Miracle-dz/UniFAR-FTUs huggingface dataset:
Accurate EH Modelling and Achievable Information Rate for SLIPT Systems with Multi-Junction Photovoltaic Receivers

In this paper, we study optical simultaneous lightwave information and power transfer (SLIPT) systems employing photovoltaic optical receivers (RXs). To be able to efficiently harvest energy from broadband light, we propose to employ multi-junction photovoltaic cells at the optical RX. We consider the case, where the optical RX is illuminated by ambient light, an intensity-modulated information free space optical (FSO) signal, and since the ambient light may not be always present, a dedicated energy-providing broadband optical signal. Based on the analysis of the equivalent electrical circuit of the multi-junction photovoltaic RX, we model the current flow through the photovoltaic cell and derive a novel accurate and two novel approximate energy harvesting (EH) models for the two cases, where the optical RX is equipped with a single and multiple p-n junctions, respectively. We derive the optimal distribution of the transmit information signal that maximizes the achievable information rate. We validate the proposed EH models by circuit simulations and show that the photovoltaic RXs saturate for high received signal powers. For single-junction RXs, we compare the proposed EH model with two well-known baseline models and demonstrate that, in contrast to the proposed EH model, they are not able to fully capture the RX non-linearity. Since multi-junction RXs allow a more efficient allocation of the optical power, they are more robust against saturation, and thus, are able to harvest significantly more power and achieve higher data rates than RXs employing a single p-n junction. Finally, we highlight a tradeoff between the information rate and harvested power in SLIPT systems and demonstrate that the proposed optimal distribution yields significantly higher achievable information rates compared to uniformly distributed transmit signals, which are optimal for linear optical information RXs.
Explorer Single-Molecule Characterization of the Interactions between Extracellular Chaperones and Toxic -Synuclein Oligomers

α 2 -macroglobulin, exhibits differential binding to α-synuclein oligomers which may be related to structural differences between two previously described forms of αS oligomers. The binding of both chaperones reduces the ability of the oligomers to permeabilize lipid membranes and prevents an oligomer-induced increase in ROS production in cultured neuronal cells. Taken together, these data suggest a neuroprotective role for extracellular chaperones in suppressing the toxicity associated with α-synuclein oligomers.
Composition in Differential Privacy for General Granularity Notions (Long Version)

The composition theorems of differential privacy (DP) allow data curators to combine different algorithms to obtain a new algorithm that continues to satisfy DP. However, new granularity notions (i.e., neighborhood definitions), data domains, and composition settings have appeared in the literature that the classical composition theorems do not cover. For instance, the parallel composition theorem does not apply to general granularity notions. This complicates the opportunity of composing DP mechanisms in new settings and obtaining accurate estimates of the incurred privacy loss after composition. To overcome these limitations, we study the composability of DP in a general framework and for any kind of data domain or neighborhood definition. We give a general composition theorem in both independent and adaptive versions and we provide analogous composition results for approximate, zero-concentrated, and Gaussian DP. Besides, we study the hypothesis needed to obtain the best composition bounds. Our theorems cover both parallel and sequential composition settings. Importantly, they also cover every setting in between, allowing us to compute the final privacy loss of a composition with greatly improved accuracy.
Eleven Years of Gender Data Visualization: A Step Towards More Inclusive Gender Representation

We present an analysis of the representation of gender as a data dimension in data visualizations and propose a set of considerations around visual variables and annotations for gender-related data. Gender is a common demographic dimension of data collected from study or survey participants, passengers, or customers, as well as across academic studies, especially in certain disciplines like sociology. Our work contributes to multiple ongoing discussions on the ethical implications of data visualizations. By choosing specific data, visual variables, and text labels, visualization designers may, inadvertently or not, perpetuate stereotypes and biases. Here, our goal is to start an evolving discussion on how to represent data on gender in data visualizations and raise awareness of the subtleties of choosing visual variables and words in gender visualizations. In order to ground this discussion, we collected and coded gender visualizations and their captions from five different scientific communities (Biology, Politics, Social Studies, Visualisation, and Human-Computer Interaction), in addition to images from Tableau Public and the Information Is Beautiful awards showcase. Overall we found that representation types are community-specific, color hue is the dominant visual channel for gender data, and nonconforming gender is under-represented. We end our paper with a discussion of considerations for gender visualization derived from our coding and the literature and recommendations for large data collection bodies. A free copy of this paper and all supplemental materials are available at https://osf.io/v9ams/.
Ozone Reactivity Measurement of Biogenic Volatile Organic 347 Compound Emissions

Ozone Reactivity Measurement of Biogenic Volatile Organic 347 Compound Emissions 348 349 350 Detlev Helmig1,2*, Alex Guenther3, Jacques Hueber1, Ryan Daly1, Wei Wang1, Jeong-Hoo Park1, 351 Anssi Liikanen4, Arnaud P. Praplan4 352 353 1Institute of Arctic and Alpine Research, University of Colorado, Boulder, CO 80309, USA 354 2 now at: Boulder Atmosphere Innovation Research LLC, Boulder, CO 80305, USA 355 3 University of California Irvine, CA, USA 356 4Atmospheric Research Composition, Finnish Meteorological Institute, 00101 Helsinki, Finland 357 *corresponding author: dh.bouldair@gmail.com 358 359 Manuscript submitted to 360 361 Atmospheric Measurement Techniques 362 363 October 25, 2021 364 365 Abstract 366 367 Previous research on atmospheric chemistry in the forest environment has shown that the total 368
Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacy

Over the last ten years, Patient-Derived Organoids (PDOs) emerged as the most reliable technology to generate ex-vivo tumor avatars. PDOs retain the main characteristics of their original tumor, making them a system of choice for pre-clinical and clinical studies. In particular, PDOs are attracting interest in the field of Functional Precision Medicine (FPM), which is based upon an ex-vivo drug test in which living tumor cells (such as PDOs) from a specific patient are exposed to a panel of anti-cancer drugs. Currently, the Adenosine Triphosphate (ATP) based cell viability assay is the gold standard test to assess the sensitivity of PDOs to drugs. The readout is measured at the end of the assay from a global PDO population and therefore does not capture single PDO responses and does not provide time resolution of drug effect. To this end, in this study, we explore for the first time the use of powerful large foundation models for the automatic processing of PDO data. In particular, we propose a novel imaging-based high-throughput screening method to assess real-time drug efficacy from a time-lapse microscopy video of PDOs. The recently proposed SAM algorithm for segmentation and DI-NOv2 model are adapted in a comprehensive pipeline for processing PDO microscopy frames. Moreover, an attention mechanism is proposed for fusing temporal and spatial features in a multiple instance learning setting to predict ATP. We report better results than other non-time-resolved methods, indicating that the temporality of data is an important factor for the prediction of ATP. Extensive ablations shed light on optimizing the experimental setting and automating the prediction both in real-time and for forecasting.
VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization

Few-shot font generation is challenging, as it needs to capture the fine-grained stroke styles from a limited set of reference glyphs, and then transfer to other characters, which are expected to have similar styles. However, due to the diversity and complexity of Chinese font styles, the synthesized glyphs of existing methods usually exhibit visible artifacts, such as missing details and distorted strokes. In this paper, we propose a VQGAN-based framework (i.e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement. Specifically, we pre-train a VQGAN to encapsulate font token prior within a code-book. Subsequently, VQ-Font refines the synthesized glyphs with the codebook to eliminate the domain gap between synthesized and real-world strokes. Furthermore, our VQ-Font leverages the inherent design of Chinese characters, where structure components such as radicals and character components are combined in specific arrangements, to recalibrate fine-grained styles based on references. This process improves the matching and fusion of styles at the structure level. Both modules collaborate to enhance the fidelity of the generated fonts. Experiments on a collected font dataset show that our VQ-Font outperforms the competing methods both quantitatively and qualitatively, especially in generating challenging styles. Our code is available at https://github.com/Yaomingshuai/VQ-Font.
An Empirical Study of Smoothing Techniques for Language Modeling

We present a tutorial introduction to n-gram models for language modeling and survey the most widely-used smoothing algorithms for such models. We then present an extensive empirical comparison of several of these smoothing techniques, including those described by Jelinek and Mercer (1980), Katz (1987), Bell, Cleary, and Witten (1990), Ney, Essen, and Kneser (1994), and Kneser and Ney (1995). We investigate how factors such as training data size, training corpus (e.g., Brown versus Wall Street Journal), count cuto s, and n-gram order (bigram versus trigram) a ect the relative performance of these methods, which is measured through the cross-entropy of test data. Our results show that previous comparisons have not been complete enough to fully characterize smoothing algorithm performance. We introduce methodologies for analyzing smoothing algorithm e cacy in detail, and using these techniques we motivate a novel variation of Kneser-Ney smoothing that consistently outperforms all other algorithms evaluated. Finally, results showing that improved language model smoothing leads to improved speech recognition performance are presented.
Learning Human-arm Reaching Motion Using IMU in Human-Robot Collaboration

Many tasks performed by two humans require mutual interaction between arms such as handing-over tools and objects. In order for a robotic arm to interact with a human in the same way, it must reason about the location of the human arm in real-time. Furthermore and to acquire interaction in a timely manner, the robot must be able predict the final target of the human in order to plan and initiate motion beforehand. In this paper, we explore the use of a low-cost wearable device equipped with two inertial measurement units (IMU) for learning reaching motion for real-time applications of Human-Robot Collaboration (HRC). A wearable device can replace or be complementary to visual perception in cases of bad lighting or occlusions in a cluttered environment. We first train a neural-network model to estimate the current location of the arm. Then, we propose a novel model based on a recurrent neural-network to predict the future target of the human arm during motion in real-time. Early prediction of the target grants the robot with sufficient time to plan and initiate motion during the motion of the human. The accuracies of the models are analyzed concerning the features included in the motion representation. Through experiments and real demonstrations with a robotic arm, we show that sufficient accuracy is achieved for feasible HRC without any visual perception. Once trained, the system can be deployed in various spaces with no additional effort. The models exhibit high accuracy for various initial poses of the human arm. Moreover, the trained models are shown to provide high success rates with additional human participants not included in the model training.
Some physical and population characteristics of Egyptian mongooses (Herpestesic.hneunion L., 1758) in southwestern Spain

Studied physical and population characteristics of Egyptian mongosses, Herpestes ichneumon, in southwestern Spain using trapping and radio-tracking. Males and famels differed (p=0.04) in body mass (3142 and 2823 g, respectively). Females were more often trapped than males (1.8F:1M), and adult than young (1.8A:1Y). Copulations occurred mainly (63.6%) in March and April, and births (77.8%) in May, June and July. The mean number of cubs per litter was 2.7±0.8 (N=7). Density for adult individuals was estimated as 1.2 ind. /km 2 . Annual survival rates were very low, oscillating between 0.13 in 1988/89 for adults and the same figure in 1987/88 for young, and 0.6 in 1987/88 for adults
Determining the parameters of the spiral arms of the Galaxy from kinematic tracers based on Gaia DR3 data

We present the results of determining the parameters of the spiral arms of the Galaxy using the stars Gaia DR3, whose absolute magnitude is MG < 4, and which allow tracing spiral arms at large distances from the Sun. As tracers of spiral arms, we use the centroids of stellar spherical regions with a radius of 0.5 kpc, in which the deformation velocities along the coordinate axis R are insignificant. These kinematic tracers cover the Galactic plane within the Galactocentric coordinate ranges 140° < θ < 220° and 4 kpc < R < 14 kpc. The numerical values of the pitch angles of the spirals and their Galactocentric distances to the point of intersection of the spiral with the direction of the Galactic Centre and the Sun are in good agreement with the results of other authors. By extrapolating beyond the data we have, we present a schematic four-arm global pattern, consisting of the Scutum–Centaurus, Sagittarius–Carina, Perseus, Norma-Outer arms, as well as the local arm Orion. The uncertainties of the determined spiral parameters confirm that the structures identified are not false, but are reliable from the statistical point of view.
An Iterative Sample Scenario Approach for the Dynamic Dispatch Waves Problem

A challenge in same-day delivery operations is that delivery requests are typically not known beforehand, but are instead revealed dynamically during the day. This uncertainty introduces a trade-off between dispatching vehicles to serve requests as soon as they are revealed to ensure timely delivery and delaying the dispatching decision to consolidate routing decisions with future, currently unknown requests. In this paper, we study the dynamic dispatch waves problem, a same-day delivery problem in which vehicles are dispatched at fixed decision moments. At each decision moment, the system operator must decide which of the known requests to dispatch and how to route these dispatched requests. The operator’s goal is to minimize the total routing cost while ensuring that all requests are served on time. We propose iterative conditional dispatch (ICD), an iterative solution construction procedure based on a sample scenario approach. ICD iteratively solves sample scenarios to classify requests to be dispatched, postponed, or undecided. The set of undecided requests shrinks in each iteration until a final dispatching decision is made in the last iteration. We develop two variants of ICD: one variant based on thresholds, and another variant based on similarity. A significant strength of ICD is that it is conceptually simple and easy to implement. This simplicity does not harm performance: through rigorous numerical experiments, we show that both variants efficiently navigate the large state and action spaces of the dynamic dispatch waves problem and quickly converge to a high-quality solution. Finally, we demonstrate that the threshold-based ICD variant achieves excellent results on instances from the EURO Meets NeurIPS 2022 Vehicle Routing Competition, nearly matching the performance of the winning machine learning–based strategy. History: This paper has been accepted for the Transportation Science Special Issue on DIMACS Implementation Challenge: Vehicle Routing Problems. Funding: This work was supported by TKI Dinalog, Topsector Logistics, and the Dutch Ministry of Economic Affairs and Climate Policy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023. 0111 .
A generalized Bayesian stochastic block model for microbiome community detection

Advances in next-generation sequencing technology have enabled the high-throughput profiling of metagenomes and accelerated the microbiome study. Recently, there has been a rise in quantitative studies that aim to decipher the microbiome co-occurrence network and its underlying community structure based on metagenomic sequence data. Uncovering the complex microbiome community structure is essential to understanding the role of the microbiome in disease progression and susceptibility. Taxonomic abundance data generated from metagenomic sequencing technologies are high-dimensional and compositional, suffering from uneven sampling depth, over-dispersion, and zero-inflation. These characteristics often challenge the reliability of the current methods for microbiome community detection. To this end, we propose a Bayesian stochastic block model to study the microbiome co-occurrence network based on the recently developed modified centered-log ratio transformation tailored for microbiome data analysis. Our model allows us to incorporate taxonomic tree information using a Markov random field prior. The model parameters are jointly inferred by using Markov chain Monte Carlo sampling techniques. Our simulation study showed that the proposed approach performs better than competing methods even when taxonomic tree information is non-informative. We applied our approach to a real urinary microbiome dataset from postmenopausal women, the first time the urinary microbiome co-occurrence network structure has been studied. In summary, this statistical methodology provides a new tool for facilitating advanced microbiome studies.
An underground fiber cable discrimination method based on laser interferometer

The maintenance and repair of optical fiber networks often requires the discrimination of underground cables among a group of buried ones. Presently, the methods commonly used are either inefficient or harmful to the fiber cable. In this paper, we evaluate the effectiveness of fiber optic vibration sensing method on underground fiber cable discrimination. We find that the typical vibration sensing method-distributed acoustic sensing (DAS) is not suitable for fiber cable discrimination. Especially for long distance scenario, due to"frequency grafting"effect, the DAS method will give a false response on the spectrum of knock events during cable discrimination process. In response, we propose an underground fiber cable discrimination method based on laser interferometer, and demonstrate it on the 40-km urban fiber cable connecting Tsinghua University and Yongding Road. It can give an obvious and real response to the knock event, and can be used in practical applications.
Queering the ethics of AI

This book chapter delves into the pressing need to"queer"the ethics of AI to challenge and re-evaluate the normative suppositions and values that underlie AI systems. The chapter emphasizes the ethical concerns surrounding the potential for AI to perpetuate discrimination, including binarism, and amplify existing inequalities due to the lack of representative datasets and the affordances and constraints depending on technology readiness. The chapter argues that a critical examination of the neoliberal conception of equality that often underpins non-discrimination law is necessary and cannot stress more the need to create alternative interdisciplinary approaches that consider the complex and intersecting factors that shape individuals' experiences of discrimination. By exploring such approaches centering on intersectionality and vulnerability-informed design, the chapter contends that designers and developers can create more ethical AI systems that are inclusive, equitable, and responsive to the needs and experiences of all individuals and communities, particularly those who are most vulnerable to discrimination and harm.
Cura: Curation at Social Media Scale

How can online communities execute a focused vision for their space? Curation offers one approach, where community leaders manually select content to share with the community. Curation enables leaders to shape a space that matches their taste, norms, and values, but the practice is often intractable at social media scale: curators cannot realistically sift through hundreds or thousands of submissions daily. In this paper, we contribute algorithmic and interface foundations enabling curation at scale, and manifest these foundations in a system called Cura. Our approach draws on the observation that, while curators' attention is limited, other community members' upvotes are plentiful and informative of curators' likely opinions. We thus contribute a transformer-based curation model that predicts whether each curator will upvote a post based on previous community upvotes. Cura applies this curation model to create a feed of content that it predicts the curator would want in the community. Evaluations demonstrate that the curation model accurately estimates opinions of diverse curators, that changing curators for a community results in clearly recognizable shifts in the community's content, and that, consequently, curation can reduce anti-social behavior by half without extra moderation effort. By sampling different types of curators, Cura lowers the threshold to genres of curated social media ranging from editorial groups to stakeholder roundtables to democracies.
Three‐dimensional structure determination of an anti‐2‐phenyloxazolone antibody: the role of somatic mutation and heavy/light chain pairing in the maturation of an immune response.

The three‐dimensional structure of the Fab fragment of an anti‐2‐phenyloxazolone monoclonal antibody (NQ10/12.5) in its native and complexed forms has been determined at 2.8 and 3.0 A resolution, respectively. Identification of hapten‐contacting residues has allowed us to evaluate the contribution of individual somatic point mutations to maturation of the immune response. In particular, amino acid residues 34 and 36 of the light chain, which are frequently mutated in antibodies with increased affinity for 2‐phenyloxazolone, are shown to interact directly with the hapten. We propose that the strict maintenance of certain amino acid sequences at the potentially highly variable VL‐JL and VH‐D‐JH junctions observed among anti‐2‐phenyloxazolone antibodies is due largely to structural constraints related to antigen recognition. Finally, the three‐dimensional model of NQ10/12.5, which uses the typical light chain of primary response anti‐2‐phenyloxazolone antibodies but a different heavy chain, allows an understanding of how, by preserving key contact residues, a given heavy chain may be replaced by another, apparently unrelated one, without loss of hapten binding activity and why the V kappa Ox1 germline gene is so frequently selected amongst the other known members of this family.