeneity is persistent too. We also demonstrate the effectiveness of the two measures on multichannel data, where tie strength heterogeneity in personal networks is seen to moderately correlate for the same users across two communication channels (calls and text messages).
Generate then Retrieve: Conversational Response Retrieval Using LLMs as Answer and Query Generators

CIS is a prominent area in IR that focuses on developing interactive knowledge assistants. These systems must adeptly comprehend the user's information requirements within the conversational context and retrieve the relevant information. To this aim, the existing approaches model the user's information needs with one query called rewritten query and use this query for passage retrieval. In this paper, we propose three different methods for generating multiple queries to enhance the retrieval. In these methods, we leverage the capabilities of large language models (LLMs) in understanding the user's information need and generating an appropriate response, to generate multiple queries. We implement and evaluate the proposed models utilizing various LLMs including GPT-4 and Llama-2 chat in zero-shot and few-shot settings. In addition, we propose a new benchmark for TREC iKAT based on gpt 3.5 judgments. Our experiments reveal the effectiveness of our proposed models on the TREC iKAT dataset.
Dominant resource fairness in cloud computing systems with heterogeneous servers

We study the multi-resource allocation problem in cloud computing systems where the resource pool is constructed from a large number of heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, and storage. We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; no one can improve its allocation without decreasing that of the others; and more importantly, no user has an incentive to lie about its resource demand. As a direct application, we design a simple heuristic that implements DRFH in real-world systems. Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.
Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM

Implicit neural representation (INR), in combination with geometric rendering, has recently been employed in real-time dense RGB-D SLAM. Despite active research endeavors being made, there lacks a unified protocol for fair evaluation, impeding the evolution of this area. In this work, we establish, to our knowledge, the first open-source benchmark framework to evaluate the performance of a wide spectrum of commonly used INRs and rendering functions for mapping and localization. The goal of our benchmark is to 1) gain an intuition of how different INRs and rendering functions impact mapping and localization and 2) establish a unified evaluation protocol w.r. t. the design choices that may impact the mapping and localization. With the framework, we conduct a large suite of experiments, offering various insights in choosing the INRs and geometric rendering functions: for example, the dense feature grid outperforms other INRs (e.g. tri-plane and hash grid), even when geometric and color features are jointly encoded for memory efficiency. To extend the findings into the practical scenario, a hybrid encoding strategy is proposed to bring the best of the accuracy and completion from the grid-based and decomposition-based INRs. We further propose explicit hybrid encoding for high-fidelity dense grid mapping to comply with the RGB-D SLAM system that puts the premise on robustness and computation efficiency.
The Synthetic Antimicrobial Peptide Pexiganan and Its Nanoparticles (PNPs) Exhibit the Anti-Helicobacter pylori Activity in Vitro and in Vivo

The aim of this study was to probe the potential anti-H. pylori activity of the synthetic antimicrobial peptide pexiganan, which is an analog of the peptide magainin, and its nanoparticles (PNPs) that were prepared in our laboratory. To compare their antibacterial effects in vitro and in vivo, studies of H. pylori growth inhibition, kinetics and resistance assays were undertaken. The gastric mucoadhesive efficiency and H. pylori clearance efficiency of pexiganan and PNPs were evaluated in rats and mice infected with H. pylori. The eradication of H. pylori was determined using urease tests and a microbial culture method. We observed that PNPs adhered to gastric mucosa more effectively owing to a prolonged stay in the stomach, which resulted in a more effective H. pylori clearance. In addition, PNPs had greater anti-H. pylori effect than pexiganan in infected mice. The amount of pexiganan required to eradicate H. pylori was significantly less using PNPs than the corresponding pexiganan suspension. The results confirmed that PNPs improved peptide stability in the stomach and more effectively eradicated H. pylori from mice stomachs than pexiganan.
An Enhanced Energy Aware Coverage Preservation with Static Clustering for WSN

wireless sensor network consists of a huge number of sensor nodes associated via wireless medium. Nodes in a WSN are organized with limited battery energy and thus improvement on network lifetime by reducing energy-usage is of extreme important. One of the solutions to minimize such energy-usage is associate clustering of network nodes. In this paper an associate clustering scheme in WSN is proposed where cluster heads are formed by creating a probable associate list is in descending order in which cluster heads are organized according to their residual energy. We have conducted simulation-based evaluations to compare the performance of EEACPSC against Energy Aware Coverage Preservation and Lifetime Enhancement Protocol (EACPLEP) and Enhanced-Energy Efficient Protocol with Static Clustering (E3PSC). Our experiment results show that EEACPSC outperforms EACPELP and E3PSC in terms of network lifetime and power consumption minimization. And its results come out in 3D form.
[Treatment of intractable postherpetic neuralgia and blepharospasm: intraneural injection of adriamycin].

Adriamycin, an anthracycline antineoplastic agent, can swiftly be transported to the sensory or somatic motor neurons by way of axoplasmic transport when injected into the subepineurium of the trigeminal nerve or sciatic nerve in experimental animals, and is consequently able to induce degeneration of the neurons without any systemic side effects. Intraneural injection of this agent was carried out for the treatment of a total of 22 patients presenting with intractable neural dysfunction (12 with neuralgia, including 7 with post-herpetic neuralgia and 10 with facial dystonia). The nerve which innervated the affected site was exposed under local anesthesia and approximately 10-60 microliters of 1-20% adriamycin was injected into the subepineurium. Results of the treatment after average follow-up periods of 21.5 months were as follows: Out of 12 patients with neuralgia, good results were obtained in 2 cases (16.7%), fair results in 6 cases (50.0%) (overall effective rate 67.7%). There were no changes in symptoms in 4 cases (33.3%). Out of 10 patients with facial dystonia, good results were obtained in 2 cases (20.0%), fair in 2 cases (20.0%) (overall effective rate 40.0%), and no changes in symptoms in 6 cases (60.0%). No major complications were encountered during these procedures and, once symptoms had disappeared after the treatment, no recurrence of symptoms was experienced. This method clearly differs from other various kinds of simple peripheral neurotomy, since transection of the peripheral nerve does not cause any, destructive changes in the sensory ganglion or motor nucleus and, hence, symptoms may recur. (ABSTRACT TRUNCATED AT 250 WORDS)
SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data

Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software (i.e., CellProfiler) requires computational resources that are not available to most laboratories worldwide. In addition, the image-embedded cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. Here we introduce SPACe (Swift Phenotypic Analysis of Cells), an open source, Python-based platform for the analysis of single cell image-based morphological profiles produced by CP experiments. SPACe can process a typical dataset approximately ten times faster than CellProfiler on common desktop computers without loss in mechanism of action (MOA) recognition accuracy. It also computes directional distribution-based distances (Earth Mover’s Distance – EMD) of morphological features for quality control and hit calling. We highlight several advantages of SPACe analysis on CP assays, including reproducibility across multiple biological replicates, easy applicability to multiple (∼20) cell lines, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We ultimately illustrate the advantages of SPACe in a screening campaign of cell metabolism small molecule inhibitors which we performed in seven cell lines to highlight the importance of testing perturbations across models.
An Image Quality Transfer Technique for Localising Deep Brain Stimulation Targets

The ventral intermediate nucleus of the thalamus (Vim) is a well-established surgical target in functional neurosurgery for the treatment of tremor. As the structure lacks intrinsic contrast on conventional MRI sequences, targeting the Vim has predominantly relied on standardised Vim atlases which can fail to account for individual anatomical variability. To overcome this limitation, recent studies define the Vim using its structural connectivity profile generated via tractography. Although successful in accounting for individual variability, these connectivity-based methods are sensitive to variations in image acquisition and processing, and require high-quality diffusion imaging protocols which are usually not available in clinical settings. Here we propose a novel transfer learning approach to accurately target the Vim particularly on clinical-quality data. The approach transfers anatomical information from publicly-available high-quality datasets to a wide range of white matter connectivity features in low-quality data to augment inference on the Vim. We demonstrate that the approach can robustly and reliably identify Vim even with compromised data quality and is generalisable to datasets acquired with different protocols, outperforming previous surgical targeting methods. The approach is not limited to targeting Vim and can be adapted to other deep brain structures.