Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrotaxis of self-propelling artificial swimmers in microchannels

Ciliated microswimmers and flagellated bacteria alter their swimming trajectories to follow the direction of an applied electric field exhibiting electrotaxis. Both for matters of application and physical modelling, it is instructive to study such behaviour in synthetic swimmers. We show here that under an external electric field, self-propelling active droplets autonomously modify their swimming trajectories in microchannels, even undergoing `U-turns', to exhibit robust electrotaxis. Depending on the relative initial orientations of the microswimmer and the external electric field, the active droplet can also navigate upstream of an external flow following a centre-line motion, instead of the oscillatory upstream trajectory observed in absence of electric field. Using a hydrodynamic theory model, we show that the electrically induced angular velocity and electrophoretic effects, along with the microswimmer motility and its hydrodynamic interactions with the microchannel walls, play crucial roles in dictating the electrotactic trajectories and dynamics. Specifically, the transformation in the trajectories during upstream swimming against an external flow under an electric field can be understood as a reverse Hopf bifurcation for a dynamical system. Our study provides a simple methodology and a systematic understanding of manoeuvring active droplets in microconfinements for micro-robotic applications especially in biotechnology.
Non‐symmetrical sparking may hint “zits” on a pulsar surface

Pulsar electrodynamics could be relevant to the physics of stellar surface, which remains poorly understood for more than half a centenary and is difficult to probe due to the absence of direct and clear observational evidence. Nevertheless, highly sensitive telescopes (e.g., China's Five‐hundred‐meter Aperture Spherical radio Telescope, FAST) may play an essential role to solve the problem since the predicted surface condition would have quite different characteristics in some models of pulsar structure, especially after the establishment of the standard model of particle physics. For instance, small hills (or “zits”) may exist on solid strangeon star surface with rigidity, preferential discharge, that is, gap sparking, may occur around the hills in the polar cap region. In this work, with the 110‐min polarization observation of PSR B095008 targeted by FAST, we report that the gap sparking is significantly non‐symmetrical to the meridian plane on which the rotational and magnetic axes lie. It is then speculated that this asymmetry could be the result of preferential sparking around zits which might rise randomly on pulsar surface. Some polarization features of both single pulses and the mean pulse, as well as the cross‐correlation function of different emission regions, have also been presented.
Stability and Transport of Gyrokinetic Critical Pedestals

A gyrokinetic threshold model for pedestal width-height scaling prediction is applied to multiple devices and to a shaping and aspect-ratio scan giving $\Delta_{\mathrm{ped}} = 0.92 A^{1.04} \kappa^{-1.24} 0.38^{\delta} \beta_{\theta,\mathrm{ped}}^{1.05}$ for pedestal width $\Delta_{\mathrm{ped}}$, aspect-ratio $A$, elongation $\kappa$, triangularity $\delta$, and normalized pedestal height $\beta_{\theta,\mathrm{ped}}$. We also find a width-transport scaling $\Delta_{\mathrm{ped} } = 0.028 \left(q_e/\Gamma_e - 1.7 \right)^{1.5} \sim \eta_e ^{1.5}$ where $q_e$ and $\Gamma_e$ are turbulent electron heat and particle fluxes and $\eta_e = \nabla \ln T_e / \nabla \ln n_e$ for electron temperature $T_e$ and density $n_e$. Pedestals close to those limited by kinetic-ballooning-modes (KBMs) have modified turbulent transport properties compared to strongly driven KBMs. The role of flow shear is studied as a width-height scaling constraint and pedestal saturation mechanism for a standard and wide pedestal discharge.
POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation

Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans for generating attenuation maps (u-map) for PET attenuation correction significantly elevates radiation doses. To address this concern and further mitigate radiation exposure in low-dose PET exams, we propose POUR-Net - an innovative population-prior-aided over-under-representation network that aims for high-quality attenuation map generation from low-dose PET. First, POUR-Net incorporates an over-under-representation network (OUR-Net) to facilitate efficient feature extraction, encompassing both low-resolution abstracted and fine-detail features, for assisting deep generation on the full-resolution level. Second, complementing OUR-Net, a population prior generation machine (PPGM) utilizing a comprehensive CT-derived u-map dataset, provides additional prior information to aid OUR-Net generation. The integration of OUR-Net and PPGM within a cascade framework enables iterative refinement of $\mu$-map generation, resulting in the production of high-quality $\mu$-maps. Experimental results underscore the effectiveness of POUR-Net, showing it as a promising solution for accurate CT-free low-count PET attenuation correction, which also surpasses the performance of previous baseline methods.
A Systematic Approach to Robustness Modelling for Deep Convolutional Neural Networks

Convolutional neural networks have shown to be widely applicable to a large number of fields when large amounts of labelled data are available. The recent trend has been to use models with increasingly larger sets of tunable parameters to increase model accuracy, reduce model loss, or create more adversarially robust models -- goals that are often at odds with one another. In particular, recent theoretical work raises questions about the ability for even larger models to generalize to data outside of the controlled train and test sets. As such, we examine the role of the number of hidden layers in the ResNet model, demonstrated on the MNIST, CIFAR10, CIFAR100 datasets. We test a variety of parameters including the size of the model, the floating point precision, and the noise level of both the training data and the model output. To encapsulate the model's predictive power and computational cost, we provide a method that uses induced failures to model the probability of failure as a function of time and relate that to a novel metric that allows us to quickly determine whether or not the cost of training a model outweighs the cost of attacking it. Using this approach, we are able to approximate the expected failure rate using a small number of specially crafted samples rather than increasingly larger benchmark datasets. We demonstrate the efficacy of this technique on both the MNIST and CIFAR10 datasets using 8-, 16-, 32-, and 64-bit floating-point numbers, various data pre-processing techniques, and several attacks on five configurations of the ResNet model. Then, using empirical measurements, we examine the various trade-offs between cost, robustness, latency, and reliability to find that larger models do not significantly aid in adversarial robustness despite costing significantly more to train.
Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs

Generating sufficient labeled data is a significant hurdle in the efficient execution of deep learning projects, especially in uncharted territories of image segmentation where labeling demands extensive time, unlike classification tasks. Our study confronts this challenge, operating in an environment constrained by limited hardware resources and the lack of extensive datasets or pre-trained models. We introduce the novel use of Inconsistency Masks (IM) to effectively filter uncertainty in image-pseudo-label pairs, substantially elevating segmentation quality beyond traditional semi-supervised learning techniques. By integrating IM with other methods, we demonstrate remarkable binary segmentation performance on the ISIC 2018 dataset, starting with just 10% labeled data. Notably, three of our hybrid models outperform those trained on the fully labeled dataset. Our approach consistently achieves exceptional results across three additional datasets and shows further improvement when combined with other techniques. For comprehensive and robust evaluation, this paper includes an extensive analysis of prevalent semi-supervised learning strategies, all trained under identical starting conditions. The full code is available at: https://github.com/MichaelVorndran/InconsistencyMasks
Computing Derivations on Nilpotent Quadratic Lie Algebras

Every non-solvable and non-semisimple quadratic Lie algebra can be obtained as a double extension of a solvable quadratic Lie algebra. Thanks to a partial classification of nilpotent Lie algebras and this result, we can design different techniques to obtain any quadratic Lie algebra whose (solvable) radical ideal is nilpotent. To achieve this, we propose two alternative methods, both involving the use of quotients. In addition to their mathematical description, both approaches introduced in this paper have been computationally implemented and are publicly available to use for generating these algebras.
Towards Cheaper Inference in Deep Networks with Lower Bit-Width Accumulators

The majority of the research on the quantization of Deep Neural Networks (DNNs) is focused on reducing the precision of tensors visible by high-level frameworks (e.g., weights, activations, and gradients). However, current hardware still relies on high-accuracy core operations. Most significant is the operation of accumulating products. This high-precision accumulation operation is gradually becoming the main computational bottleneck. This is because, so far, the usage of low-precision accumulators led to a significant degradation in performance. In this work, we present a simple method to train and fine-tune high-end DNNs, to allow, for the first time, utilization of cheaper, $12$-bits accumulators, with no significant degradation in accuracy. Lastly, we show that as we decrease the accumulation precision further, using fine-grained gradient approximations can improve the DNN accuracy.
Topologies of Reasoning: Demystifying Chains, Trees, and Graphs of Thoughts

The field of natural language processing (NLP) has witnessed significant progress in recent years, with a notable focus on improving large language models' (LLM) performance through innovative prompting techniques. Among these, prompt engineering coupled with structures has emerged as a promising paradigm, with designs such as Chain-of-Thought, Tree of Thoughts, or Graph of Thoughts, in which the overall LLM reasoning is guided by a structure such as a graph. As illustrated with numerous examples, this paradigm significantly enhances the LLM's capability to solve numerous tasks, ranging from logical or mathematical reasoning to planning or creative writing. To facilitate the understanding of this growing field and pave the way for future developments, we devise a general blueprint for effective and efficient LLM reasoning schemes. For this, we conduct an in-depth analysis of the prompt execution pipeline, clarifying and clearly defining different concepts. We then build the first taxonomy of structure-enhanced LLM reasoning schemes. We focus on identifying fundamental classes of harnessed structures, and we analyze the representations of these structures, algorithms executed with these structures, and many others. We refer to these structures as reasoning topologies, because their representation becomes to a degree spatial, as they are contained within the LLM context. Our study compares existing prompting schemes using the proposed taxonomy, discussing how certain design choices lead to different patterns in performance and cost. We also outline theoretical underpinnings, relationships between prompting and others parts of the LLM ecosystem such as knowledge bases, and the associated research challenges. Our work will help to advance future prompt engineering techniques.
Spatially Resolved Conductivity of Rectangular Interconnects considering Surface Scattering -- Part I: Physical Modeling

Accurate modeling of interconnect conductivity is important for performance evaluation of chips in advanced technologies. Surface scattering in interconnects is usually treated by using Fuchs-Sondheimer (FS) approach. While the FS model offer explicit inclusion of the physical parameters, it lacks spatial dependence of conductivity across the interconnect cross-section. To capture the space-dependency of conductivity, an empirical modeling approach based on"cosh"function has been proposed, but it lacks physical insights. In this work, we present a 2D spatially resolved FS (SRFS) model for rectangular interconnects derived from the Boltzmann transport equations. The proposed SRFS model for surface scattering offers both spatial dependence and explicit relation of conductivity to physical parameters such as mean free path and specularity of electrons and interconnect geometry. We highlight the importance of physics-based spatially resolved conductivity model by showing the differences in the spatial profiles between the proposed physical approach and the previous empirical approach. In Part II of this work, we build upon the SRFS approach to propose a compact model for spatially-resolved conductivity accounting for surface scattering in rectangular interconnects.
Producing Plankton Classifiers that are Robust to Dataset Shift

Modern plankton high-throughput monitoring relies on deep learning classifiers for species recognition in water ecosystems. Despite satisfactory nominal performances, a significant challenge arises from Dataset Shift, which causes performances to drop during deployment. In our study, we integrate the ZooLake dataset with manually-annotated images from 10 independent days of deployment, serving as test cells to benchmark Out-Of-Dataset (OOD) performances. Our analysis reveals instances where classifiers, initially performing well in In-Dataset conditions, encounter notable failures in practical scenarios. For example, a MobileNet with a 92% nominal test accuracy shows a 77% OOD accuracy. We systematically investigate conditions leading to OOD performance drops and propose a preemptive assessment method to identify potential pitfalls when classifying new data, and pinpoint features in OOD images that adversely impact classification. We present a three-step pipeline: (i) identifying OOD degradation compared to nominal test performance, (ii) conducting a diagnostic analysis of degradation causes, and (iii) providing solutions. We find that ensembles of BEiT vision transformers, with targeted augmentations addressing OOD robustness, geometric ensembling, and rotation-based test-time augmentation, constitute the most robust model, which we call BEsT model. It achieves an 83% OOD accuracy, with errors concentrated on container classes. Moreover, it exhibits lower sensitivity to dataset shift, and reproduces well the plankton abundances. Our proposed pipeline is applicable to generic plankton classifiers, contingent on the availability of suitable test cells. By identifying critical shortcomings and offering practical procedures to fortify models against dataset shift, our study contributes to the development of more reliable plankton classification technologies.
Depth-Optimal Addressing of 2D Qubit Array with 1D Controls Based on Exact Binary Matrix Factorization

Reducing control complexity is essential for achieving large-scale quantum computing. However, reducing control knobs may compromise the ability to independently address each qubit. Recent progress in neutral atom-based platforms suggests that rectangular (row-column) addressing may strike a balance between control granularity and flexibility for 2D qubit arrays. This scheme allows addressing qubits on the intersections of a set of rows and columns each time. While quadratically reducing controls, it may necessitate more depth. We formulate the depth-optimal rectangular addressing problem as exact binary matrix factorization, an NP-hard problem also appearing in communication complexity and combinatorial optimization. We introduce a satisfiability modulo theories-based solver for this problem, and a heuristic, row packing, performing close to the optimal solver on various benchmarks. Furthermore, we discuss rectangular addressing in the context of fault-tolerant quantum computing, leveraging a natural two-level structure.
CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion

Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. Firstly, users struggle to craft textual prompts that meticulously detail visual elements of the input image. Secondly, prevalent models, when effecting modifications in specific zones, frequently disrupt the overall artistic style, complicating the attainment of cohesive and aesthetically unified artworks. To surmount these obstacles, we build the innovative unified framework CreativeSynth, which is based on a diffusion model with the ability to coordinate multimodal inputs and multitask in the field of artistic image generation. By integrating multimodal features with customized attention mechanisms, CreativeSynth facilitates the importation of real-world semantic content into the domain of art through inversion and real-time style transfer. This allows for the precise manipulation of image style and content while maintaining the integrity of the original model parameters. Rigorous qualitative and quantitative evaluations underscore that CreativeSynth excels in enhancing artistic images' fidelity and preserves their innate aesthetic essence. By bridging the gap between generative models and artistic finesse, CreativeSynth becomes a custom digital palette.
Effective stick-slip parameter for structurally lubric 2D interface friction

The wear-free sliding of layers or flakes of graphene-like 2D materials, important in many experimental systems, may occur either smoothly or through stick-slip, depending on driving conditions, corrugation, twist angles, as well as edges and defects. No single parameter has been so far identified to discriminate a priori between the two sliding regimes. Such a parameter, $\eta$, does exist in the ideal (Prandtl-Tomlinson) problem of a point particle sliding across a 1D periodic lattice potential. In that case $\eta>1$ implies mechanical instability, generally leading to stick-slip, with $\eta = \frac{2\pi^2 U_0}{K_\mathrm{p} a^2}$, where $U_0$ is the potential magnitude, $a$ the lattice spacing, and $K_\mathrm{p}$ the pulling spring constant. Here we show, supported by a repertoire of graphene flake/graphene sliding simulations, that a similar stick-slip predictor $\eta_\mathrm{eff}$ can be defined with the same form but suitably defined $U_\mathrm{eff}$, $a_\mathrm{eff}$ and $K_\mathrm{eff}$. Remarkably, simulations show that $a_\mathrm{eff} = a$ of the substrate remains an excellent approximation, while $K_\mathrm{eff}$ is an effective stiffness parameter, combining equipment and internal elasticity. Only the effective energy barrier $U_\mathrm{eff}$ needs to be estimated in order to predict whether stick-slip sliding of a 2D island or extended layer is expected or not. In a misaligned defect-free circular graphene sliding island of contact area $A$, we show that $U_\mathrm{eff}$, whose magnitude for a micrometer size diameter is of order 1 eV, scales as $A^{1/4}$, thus increasing very gently with size. The PT-like parameter $\eta_\mathrm{eff}$ is therefore proposed as a valuable tool in 2D layer sliding.
Macrolithic tools for mining and primary processing of metal ores from the site of Grotta della Monaca (Calabria, Italy).

Grotta della Monaca is a karst cave located in Calabria, the southernmost region of the Italian peninsula. About half a kilometre deep, the cavern has drawn the attention of people since the Palaeolithic period due to the abundance of metal ores within (mainly iron hydroxides but also copper carbonates). These easily reachable minerals were exploited during prehistory, especially between the late Neolithic and the early Copper Age. Research conducted from 2000 to 2012 has allowed us to understand various aspects of ancient mining activities performed within the site and to reconstruct a basic operational chain concerning the procurement and the primary processing of such resources, from the choice of tools to the various extractive techniques, from the disposal of mining debris to the early operations of ore processing. In this paper we focus on two categories of macrolithic tools: those used to extract minerals, and those used for their primary processing. The first group is composed of 51 artefacts divided into axes, hammers, and pickaxes with a central groove, found in the deeper areas of the cave, which represent the mining areas. Their morphological and dimensional variability indicates a significant functional diversification; furthermore, the choice of different very hard metamorphic rocks implies a high awareness in the selection of the raw materials used for making these implements. The second group includes 22 tools with different functions - mainly made of sandstone - classified as querns, grinders and crushers. They were found in an underground area adjacent to the entrance, which is characterized by large and comfortable spaces, with the widespread presence of natural light. A recent traceological study has clarified the function of such artefacts; they were used to grind mined blocks of iron hydroxides to obtain a powder. The multidisciplinary approach adopted in studying mining tools from Grotta della Monaca, including petrographic, typological and use-wear analysis, has allowed us to gain important knowledge about the general characteristics of these tools.
(Chat)GPT v BERT: Dawn of Justice for Semantic Change Detection

In the universe of Natural Language Processing, Transformer-based language models like BERT and (Chat)GPT have emerged as lexical superheroes with great power to solve open research problems. In this paper, we specifically focus on the temporal problem of semantic change, and evaluate their ability to solve two diachronic extensions of the Word-in-Context (WiC) task: TempoWiC and HistoWiC. In particular, we investigate the potential of a novel, off-the-shelf technology like ChatGPT (and GPT) 3.5 compared to BERT, which represents a family of models that currently stand as the state-of-the-art for modeling semantic change. Our experiments represent the first attempt to assess the use of (Chat)GPT for studying semantic change. Our results indicate that ChatGPT performs significantly worse than the foundational GPT version. Furthermore, our results demonstrate that (Chat)GPT achieves slightly lower performance than BERT in detecting long-term changes but performs significantly worse in detecting short-term changes.