Miracle-dz/UniFAR-FTUs huggingface dataset:
Middle-Late Eocene marine record of the Biga Peninsula, NW Anatolia, Turkey

The Eocene shallow marine deposits marking the first marine incursion in the Biga Peninsula (NW Turkey) after the collision of the Sakarya and Anatolide-Tauride plates were investigated based on paleontological, litho- and chrono-stratigraphic data. Larger Benthic Foraminifera (LBF) from patchily distributed outcrops were studied in order to i) revise the stratigraphy of Eocene shallow marine units, and ii) establish a modern biostratigraphic setting and a correlation scheme. The Şevketiye Formation (Fm.) is herein defined as a predominantly shallow marine clastic deposit with subordinate carbonates overlying the Camlica metamorphic rocks, and passing laterally to the Sogucak Fm. , a carbonate unit that is widely represented in the Thrace Basin. The record of alveolinids, primitive developmental stages of heterosteginids, and orthophragminids in the Şevketiye Fm. suggests that this formation is part of the Shallow Benthic Zones (SBZ) 16? /17 (Late Lutetian? /Early Bartonian), SBZ17? /18 and SBZ19A (Early Bartonian/earliest Priabonian). The Sogucak Fm. , which overlies the Eocene volcanics, on the other hand, yielded advanced developmental stages of heterosteginids, Spiroclypeus sp. and Nummulites fabianii lineages, implying a younger marine incursion during the Late Eocene (earliest Priabonian; SBZ19A). A drastic shift in the depositional regime is marked amid Priabonian by the deposition of deep-marine clastics and volcanoclastics of the Ceylan Fm. In conclusion, the Eocene Sea transgressed first Gokceada (in the Aegean Sea) during the Late Lutetian, then reached the Biga and Gelibolu peninsulas in the Bartonian, and finally led to the widespread deposition of carbonate and siliciclastic rocks in the Biga Peninsula and the Thrace Basin during the Late Bartonian and Priabonian.
LookUp: Enabling Pedestrian Safety Services via Shoe Sensing

Motivated by safety challenges resulting from distracted pedestrians, this paper presents a sensing technology for fine-grained location classification in an urban environment. It seeks to detect the transitions from sidewalk locations to in-street locations, to enable applications such as alerting texting pedestrians when they step into the street. In this work, we use shoe-mounted inertial sensors for location classification based on surface gradient profile and step patterns. This approach is different from existing shoe sensing solutions that focus on dead reckoning and inertial navigation. The shoe sensors relay inertial sensor measurements to a smartphone, which extracts the step pattern and the inclination of the ground a pedestrian is walking on. This allows detecting transitions such as stepping over a curb or walking down sidewalk ramps that lead into the street. We carried out walking trials in metropolitan environments in United States (Manhattan) and Europe (Turin). The results from these experiments show that we can accurately determine transitions between sidewalk and street locations to identify pedestrian risk.
Open Continual Feature Selection via Granular-Ball Knowledge Transfer

This paper presents a novel framework for continual feature selection (CFS) in data preprocessing, particularly in the context of an open and dynamic environment where unknown classes may emerge. CFS encounters two primary challenges: the discovery of unknown knowledge and the transfer of known knowledge. To this end, the proposed CFS method combines the strengths of continual learning (CL) with granular-ball computing (GBC), which focuses on constructing a granular-ball knowledge base to detect unknown classes and facilitate the transfer of previously learned knowledge for further feature selection. CFS consists of two stages: initial learning and open learning. The former aims to establish an initial knowledge base through multi-granularity representation using granular-balls. The latter utilizes prior granular-ball knowledge to identify unknowns, updates the knowledge base for granular-ball knowledge transfer, reinforces old knowledge, and integrates new knowledge. Subsequently, we devise an optimal feature subset mechanism that incorporates minimal new features into the existing optimal subset, often yielding superior results during each period. Extensive experimental results on public benchmark datasets demonstrate our method's superiority in terms of both effectiveness and efficiency compared to state-of-the-art feature selection methods.
Do Visual-Language Maps Capture Latent Semantics?

Visual-language models (VLMs) have recently been introduced in robotic mapping by using the latent representations, i.e., embeddings, of the VLMs to represent the natural language semantics in the map. The main benefit is moving beyond a small set of human-created labels toward open-vocabulary scene understanding. While there is anecdotal evidence that maps built this way support downstream tasks, such as navigation, rigorous analysis of the quality of the maps using these embeddings is lacking. We investigate two critical properties of map quality: queryability and consistency. The evaluation of queryability addresses the ability to retrieve information from the embeddings. We investigate two aspects of consistency: intra-map consistency and inter-map consistency. Intra-map consistency captures the ability of the embeddings to represent abstract semantic classes, and inter-map consistency captures the generalization properties of the representation. In this paper, we propose a way to analyze the quality of maps created using VLMs, which forms an open-source benchmark to be used when proposing new open-vocabulary map representations. We demonstrate the benchmark by evaluating the maps created by two state-of-the-art methods, VLMaps and OpenScene, using two encoders, LSeg and OpenSeg, using real-world data from the Matterport3D data set. We find that OpenScene outperforms VLMaps with both encoders, and LSeg outperforms OpenSeg with both methods.
Improving the Quality of Automatic DNA Sequence Assembly Using Fluorescent Trace-Data Classifications

Virtually all large-scale sequencing projects use automatic sequence-assembly programs to aid in the determination of DNA sequences. The computer-generated assemblies required substantial hand-editing to transform them into submissions for GenBank. As the size of sequencing projects increases, it becomes essential to improve the quality of the automated assemblies so that this time consuming hand-editing may be reduced. Current ABI sequencing technology uses base calls made from fluorescently-labeled DNA fragments run on gels. We present a new representation for the fluorescent trace data associated with individual base calls. This representation can be used before, during, and after fragment assembly to improve the quality of assemblies. We demonstrate one such use-end-trimming of sub-optimal data-that results in a significant improvement in the quality of subsequent assemblies.
Composite Survival Index to Compare Virulence Changes in Azole-Resistant Aspergillus fumigatus Clinical Isolates

Understanding resistance to antifungal agents in Aspergillus fumigatus is of increasing importance for the treatment of invasive infections in immunocompromised patients. Although a number of molecular resistance mechanisms are described in detail, the potential accompanying virulence changes and impact on clinical outcome have had little attention. We developed a new measure of survival, the composite survival index (CSI) to use as a measure of the virulence properties of A. fumigatus. Using a novel mathematical model we found a strong correlation between the in vitro growth characteristics and virulence in vivo expressed as CSI. Our model elucidates how three critical parameters (the lag phase (τ), decay constant (λ), and growth rate (ν)) interact with each other resulting in a CSI that correlated with virulence. Hence, strains with a long lag phase and high decay constant were less virulent in a murine model of invasive aspergillosis, whereas high virulence for isolates with a high CSI was associated in vitro with rapid growth and short lag phases. Resistant isolates with cyp51A mutations, which account for the majority of azole resistant aspergillosis cases, did not show a lower virulence compared to azole-susceptible isolates. In contrast, the CSI index revealed that a non-cyp51A-mediated resistance mechanism was associated with a dramatic decrease in CSI. Because of its predictive value, the mathematical model developed may serve to explore strain characteristics in vitro to predict virulence in vivo and significantly reduce the number of experimental animals required in such studies. The proposed measure of survival, the CSI can be used more in a general form in survival studies to explore optimal treatment options.
Surveyor: Facilitating Discovery Within Video Games for Blind and Low Vision Players

Video games are increasingly accessible to blind and low vision (BLV) players, yet many aspects remain inaccessible. One aspect is the joy players feel when they explore environments and make new discoveries, which is integral to many games. Sighted players experience discovery by surveying environments and identifying unexplored areas. Current accessibility tools, however, guide BLV players directly to items and places, robbing them of that experience. Thus, a crucial challenge is to develop navigation assistance tools that also foster exploration and discovery. To address this challenge, we propose the concept of exploration assistance in games and design Surveyor, an in-game exploration assistance tool that enhances discovery by tracking where BLV players look and highlighting unexplored areas. We designed Surveyor using insights from a formative study and compared Surveyor's effectiveness to approaches found in existing accessible games. Our findings reveal implications for facilitating richer play experiences for BLV users within games.
A Refined Proximal Algorithm for Nonconvex Multiobjective Optimization in Hilbert Spaces

This paper is devoted to general nonconvex problems of multiobjective optimization in Hilbert spaces. Based on Mordukhovich's limiting subgradients, we define a new notion of Pareto critical points for such problems, establish necessary optimality conditions for them, and then employ these conditions to develop a refined version of the vectorial proximal point algorithm with providing its detailed convergence analysis. The obtained results largely extend those initiated by Bonnel, Iusem and Svaiter \cite{Bonnel2005} for convex vector optimization problems and by Bento et al. \cite{Bento2018} for nonconvex finite-dimensional problems in terms of Clarke's generalized gradients.
Probable Maximum Precipitation in the U.S. Pacific Northwest in a Changing Climate

The safety of large and aging water infrastructures is gaining attention in water management given the accelerated rate of change in landscape, climate, and society. In current engineering practice, such safety is ensured by the design of infrastructure for the Probable Maximum Precipitation (PMP). Recently, several numerical modeling approaches have been proposed to modernize the conventional and ad hoc PMP estimation approach. However, the underlying physics have not been fully investigated and thus differing PMP estimates are sometimes obtained without physics‐based interpretations. In this study, we present a hybrid approach that takes advantage of both traditional engineering practice and modern climate science to estimate PMP for current and future climate conditions. The traditional PMP approach is modified and applied to five statistically downscaled CMIP5 model outputs, producing an ensemble of PMP estimates in the Pacific Northwest (PNW) during the historical (1970–2016) and future (2050–2099) time periods. The hybrid approach produced consistent historical PMP estimates as the traditional estimates. PMP in the PNW will increase by 50% ± 30% of the current design PMP by 2099 under the RCP8.5 scenario. Most of the increase is caused by warming, which mainly affects moisture availability through increased sea surface temperature, with minor contributions from changes in storm efficiency in the future. Moist track change tends to reduce the future PMP. Compared with extreme precipitation, PMP exhibits higher internal variability. Thus, long‐time records of high‐quality data in both precipitation and related meteorological fields (temperature, wind fields) are required to reduce uncertainties in the ensemble PMP estimates.
Towards Non-Adversarial Algorithmic Recourse

The streams of research on adversarial examples and counterfactual explanations have largely been growing independently. This has led to several recent works trying to elucidate their similarities and differences. Most prominently, it has been argued that adversarial examples, as opposed to counterfactual explanations, have a unique characteristic in that they lead to a misclassification compared to the ground truth. However, the computational goals and methodologies employed in existing counterfactual explanation and adversarial example generation methods often lack alignment with this requirement. Using formal definitions of adversarial examples and counterfactual explanations, we introduce non-adversarial algorithmic recourse and outline why in high-stakes situations, it is imperative to obtain counterfactual explanations that do not exhibit adversarial characteristics. We subsequently investigate how different components in the objective functions, e.g., the machine learning model or cost function used to measure distance, determine whether the outcome can be considered an adversarial example or not. Our experiments on common datasets highlight that these design choices are often more critical in deciding whether recourse is non-adversarial than whether recourse or attack algorithms are used. Furthermore, we show that choosing a robust and accurate machine learning model results in less adversarial recourse desired in practice.
Quantization Effects on Neural Networks Perception: How would quantization change the perceptual field of vision models?

Neural network quantization is an essential technique for deploying models on resource-constrained devices. However, its impact on model perceptual fields, particularly regarding class activation maps (CAMs), remains a significant area of investigation. In this study, we explore how quantization alters the spatial recognition ability of the perceptual field of vision models, shedding light on the alignment between CAMs and visual saliency maps across various architectures. Leveraging a dataset of 10,000 images from ImageNet, we rigorously evaluate six diverse foundational CNNs: VGG16, ResNet50, EfficientNet, MobileNet, SqueezeNet, and DenseNet. We uncover nuanced changes in CAMs and their alignment with human visual saliency maps through systematic quantization techniques applied to these models. Our findings reveal the varying sensitivities of different architectures to quantization and underscore its implications for real-world applications in terms of model performance and interpretability. The primary contribution of this work revolves around deepening our understanding of neural network quantization, providing insights crucial for deploying efficient and interpretable models in practical settings.
Spatial organization of a replicating bacterial chromosome

Emerging evidence indicates that the global organization of the bacterial chromosome is defined by its physical map. This architectural understanding has been gained mainly by observing the localization and dynamics of specific chromosomal loci. However, the spatial and temporal organization of the entire mass of newly synthesized DNA remains elusive. To visualize replicated DNA within living cells, we developed an experimental system in the bacterium Bacillus subtilis whereby fluorescently labeled nucleotides are incorporated into the chromosome as it is being replicated. Here, we present the first visualization of replication morphologies exhibited by the bacterial chromosome. At the start of replication, newly synthesized DNA is translocated via a helical structure from midcell toward the poles, where it accumulates. Next, additionally synthesized DNA forms a second, visually distinct helix that interweaves with the original one. In the final stage of replication, the space between the two helices is filled up with the very last synthesized DNA. This striking geometry provides insight into the three-dimensional conformation of the replicating chromosome.
A Web-Based Negotiation Support System

In this paper a Web-based Negotiation Support System CBSS is introduced. The system facilitates a structured on-line negotiation process. It is written in JAVA language and can be accessed easily through the Web. The system has been tested in comparison with face-to-face meetings in stimulated labour union-management negotiations. The test shows that CBSS was viewed as a valid alternative to face-to-face negotiation, although bargaining processes supported by CBSS were perceived to be slower than face-to-face negotiation.
Constrained Passive Interaction Control: Leveraging Passivity and Safety for Robot Manipulators

Passivity is necessary for robots to fluidly collaborate and interact with humans physically. Nevertheless, due to the unconstrained nature of passivity-based impedance control laws, the robot is vulnerable to infeasible and unsafe configurations upon physical perturbations. In this paper, we propose a novel control architecture that allows a torque-controlled robot to guarantee safety constraints such as kinematic limits, self-collisions, external collisions and singularities and is passive only when feasible. This is achieved by constraining a dynamical system based impedance control law with a relaxed hierarchical control barrier function quadratic program subject to multiple concurrent, possibly contradicting, constraints. Joint space constraints are formulated from efficient data-driven self- and external C^2 collision boundary functions. We theoretically prove constraint satisfaction and show that the robot is passive when feasible. Our approach is validated in simulation and real robot experiments on a 7DoF Franka Research 3 manipulator.
Triboelectrification of identical insulators. II. Theory and further experiments

For pt.I see ibid., vol.19, p.1273 (1986). Two samples of nominally identical material will usually charge each other if they are rubbed together asymmetrically, e.g. in such a way that a small region of one rubs across a large region of the other. It has previously been supposed that the asymmetric rubbing induced some physical difference, in particular a temperature difference, between surfaces, which in turn causes charge transfer. However, experiments on polymers (reported in the preceding paper) are not easily reconciled with this explanation. In this paper the authors show that it is not necessary to invoke any physical distinction between the two insulators to explain charge transfer. Certain electron distributions can result in net charge transfer during asymmetric rubbing even though the temperature (and other physical properties) remains exactly the same for the two surfaces. The authors present a quantitative model and they show that it is capable of explaining a wide range of experimental facts about charge transfer between identical insulators.
GreedyML: A Parallel Algorithm for Maximizing Submodular Functions

We describe a parallel approximation algorithm for maximizing monotone submodular functions subject to hereditary constraints on distributed memory multiprocessors. Our work is motivated by the need to solve submodular optimization problems on massive data sets, for practical applications in areas such as data summarization, machine learning, and graph sparsification. Our work builds on the randomized distributed RandGreedI algorithm, proposed by Barbosa, Ene, Nguyen, and Ward (2015). This algorithm computes a distributed solution by randomly partitioning the data among all the processors and then employing a single accumulation step in which all processors send their partial solutions to one processor. However, for large problems, the accumulation step could exceed the memory available on a processor, and the processor which performs the accumulation could become a computational bottleneck. Here, we propose a generalization of the RandGreedI algorithm that employs multiple accumulation steps to reduce the memory required. We analyze the approximation ratio and the time complexity of the algorithm (in the BSP model). We also evaluate the new GreedyML algorithm on three classes of problems, and report results from massive data sets with millions of elements. The results show that the GreedyML algorithm can solve problems where the sequential Greedy and distributed RandGreedI algorithms fail due to memory constraints. For certain computationally intensive problems, the GreedyML algorithm can be faster than the RandGreedI algorithm. The observed approximation quality of the solutions computed by the GreedyML algorithm closely matches those obtained by the RandGreedI algorithm on these problems.
LINEAR REGRESSION

Much of machine learning is about fitting functions to data. That may not sound like an exciting activity that will give us artificial intelligence. However, representing and fitting functions is a core building block of most working machine learning or AI systems. We start with linear functions, both because this idea turns out to be surprisingly powerful, and because it’s a useful starting point for more interesting models and methods.
Lactation deficit in OFA hr/hr rats may be caused by differential sensitivity to stress compared with Wistar and Sprague Dawley rats

OFA hr/hr (OFA) rats present a major lactation deficit that impairs offspring survival. To explore whether abnormal stress responsiveness causes this deficit, we compared their hormonal (prolactin, progesterone, and corticosterone) responses to stress (room change and 2-min ether exposure) with those of Wistar and Sprague Dawley (SD) rats. We tested responses during the estrous cycle, pregnancy, lactation, after ovariectomy, and ovarian steroid hormone priming, and responses to suckling. We evaluated hypothalamic expression of receptors for prolactin (PRLRlong) and the isoforms of receptors for progesterone (PRA and B) and estrogen (ERα and β) in late pregnancy. We tested whether administration of an anxiolytic (diazepam) improved lactation. Ether exposure increased circulating levels of the three hormones in the three strains of rats, cycling and ovariectomized, but was less effective in pregnancy and lactation. Elevated estrogen level (estrus and estradiol-treated ovariectomized rats) potentiated the prolactin response more in SD and OFA rats than in Wistar rats. Elevated progesterone level (late pregnancy, lactation, progesterone-treated ovariectomized rats) inhibited the prolactin response less in OFA than in SD or Wistar rats. Ether exposure inhibited the prolactin and oxytocin responses to suckling only in OFA rats. Diazepam treatment increased pup survival rate and the prolactin response to suckling. Hypothalamic total PR mRNA content, assayed by RT-PCR, was higher in pregnant OFA rats compared with SD and Wistar rats, but the PRB/PRA protein ratio determined by Western blot was lowest in Wistar rats, intermediate in OFA rats, and highest in SD rats. The heightened sensitivity to stress of lactating OFA rats may contribute to their lactational deficit and be caused by a combination of hypoprolactinemia and reduced inhibitory capacity of progesterone.