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With over 500 languages in Nigeria, three languages -- Hausa, Yor\`ub\'a and Igbo -- spoken by over 175 million people, account for about 60% of the spoken languages. However, these languages are categorised as low-resource due to insufficient resources to support tasks in computational linguistics. Several research efforts and initiatives have been presented, however, a coherent understanding of the state of Natural Language Processing (NLP) - from grammatical formalisation to linguistic resources that support complex tasks such as language understanding and generation is lacking. This study presents the first comprehensive review of advancements in low-resource NLP (LR-NLP) research across the three major Nigerian languages (NaijaNLP). We quantitatively assess the available linguistic resources and identify key challenges. Although a growing body of literature addresses various NLP downstream tasks in Hausa, Igbo, and Yor\`ub\'a, only about 25.1% of the reviewed studies contribute new linguistic resources. This finding highlights a persistent reliance on repurposing existing data rather than generating novel, high-quality resources. Additionally, language-specific challenges, such as the accurate representation of diacritics, remain under-explored. To advance NaijaNLP and LR-NLP more broadly, we emphasise the need for intensified efforts in resource enrichment, comprehensive annotation, and the development of open collaborative initiatives., Comment: 35 pages, 2 figures, 4 tables

Bathtub failure rate (BFR) and roller-coaster failure rate (RCFR - a sequence of BFR and inverted BFR (IBFR)) shapes are among the non-monotone failure rate function (FRF) behaviors often observed in complex or competing risks (CR) datasets. Recent studies have introduced varied bathtub failure rate models for reliability modeling of such datasets. However, limited attention is paid to the reliability study of CR datasets characterized by RCFR. Motivated by this drawback, this paper proposes the so-called Bi-Failure Modes (BFM) model for robust reliability analysis of CR data exhibiting BFR, RCFR, and several other FRF shapes. The mean residual life function (MRLF) and cause-specific failure probabilities are studied in detail. The fundamental reciprocal relationships between the MRLF and FRF are established. We propose the Hamiltonian Monte Carlo (HMC)-based Bayesian framework for estimating the BFM parameters and its reliability attributes to offer greater computational efficiency and faster inference. Two CR datasets from electrode voltage endurance life and electrical appliance tests, respectively, characterized by BFR and RCFR behaviors, are employed to demonstrate the BFM adequacy. The recently introduced Bridge Criterion (BC) metric and other metrics are used to evaluate the BFM modeling performance against five recent methodologies under the maximum likelihood technique. The BFM compatibility with the two datasets is also examined. The findings portrayed the BFM's advantage over other competing candidates., Comment: 24 pages, 10 figures, 8 tables

With the rapid development of next-generation Internet of Things (NG-IoT) networks, the increasing number of connected devices has led to a surge in power consumption. This rise in energy demand poses significant challenges to resource availability and raises sustainability concerns for large-scale IoT deployments. Efficient energy utilization in communication networks, particularly for power-constrained IoT devices, has thus become a critical area of research. In this paper, we deployed flying LoRa gateways (GWs) mounted on unmanned aerial vehicles (UAVs) to collect data from LoRa end devices (EDs) and transmit it to a central server. Our primary objective is to maximize the global system energy efficiency (EE) of wireless LoRa networks by joint optimization of transmission power (TP), spreading factor (SF), bandwidth (W), and ED association. To solve this challenging problem, we model the problem as a partially observable Markov decision process (POMDP), where each flying LoRa GW acts as a learning agent using a cooperative Multi-Agent Reinforcement Learning (MARL) approach under centralized training and decentralized execution (CTDE). Simulation results demonstrate that our proposed method, based on the multi-agent proximal policy optimization (MAPPO) algorithm, significantly improves the global system EE and surpasses the conventional MARL schemes., Comment: 6 pages, 6 figures

When humans interact with learning-based control systems, a common goal is to minimize a cost function known only to the human. For instance, an exoskeleton may adapt its assistance in an effort to minimize the human's metabolic cost-of-transport. Conventional approaches to synthesizing the learning algorithm solve an inverse problem to infer the human's cost. However, these problems can be ill-posed, hard to solve, or sensitive to problem data. Here we show a game-theoretic learning algorithm that works solely by observing human actions to find the cost minimum, avoiding the need to solve an inverse problem. We evaluate the performance of our algorithm in an extensive set of human subjects experiments, demonstrating consistent convergence to the minimum of a prescribed human cost function in scalar and multidimensional instantiations of the game. We conclude by outlining future directions for theoretical and empirical extensions of our results.

We report the confirmation and characterization of four hot Jupiter-type exoplanets initially detected by TESS: TOI-1295 b, TOI-2580 b, TOI-6016 b, and TOI-6130 b. Using observations with the high-resolution echelle spectrograph MaHPS on the 2.1m telescope at Wendelstein Observatory, together with NEID at Kitt Peak National Observatory and TRES at the Fred Lawrence Whipple Observatory, we confirmed the planetary nature of these four planet candidates. We also performed precise mass measurements. All four planets are found to be hot Jupiters with orbital periods between 2.4 and 4.0 days. The sizes of these planets range from 1.29 to 1.64 Jupiter radii, while their masses range from 0.6 to 1.5 Jupiter masses. Additionally, we investigated whether there are signs of other planets in the systems but have found none. Lastly, we compared the radii of our four objects to the results of an empirical study of radius inflation and see that all four demonstrate a good fit with the current models. These four planets belong to the first array of planets confirmed with MaHPS data, supporting the ability of the spectrograph to detect planets around fainter stars as faint as V=12.

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables

Cells and microorganisms employ dynamic shape changes to enable steering and avoidance for efficient spatial exploration and collective organization. In contrast, active colloids, their synthetic counterparts, currently lack similar abilities and strategies. Through physical interactions alone, here we create active colloidal molecules that spontaneously reconfigure their structure, unlike traditional active particles. We find that self-reconfiguration decouples reorientational dynamics from rotational diffusivity and bestows our active molecules additional reorientation capabilities. During encounters with neighbors, rapid conformational changes lead to self-steering and avoidance. At higher area fractions, reconfiguration-induced avoidance fully inhibits characteristic dynamic clustering, motility-induced phase separation and flocking; instead, the system retains a homogeneous structure comprising well-separated active units. Self-reconfiguring systems therefore present an exciting path towards autonomous motion beyond that of classical synthetic active matter.

Active colloidal particles typically exhibit a pronounced affinity for accumulating and being captured at boundaries. Here, we engineer long-range repulsive interactions between colloids that self-propel under an electric field and patterned obstacles. As a result of these interactions, particles turn away from obstacles and avoid accumulation. We show that by tuning the applied field frequency, we precisely and rapidly control the effective size of the obstacles and therefore modulate the particle approach distance. This feature allows us to achieve gating and tunable confinement of our active particles whereby they can access regions between obstacles depending on the applied field. Our work provides a versatile means to directly control confinement and organization, paving the way towards applications such as sorting particles based on motility or localizing active particles on demand.

Active systems comprising micron-sized self-propelling units, also termed microswimmers, are promising candidates for the bottom-up assembly of small structures and reconfigurable materials. Here we leverage field-driven colloidal assembly to induce structural transformations in dense layers of microswimmers driven by an alternating current (AC) electric field and confined in a microfabricated trap under the influence of gravity. By varying the electric field frequency, we realize significant structural transformations, from a gas-like state at high frequencies to dynamically rearranging dense crystalline clusters at lower frequencies, characterized by vorticity in their dynamics. We demonstrate the ability to switch between these states on-demand, showing that the clustering mechanism differs from motility-induced phase separation. Our results offer a valuable framework for designing high-density active matter systems with controllable structural properties, envisioned to advance the development of artificial materials with self-healing and reconfiguration capabilities.

The piecewise complexity $h(u)$ of a word is the minimal length of subwords needed to exactly characterise $u$. Its piecewise minimality index $\rho(u)$ is the smallest length $k$ such that $u$ is minimal among its order-$k$ class $[u]_k$ in Simon's congruence. We initiate a study of these two descriptive complexity measures. Among other results we provide efficient algorithms for computing $h(u)$ and $\rho(u)$ for a given word $u$.

Contacts between particles in dense, sheared suspensions are believed to underpin much of their rheology. Roughness and adhesion are known to constrain the relative motion of particles, and thus globally affect the shear response, but an experimental description of how they microscopically influence the transmission of forces and relative displacements within contacts is lacking. Here we show that an innovative colloidal-probe atomic force microscopy technique allows the simultaneous measurement of normal and tangential forces exchanged between tailored surfaces and microparticles while tracking their relative sliding and rolling, unlocking the direct measurement of coefficients of rolling friction, as well as of sliding friction. We demonstrate that, in the presence of sufficient traction, particles spontaneously roll, reducing dissipation and promoting longer-lasting contacts. Conversely, when rolling is prevented, friction is greatly enhanced for rough and adhesive surfaces, while smooth particles coated by polymer brushes maintain well-lubricated contacts. We find that surface roughness induces rolling due to load-dependent asperity interlocking, leading to large off-axis particle rotations. In contrast, smooth, adhesive surfaces promote rolling along the principal axis of motion. Our results offer direct values of friction coefficients for numerical studies and an interpretation of the onset of discontinuous shear thickening based on them, opening up new ways to tailor rheology via contact engineering., Comment: 7 Figures

Activity and autonomous motion are fundamental aspects of many living and engineering systems. Here, the scale of biological agents covers a wide range, from nanomotors, cytoskeleton, and cells, to insects, fish, birds, and people. Inspired by biological active systems, various types of autonomous synthetic nano- and micromachines have been designed, which provide the basis for multifunctional, highly responsive, intelligent active materials. A major challenge for understanding and designing active matter is their inherent non-equilibrium nature due to persistent energy consumption, which invalidates equilibrium concepts such as free energy, detailed balance, and time-reversal symmetry. Furthermore, interactions in ensembles of active agents are often non-additive and non-reciprocal. An important aspect of biological agents is their ability to sense the environment, process this information, and adjust their motion accordingly. It is an important goal for the engineering of micro-robotic systems to achieve similar functionality. With many fundamental properties of motile active matter now reasonably well understood and under control, the ground is prepared for the study of physical aspects and mechanisms of motion in complex environments, of the behavior of systems with new physical features like chirality, of the development of novel micromachines and microbots, of the emergent collective behavior and swarming of intelligent self-propelled particles, and of particular features of microbial systems. The vast complexity of phenomena and mechanisms involved in the self-organization and dynamics of motile active matter poses major challenges, which can only be addressed by a truly interdisciplinary effort involving scientists from biology, chemistry, ecology, engineering, mathematics, and physics.

Integrating machine learning (ML) into customer service chatbots enhances their ability to understand and respond to user queries, ultimately improving service performance. However, they may appear artificial to some users and affecting customer experience. Hence, meticulous evaluation of ML models for each pipeline component is crucial for optimizing performance, though differences in functionalities can lead to unfair comparisons. In this paper, we present a tailored experimental evaluation approach for goal-oriented customer service chatbots with pipeline architecture, focusing on three key components: Natural Language Understanding (NLU), dialogue management (DM), and Natural Language Generation (NLG). Our methodology emphasizes individual assessment to determine optimal ML models. Specifically, we focus on optimizing hyperparameters and evaluating candidate models for NLU (utilizing BERT and LSTM), DM (employing DQN and DDQN), and NLG (leveraging GPT-2 and DialoGPT). The results show that for the NLU component, BERT excelled in intent detection whereas LSTM was superior for slot filling. For the DM component, the DDQN model outperformed DQN by achieving fewer turns, higher rewards, as well as greater success rates. For NLG, the large language model GPT-2 surpassed DialoGPT in BLEU, METEOR, and ROUGE metrics. These findings aim to provide a benchmark for future research in developing and optimizing customer service chatbots, offering valuable insights into model performance and optimal hyperparameters.

In the previously submitted version of this paper, available here for the record, we stated the following : "We give a self-contained proof of Isa Vialard's formula for $o(P\cdot Q)$ where $P$ and $Q$ are wpos. The proof introduces the notion of a cut of partial order, which might be of independent interest." In fact, the argument presented in the paper is wrong and Vialard formula has no known proof. I will try to prove the formula $o(P\cdot Q)=o(P)\cdot o(Q)$ from the [DzSS] paper because I believe that Altman's purported counter-example mentioned in the preprint is incorrect. This statement is written by Mirna D\v{z}amonja without consultation with Isa Vialard, who may hold different views. Mirna D\v{z}amonja has withdrawn her authorship from the conditionally accepted version of this note (IGPL) on January 20, 2025, Comment: An incorrect attribution to Abraham and Bonnet of the formula $o(P\cdot Q)=o(P)\cdot o(Q)$ was given in arxiv.org/abs/1711.00428 in MATH/LO

As interactions between humans and AI become more prevalent, it is critical to have better predictors of human behavior in these interactions. We investigated how changes in the AI's adaptive algorithm impact behavior predictions in two-player continuous games. In our experiments, the AI adapted its actions using a gradient descent algorithm under different adaptation rates while human participants were provided cost feedback. The cost feedback was provided by one of two types of visual displays: (a) cost at the current joint action vector, or (b) cost in a local neighborhood of the current joint action vector. Our results demonstrate that AI adaptation rate can significantly affect human behavior, having the ability to shift the outcome between two game theoretic equilibrium. We observed that slow adaptation rates shift the outcome towards the Nash equilibrium, while fast rates shift the outcome towards the human-led Stackelberg equilibrium. The addition of localized cost information had the effect of shifting outcomes towards Nash, compared to the outcomes from cost information at only the current joint action vector. Future work will investigate other effects that influence the convergence of gradient descent games.

Large language models (LLMs) can be used to analyze cyber threat intelligence (CTI) data from cybercrime forums, which contain extensive information and key discussions about emerging cyber threats. However, to date, the level of accuracy and efficiency of LLMs for such critical tasks has yet to be thoroughly evaluated. Hence, this study assesses the performance of an LLM system built on the OpenAI GPT-3.5-turbo model [8] to extract CTI information. To do so, a random sample of more than 700 daily conversations from three cybercrime forums - XSS, Exploit_in, and RAMP - was extracted, and the LLM system was instructed to summarize the conversations and predict 10 key CTI variables, such as whether a large organization and/or a critical infrastructure is being targeted, with only simple human-language instructions. Then, two coders reviewed each conversation and evaluated whether the information extracted by the LLM was accurate. The LLM system performed well, with an average accuracy score of 96.23%, an average precision of 90% and an average recall of 88.2%. Various ways to enhance the model were uncovered, such as the need to help the LLM distinguish between stories and past events, as well as being careful with verb tenses in prompts. Nevertheless, the results of this study highlight the relevance of using LLMs for cyber threat intelligence.

Hydrogels are particularly versatile materials that are widely found in both Nature and industry. One key reason for this versatility is their high water content, which lets them dramatically change their volume and many of their mechanical properties -- often by orders of magnitude -- as they swell and dry out. Currently, we lack techniques that can precisely characterize how these properties change with water content. To overcome this challenge, here we develop Gel-Freezing Osmometry (GelFrO): an extension of freezing-point osmometry. We show how GelFrO can measure a hydrogel's mechanical response to compression and osmotic pressure, while only using small, $O(100\mu$L$)$ samples. Because the technique allows measurement of properties over an unusually wide range of water contents, it allows us to accurately test theoretical predictions. We find simple, power-law behavior for both mechanical response to compression, and osmotic pressure, while these are not well-captured by classical Flory-Huggins theory. We interpret this power-law behavior as a hallmark of a microscopic fractal structure of the gel's polymer network, and propose a simple way to connect the gel's fractal dimension to its mechanical and osmotic properties. This connection is supported by observations of hydrogel microstructures using small-angle x-ray scattering. Finally, our results motivate us to propose an updated constitutive model describing hydrogel swelling, and mechanical response.

Over the past century, the Turkish language has undergone substantial changes, primarily driven by governmental interventions. In this work, our goal is to investigate the evolution of the Turkish language since the establishment of T\"urkiye in 1923. Thus, we first introduce Turkronicles which is a diachronic corpus for Turkish derived from the Official Gazette of T\"urkiye. Turkronicles contains 45,375 documents, detailing governmental actions, making it a pivotal resource for analyzing the linguistic evolution influenced by the state policies. In addition, we expand an existing diachronic Turkish corpus which consists of the records of the Grand National Assembly of T\"urkiye by covering additional years. Next, combining these two diachronic corpora, we seek answers for two main research questions: How have the Turkish vocabulary and the writing conventions changed since the 1920s? Our analysis reveals that the vocabularies of two different time periods diverge more as the time between them increases, and newly coined Turkish words take the place of their old counterparts. We also observe changes in writing conventions. In particular, the use of circumflex noticeably decreases and words ending with the letters "-b" and "-d" are successively replaced with "-p" and "-t" letters, respectively. Overall, this study quantitatively highlights the dramatic changes in Turkish from various aspects of the language in a diachronic perspective.

Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making facilitated by localization solutions form the foundation for location-based services, enabling them to support critical functions within diverse IoT ecosystems. However, most existing works on localization focus on single environment, resulting in the development of multiple models to support multiple environments. In the context of smart cities, these raise costs and complexity due to the dynamicity of such environments. To address these challenges, this paper presents a unified indoor-outdoor localization solution that leverages transfer learning (TL) schemes to build a single deep learning model. The model accurately predicts the localization of IoT devices in diverse environments. The performance evaluation shows that by adopting an encoder-based TL scheme, we can improve the baseline model by about 17.18% in indoor environments and 9.79% in outdoor environments., Comment: 6 pages, 8 figures, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2024)

We examine a unique auction format used in the Istanbul flower market, which could transform into either Dutch or English auction depending on bidders' bidding behaviors. By introducing a time cost that reduces the value of a perishable good as time passes, we explore how this hybrid auction format accommodates the desire for speed via an adaptive starting price. We show that the Istanbul Flower Auction outperforms both the Dutch and English auctions in terms of the auctioneer's utility. With numerical analysis, we also illustrate the Istanbul Flower Auction's superiority in terms of social welfare and auction duration. Our results highlight the critical role of auction design in improving welfare when the duration of the auction process plays a role., Comment: 24 pages, 3 figures, working paper

To realise the goals of active matter at the micro- and nano-scale, the next generation of microrobots must be capable of autonomously sensing and responding to their environment to carry out pre-programmed tasks. Memory effects are proposed to have a significant effect on the dynamics of responsive robotic systems, drawing parallels to strategies used in nature across all length-scales. Inspired by the integral feedback control mechanism by which E.coli are proposed to sense their environment, we develop a numerical responsive active Brownian particle (rABP) model in which the rABPs continuously react to changes in the physical parameters dictated by their local environment. The resulting generated time series are then used to classify and characterise their response, leading to the identification of conditional heteroscedasticity in their physics. We then train recurrent neural networks (RNNs) capable of quantitatively describing the responsiveness of rABPs using their 2-D trajectories. We believe that our proposed strategy to determine the parameters governing the dynamics of rABPs can be applied to guide the design of microrobots with physical intelligence encoded during their fabrication., Comment: 13 pages, 6 figures

Particle surface chemistry and internal softness are two fundamental parameters in governing the mechanical properties of dense colloidal suspensions, dictating structure and flow, therefore of interest from materials fabrication to processing. Here, we modulate softness by tuning the crosslinker content of poly(N-isopropylacrylamide) microgels, and we adjust their surface properties by co-polymerization with polyethylene glycol (PEG) chains, controlling adhesion, friction and fuzziness. We investigate the distinct effects of these parameters on the entire mechanical response from restructuring to complete fluidization of jammed samples at varying packing fractions under large-amplitude oscillatory shear experiments, and we complement rheological data with colloidal-probe atomic force microscopy to unravel variations in the particles' surface properties. We find that surface properties play a fundamental role at smaller packings; decreasing adhesion and friction at contact causes the samples to yield and fluidify in a lower deformation range. Instead, increasing softness or fuzziness has a similar effect at ultra-high densities, making suspensions able to better adapt to the applied shear and reach complete fluidization over a larger deformation range. These findings shed new light on the single-particle parameters governing the mechanical response of dense suspensions subjected to deformation, offering synthetic approaches to design materials with tailored mechanical properties.

The complexity of a well-quasi-order (wqo) can be measured through three ordinal invariants: the width as a measure of antichains, height as a measure of chains, and maximal order type as a measure of bad sequences. We study these ordinal invariants for the finitary powerset, i.e., the collection Pf(A) of finite subsets of a wqo A ordered with the Hoare embedding relation. We show that the invariants of Pf(A) cannot be expressed as a function of the invariants of A, and provide tight upper and lower bounds for them. We then focus on a family of well-behaved wqos, for which these invariants can be computed compositionally, using a newly defined ordinal invariant called the approximate maximal order type. This family is built from multiplicatively indecomposable ordinals, using classical operations such as disjoint unions, products, finite words, finite multisets, and the finitary powerset construction., Comment: 27 pages

Half-metal ferromagnets were predicted [in IEEE Trans. Mag. 51, 1 (2015)] to give large thermoelectric performance in anti-parallel spin valve configuration. Despite being metals that suffer from the Wiedemann-Franz law, the additional spin degrees of freedom allow for tuning of the thermoelectric properties due to the spin-valve enhancement factor (SVEF). We test this theory and find a mismatch of parameters that gives large TE performance and large SVEF. As a result, we show that the spin-valve setup is useful only for gapless HMF with initially poor TE performance. To obtain the largest TE performance, one still needs to open the band gap., Comment: 9 pages

The genome sequence contains the blueprint for governing cellular processes. While the availability of genomes has vastly increased over the last decades, experimental annotation of the various functional, non-coding and regulatory elements encoded in the DNA sequence remains both expensive and challenging. This has sparked interest in unsupervised language modeling of genomic DNA, a paradigm that has seen great success for protein sequence data. Although various DNA language models have been proposed, evaluation tasks often differ between individual works, and might not fully recapitulate the fundamental challenges of genome annotation, including the length, scale and sparsity of the data. In this study, we introduce BEND, a Benchmark for DNA language models, featuring a collection of realistic and biologically meaningful downstream tasks defined on the human genome. We find that embeddings from current DNA LMs can approach performance of expert methods on some tasks, but only capture limited information about long-range features. BEND is available at https://github.com/frederikkemarin/BEND., Comment: 9 pages, 1 figure, 3 tables, code available at https://github.com/frederikkemarin/BEND, to be published in ICLR 2024

Hausa is a major Chadic language, spoken by over 100 million people in Africa. However, from a computational linguistic perspective, it is considered a low-resource language, with limited resources to support Natural Language Processing (NLP) tasks. Online platforms often facilitate social interactions that can lead to the use of offensive and threatening language, which can go undetected due to the lack of detection systems designed for Hausa. This study aimed to address this issue by (1) conducting two user studies (n=308) to investigate cyberbullying-related issues, (2) collecting and annotating the first set of offensive and threatening datasets to support relevant downstream tasks in Hausa, (3) developing a detection system to flag offensive and threatening content, and (4) evaluating the detection system and the efficacy of the Google-based translation engine in detecting offensive and threatening terms in Hausa. We found that offensive and threatening content is quite common, particularly when discussing religion and politics. Our detection system was able to detect more than 70% of offensive and threatening content, although many of these were mistranslated by Google's translation engine. We attribute this to the subtle relationship between offensive and threatening content and idiomatic expressions in the Hausa language. We recommend that diverse stakeholders participate in understanding local conventions and demographics in order to develop a more effective detection system. These insights are essential for implementing targeted moderation strategies to create a safe and inclusive online environment., Comment: 25 pages, 5 figures, 8 tables

The generation of fluid flows by autophoretic microswimmers has been proposed as a mechanism to enhance mass transport and mixing at the micro- and nanoscale. Here, we experimentally investigate the ability of model 2-D "active baths" of photocatalytic silica-titania Janus microspheres to enhance the diffusivity of tracer particles at different microswimmer densities below the onset of collective behaviour. Inspired by the similarities between our experimental findings and previous results for biological microorganisms, we then model our Janus microswimmers using a general squirmer framework, specifically treating them as neutral squirmers. The numerical simulations faithfully capture our observations, offer an insight into the microscopic mechanism underpinning tracer transport, and allow us to expand the parameter space beyond our experimental system. We find strong evidence that near-field interactions dominate enhancements in tracer diffusivity in active Janus baths, leading to the identification of an operating window for enhanced tracer transport by chemical microswimmers based on scaling arguments. Based on this argumentation, we suggest that for many chemically active colloidal systems, hydrodynamics alone is likely to be insufficient to induce appreciable mixing of passive components with large diffusion coefficients., Comment: 18 pages, 12 Figures

This paper introduces the target sum function along with its characteristics. The target sum function takes a list of integers and a specific target integer as input values and expresses the number of ways to obtain the target sum by either adding or subtracting all of the integers from the given list. This function is rooted in the target sum problem which is explored in the fields of computer science and combinatorics. This paper, following the establishment of the generalized Werner's formula, will propose a link between the target sum function and the definite integral of the product of sine and cosine functions based on the formula., Comment: 5 pages

Spatial confounding is a fundamental issue in spatial regression models which arises because spatial random effects, included to approximate unmeasured spatial variation, are typically not independent of covariates in the model. This can lead to significant bias in covariate effect estimates. The problem is complex and has been the topic of extensive research with sometimes puzzling and seemingly contradictory results. Here, we develop a broad theoretical framework that brings mathematical clarity to the mechanisms of spatial confounding, providing explicit analytical expressions for the resulting bias. We see that the problem is directly linked to spatial smoothing and identify exactly how the size and occurrence of bias relate to the features of the spatial model as well as the underlying confounding scenario. Using our results, we can explain subtle and counter-intuitive behaviours. Finally, we propose a general approach for dealing with spatial confounding bias in practice, applicable for any spatial model specification. When a covariate has non-spatial information, we show that a general form of the so-called spatial+ method can be used to eliminate bias. When no such information is present, the situation is more challenging but, under the assumption of unconfounded high frequencies, we develop a procedure in which multiple capped versions of spatial+ are applied to assess the bias in this case. We illustrate our approach with an application to air temperature in Germany.

Spatial confounding is a common issue in spatial regression models, occurring when spatially indexed covariates that model the mean of the response are correlated with a spatial effect included in the model. This dependence, particularly at high spatial frequencies combined with smoothing, can introduce bias in the regression coefficient estimates. The spatial+ framework is a widely used two-stage frequentist approach to mitigate spatial confounding by explicitly modeling and removing the spatial structure in the confounding covariate, replacing it with residuals in the second-stage model for the response. However, frequentist spatial+ does not propagate uncertainty from the first-stage estimation to the second stage, and inference can be cumbersome in a frequentist setting. In contrast, a Bayesian joint modeling framework inherently propagates uncertainty between stages and allows for direct inference on the model parameters. Despite its advantages, the original spatial+ method does not ensure the residuals and spatial effects in the second-stage model are free of shared high spatial frequencies without additional assumptions. To address this, we propose a novel joint prior for the smoothness parameters of the spatial effects that mitigates this issue while preserving the predictive power of the model. We demonstrate the efficacy of our approach through simulation studies and real-world applications.

The underlying mechanisms and physics of catalytic Janus microswimmers is highly complex, requiring details of the associated phoretic fields and the physiochemical properties of catalyst, particle, boundaries, and the fuel used. Therefore, developing a minimal (and more general) model capable of capturing the overall dynamics of these autonomous particles is highly desirable. In the presented work, we demonstrate that a coarse-grained dissipative particle-hydrodynamics model is capable of describing the behaviour of various chemical microswimmer systems. Specifically, we show how a competing balance between hydrodynamic interactions experienced by a squirmer in the presence of a substrate, gravity, and mass and shape asymmetries can reproduce a range of dynamics seen in different experimental systems. We hope that our general model will inspire further synthetic work where various modes of swimmer motion can be encoded via shape and mass during fabrication, helping to realise the still outstanding goal of microswimmers capable of complex 3-D behaviour

Sampling conditional distributions is a fundamental task for Bayesian inference and density estimation. Generative models, such as normalizing flows and generative adversarial networks, characterize conditional distributions by learning a transport map that pushes forward a simple reference (e.g., a standard Gaussian) to a target distribution. While these approaches successfully describe many non-Gaussian problems, their performance is often limited by parametric bias and the reliability of gradient-based (adversarial) optimizers to learn these transformations. This work proposes a non-parametric generative model that iteratively maps reference samples to the target. The model uses block-triangular transport maps, whose components are shown to characterize conditionals of the target distribution. These maps arise from solving an optimal transport problem with a weighted $L^2$ cost function, thereby extending the data-driven approach in [Trigila and Tabak, 2016] for conditional sampling. The proposed approach is demonstrated on a two dimensional example and on a parameter inference problem involving nonlinear ODEs., Comment: 18 pages, 5 figures

Monolayers of colloidal particles at oil-water interfaces readily crystalize owing to electrostatic repulsion, which is often mediated through the oil. However, little attempts exist to control it using oil-soluble electrolytes. We probe the interactions amongst charged hydrophobic micospheres confined at a water/hexadecane interface and show that repulsion can be continuously tuned over orders of magnitude upon introducing minor amounts of an organic salt into the oil. Our results show that charged groups at the particle/oil interface are subject to an associative discharging mechanism, analogous to the charge regulation kinetics observed for charged colloids in non-polar solvents.

Implementation of a sustainable security architecture has been quite a challenging task with several technology deployed to achieve the feat. Automatic IDentification (Auto-ID) procedures exist to provide information about people, animals, goods and products in transit and found several applications in purchasing and distribution logistics, industries, manufacturing companies and material flow systems. This work focuses on the development and implementation of an access control system using Radio Frequency Identification (RFID) technology to enhance a sustainable security architecture. The system controls access into a restricted area by granting access only to authorized persons, which incorporates the RFID hardware (RFID tags and readers and their antennas) and the software. The antenna are to be configured for a read range of about 1.5 m and TMBE kit reader module was used to test the RFID tags. The encoding and decoding process for the reading and writing to the tag as well as interfacing of the hardware and software was achieved through the use of a FissaiD RFID Reader Writer. The software that controls the whole system was designed using in Java Language. The database required for saving the necessary information, staff/guest was designed using appropriate DataBase Management System (DBMS). The system designed and implemented provide records of all accesses (check-in and check-out) made into the restricted area with time records. Other than this system, Model based modeling through the MATLAB/Simulink, Arduino platform, etc. can be used for similar implementation., Comment: Presented at the 19th Annual National Conference of the School of Business Studies, Federal Polytechnic, Ede, 11 pages, 10 figures

Path independence is arguably one of the most important choice rule properties in economic theory. We show that a choice rule is path independent if and only if it is rationalizable by a utility function satisfying ordinal concavity, a concept closely related to concavity notions in discrete mathematics. We also provide a rationalization result for choice rules that satisfy path independence and the law of aggregate demand.

Well-partial orders, and the ordinal invariants used to measure them, are relevant in set theory, program verification, proof theory and many other areas of computer science and mathematics. In this article we focus on one of the most common data structure in programming, the finite multiset of some wpo. There are two natural orders one can define on the set of finite multisets $M(X)$ of a partial order $X$: the multiset embedding and the multiset ordering, for which $M(X)$ remains a wpo when $X$ is. Though the maximal order type of these orders is already known, the other ordinal invariants remain mostly unknown. Our main contributions are expressions to compute compositionally the width of the multiset embedding and the height of the multiset ordering. Furthermore, we provide a new ordinal invariant useful for characterizing the width of the multiset ordering.

Using the @NGRPresident Twitter handle, the Government of Nigeria issued a special directive banning Special Anti-Robbery Squad (SARS) with immediate effect. The SARS is a special police unit under the Nigeria Police Force tasked with the responsibility of fighting violent crimes. However, the unit has been accused of waves of human rights abuse across the nation. According to a report by Amnesty International, between January 2017 and May 2020, 82 cases of police brutality have been committed. This has led to one of the major protests demanding more measures to be taken. The #EndSARS hashtag was widely used by the protesters to amplify their messages and reach out to wider communities on Twitter. In this study, we present a critical analysis of how the online protest unfolded. Essentially, we examine how the protest evolves on Twitter, the nature of engagement with the protest themes, the factors influencing the protest and public perceptions about the online movement. We found that the mobilisation strategies include direct and indirect engagements with influential users, sharing direct stories and vicarious experiences. Also, there is evidence that suggests the deployment of automated accounts to promote the course of the protest. In terms of participation, over 70% of the protest is confined within a few states in Nigeria, and the diaspora communities also lent their voices to the movement. The most active users are not those with high followership, and the majority of the protesters utilised mobile devices, accounting for 88% to mobilise and report on the protest. We also examined how social media users interact with the movement and the response from the wider online communities. Needless to say, the themes in the online discourse are mostly about #EndSARS and vicarious experiences with the police, however, there are topics around police reform and demand for regime change., Comment: 17 pages, 11 figures, 2 tables

Artificial Intelligence (AI) is at the forefront of modern technology, and its effects are felt in many areas of society. To prevent algorithmic disparities, fairness, accountability, transparency, and ethics (FATE) in AI are being implemented. However, the current discourse on these issues is largely dominated by more economically developed countries (MEDC), leaving out local knowledge, cultural pluralism, and global fairness. This study aims to address this gap by examining FATE-related desiderata, particularly transparency and ethics, in areas of the global South that are underserved by AI. A user study (n=43) and a participatory session (n=30) were conducted to achieve this goal. The results showed that AI models can encode bias and amplify stereotypes. To promote inclusivity, a community-led strategy is proposed to collect and curate representative data for responsible AI design. This will enable the affected community or individuals to monitor the increasing use of AI-powered systems. Additionally, recommendations based on public input are provided to ensure that AI adheres to social values and context-specific FATE needs., Comment: 24 pages, 6 figures, 7 Tables

We provide optimal solutions to an institution that has dual goals of diversity and meritocracy when choosing from a set of applications. For example, in college admissions, administrators may want to admit a diverse class in addition to choosing students with the highest qualifications. We provide a class of choice rules that maximize merit subject to attaining a diversity level. Using this class, we find all subsets of applications on the diversity-merit Pareto frontier. In addition, we provide two novel characterizations of matroids.

Given an initial matching and a policy objective on the distribution of agent types to institutions, we study the existence of a mechanism that weakly improves the distributional objective and satisfies constrained efficiency, individual rationality, and strategy-proofness. We show that such a mechanism need not exist in general. We introduce a new notion of discrete concavity, which we call pseudo M$^{\natural}$-concavity, and construct a mechanism with the desirable properties when the distributional objective satisfies this notion. We provide several practically relevant distributional objectives that are pseudo M$^{\natural}$-concave.

Basaltic rocks occur widely on the terrestrial planets and differentiated asteroids, including the asteroid 4 Vesta. We conducted a shock recovery experiment with decaying compressive pulses on a terrestrial basalt at Chiba Institute of Technology, Japan. The sample recorded a range of pressures, and shock physics modeling was conducted to add a pressure scale to the observed shock features. The shocked sample was examined by optical and electron microscopy, electron back-scattered diffractometry, and Raman spectroscopy. We found that localized melting occurs at a lower pressure (~10 GPa) than previously thought (>20 GPa). The shocked basalt near the epicenter represents shock degree C of a recently proposed classification scheme for basaltic eucrites and, as such, our results provide a pressure scale for the classification scheme. Finally, we estimated the total fraction of the basaltic eucrites classified as shock degree C to be ~15% by assuming the impact velocity distribution onto Vesta., Comment: 17 pages, 4 figures, 1 table, 1 supplementary Information, accepted for publication in Geophysical Research Letters

Anisotropic colloidal particles exhibit complex dynamics which play a crucial role in their functionality, transport and phase behaviour. In this work, we investigate the two-dimensional diffusion of smoothly curved colloidal rods -- also known as colloidal bananas -- as a function of their opening angle, {\alpha}. We measure the translational and rotational diffusion coefficients of the particles with opening angles ranging from 0{\deg} (straight rods) to nearly 360{\deg}(closed rings). In particular, we find that the anisotropic diffusion of the particles varies non-monotonically with their opening angle and that the axis of fastest diffusion switches from the long to the short axis of the particles when {\alpha}>180{\deg}. We also find that the rotational diffusion coefficient of nearly closed rings is approximately an order of magnitude higher than that of straight rods of the same length. Finally, we show that the experimental results are consistent with Slender Body Theory, indicating that the dynamical behavior of the particles arises primarily from their local drag anisotropy. These results highlight the impact of curvature on the Brownian Motion of elongated colloidal particles, which must be taken into account when seeking to understand the behaviour of curved colloidal particles., Comment: 5 pages (including references) and 4 figures

Prosody plays a vital role in verbal communication. Acoustic cues of prosody have been examined extensively. However, prosodic characteristics are not only perceived auditorily, but also visually based on head and facial movements. The purpose of this report is to present a method for examining audiovisual prosody using virtual reality. We show that animations based on a virtual human provide motion cues similar to those obtained from video recordings of a real talker. The use of virtual reality opens up new avenues for examining multimodal effects of verbal communication. We discuss the method in the framework of examining prosody perception in cochlear implant listeners.

Dental age is one of the most reliable methods to identify an individual's age. By using dental panoramic radiography (DPR) images, physicians and pathologists in forensic sciences try to establish the chronological age of individuals with no valid legal records or registered patients. The current methods in practice demand intensive labor, time, and qualified experts. The development of deep learning algorithms in the field of medical image processing has improved the sensitivity of predicting truth values while reducing the processing speed of imaging time. This study proposed an automated approach to estimate the forensic ages of individuals ranging in age from 8 to 68 using 1,332 DPR images. Initially, experimental analyses were performed with the transfer learning-based models, including InceptionV3, DenseNet201, EfficientNetB4, MobileNetV2, VGG16, and ResNet50V2; and accordingly, the best-performing model, InceptionV3, was modified, and a new neural network model was developed. Reducing the number of the parameters already available in the developed model architecture resulted in a faster and more accurate dental age estimation. The performance metrics of the results attained were as follows: mean absolute error (MAE) was 3.13, root mean square error (RMSE) was 4.77, and correlation coefficient R$^2$ was 87%. It is conceivable to propose the new model as potentially dependable and practical ancillary equipment in forensic sciences and dental medicine., Comment: 18 pages, 10 figures, 3 tables

Isolated, micro-meter sized diamonds are grown by micro-wave plasma chemical vapour deposition technique on Si(001) substrates. Each diamond is uniquely identified by markers milled in the Si substrate by Ga+ focused ion beam. The morphology and micrograin structure analysis indicates that the diamonds are icosahedral or bi-crystals. Icosahedral diamonds have higher (up to $\sigma_\mathrm{h}$ = 2.3 GPa), and wider distribution ($\Delta\sigma_\mathrm{h}$ = 4.47 GPa) of hydrostatic stress built up at the microcrystal grain boundaries, compared to the other crystals. The number and spectral shape of SiV- color centers incorporated in the micro-diamonds is analysed, and estimated by means of temperature dependent photoluminescence measurements, and Montecarlo simulations. The Montecarlo simulations indicate that the number of SiV- color centers is a few thousand per micro-diamond., Comment: 10 pages, 8 figures

Endowing materials with physical intelligence holds the key for a progress leap in robotic systems. In spite of the growing success for macroscopic devices, transferring these concepts to the microscale presents several challenges connected to the lack of suitable fabrication and design techniques, and of internal response schemes that connect the materials' properties to the function of an autonomous unit. Here, we realize self-propelling colloidal clusters which behave as simple finite state machines, i.e. systems built to possess a finite number of internal states connected by reversible transitions and associated to distinct functions. We produce these units via capillary assembly combining hard polystyrene colloids with two different types of thermo-responsive microgels. The clusters, actuated by spatially uniform AC electric fields, adapt their shape and dielectric properties, and consequently their propulsion, via reversible temperature-induced transitions controlled by light. The different transition temperatures for the two microgels enable three distinct dynamical states corresponding to three illumination intensity levels. The sequential reconfiguration of the microgels affects the velocity and shape of the active trajectories according to a pathway defined by tailoring the clusters' geometry during assembly. The demonstration of these simple systems indicates an exciting route to build more complex units with broader reconfiguration schemes and multiple responses towards the realization of autonomous systems with physical intelligence at the colloidal scale.

Additive manufacturing at the micro- and nanoscale has seen a recent upsurge to suit the increasing demand for more elaborate structures. However, the integration and precise placement of multiple distinct materials at small scales remain a challenge. To this end, we combine here the directed capillary assembly of colloidal particles and two-photon direct laser writing (DLW) to realize a new class of multi-material microstructures. We use DLW both to fabricate 3D micro-templates to guide the capillary assembly of soft- and hard colloids, and to link well-defined arrangements of polystyrene or silica particles produced with capillary assembly, a process we term "printing on particles". The printing process is based on automated particle recognition algorithms and enables the user to connect colloids into one- and two-dimensional tailored structures, including particle clusters and lattices of varying symmetry and composition, using commercial photo-resists (IP-L or IP-PDMS). Once printed and developed, the structures can be easily harvested and re-dispersed in water. The flexibility of our method allows the combination of a wide range of materials into complex structures, which we envisage will boost the realization of new systems for a broad range of fields, including microrobotics, micromanipulation and metamaterials., Comment: 11 pages, 5 figures

The active Brownian particle (ABP) model is widely used to describe the dynamics of active matter systems, such as Janus microswimmers. In particular, the analytical expression for an ABP's mean-squared-displacement (MSD) is useful as it provides a means to describe the essential physics of a self-propelled, spherical Brownian particle. However, the truncated or 'short-time' form of the MSD equation is typically fitted, which can lead to significant problems in parameter estimation. Furthermore, heteroscedasticity and the often statistically dependent observations of an ABP's MSD lead to a situation where standard ordinary least squares (OLS) regression will obtain biased estimates and unreliable confidence intervals. Here, we propose to revert to always fitting the full expression of an ABP's MSD at short timescales, using bootstrapping to construct confidence intervals of the fitted parameters. Additionally, after comparison between different fitting strategies, we propose to extract the physical parameters of an ABP using its mean logarithmic squared displacement (MLSD). These steps improve the estimation of an ABP's physical properties, and provide more reliable confidence intervals, which are critical in the context of a growing interest in the interactions of microswimmers with confining boundaries and the influence on their motion., Comment: 20 pages, 5 figures

Advancements in artificial active matter heavily rely on our ability to characterise their motion. Yet, the most widely used tool to analyse the latter is standard wide-field microscopy, which is largely limited to the study of two-dimensional motion. In contrast, real-world applications often require the navigation of complex three-dimensional environments. Here, we present a Machine Learning (ML) approach to track Janus microswimmers in three dimensions, using Z-stacks as labelled training data. We demonstrate several examples of ML algorithms using freely available and well-documented software, and find that an ensemble decision tree-based model (Extremely Randomised Decision Trees) performs the best at tracking the particles over a volume spanning a depth of more than 40 $\mu$m. With this model, we are able to localise Janus particles with a significant optical asymmetry from standard wide-field microscopy images, bypassing the need for specialised equipment and expertise such as that required for digital holographic microscopy. We expect that ML algorithms will become increasingly prevalent by necessity in the study of active matter systems, and encourage experimentalists to take advantage of this powerful tool to address the various challenges within the field., Comment: 40 pages, 10 figures

Dissipative soliton resonance (DSR) is a promising way for high-energy pulse generation typically having a symmetrical square pulse profile. While this method is well known, the impact of third order dispersion (TOD) on DSR is yet to be fully addressed in the literature. In this article, the impact of TOD on DSR is numerically investigated under the frame of the complex cubic-quintic Ginzburg-Landau equation (CQGLE). Our numerical investigations indicate that DSR can stably exist under TOD with nearly the same pulse amplitude, but with a (significantly) different pulse duration. Depending on the value of chromatic dispersion, the pulse duration can be notably longer or shorter due to the presence of TOD. The TOD effect also alters the dependence of pulse duration on the nonlinear gain. Another impact of TOD on DSR is that the DSR exists with an asymmetric pulse profile, leading to steepening of one edge of the DSR pulse, while flattening of the other. Our results indicate that TOD has a critical role for realizing DSR in mode-locked lasers and it should be taken into consideration during design and development of DSR-based lasers.