Miracle-dz/UniFAR-FTUs huggingface dataset:
Shaping entangled photons through thick scattering media using an advanced wave beacon

Entangled photons provide transformative new paths in the fields of communication, sensing, and computing. However, when entangled photons propagate through a complex medium such as a biological tissue or a turbulent atmosphere, their correlations are scrambled. Using wavefront shaping to compensate for the scattering and retrieve the two-photon correlations is challenging due to the low signal-to-noise ratio of the two-photon signal. While previous works partly addressed this challenge by using feedback from a strong classical laser beam that co-propagates with the entangled photons, such methods frequently depend on assumptions about the complex medium, limiting the applicability of quantum wavefront shaping. In this work, we propose and demonstrate a new feedback mechanism that is inspired by Klyshko's advanced wave picture, in which the classical laser beam counter-propagates with one of the entangled photons and co-propagates with the other. The new Klyshko feedback allows compensation of scattering in thick samples and even in situations where each photon propagates through a different scattering medium. Since the advanced wave picture applies whenever optical reciprocity is valid, such Klyshko optimization can be utilized across a wide range of configurations, offering a robust and alignment-free setup. We therefore believe this protocol will open the door for real-world applications of quantum wavefront shaping.
Combating spam in tagging systems: An evaluation

Tagging systems allow users to interactively annotate a pool of shared resources using descriptive strings called tags. Tags are used to guide users to interesting resources and help them build communities that share their expertise and resources. As tagging systems are gaining in popularity, they become more susceptible to tag spam: misleading tags that are generated in order to increase the visibility of some resources or simply to confuse users. Our goal is to understand this problem better. In particular, we are interested in answers to questions such as: How many malicious users can a tagging system tolerate before results significantly degrade? What types of tagging systems are more vulnerable to malicious attacks? What would be the effort and the impact of employing a trusted moderator to find bad postings? Can a system automatically protect itself from spam, for instance, by exploiting user tag patterns? In a quest for answers to these questions, we introduce a framework for modeling tagging systems and user tagging behavior. We also describe a method for ranking documents matching a tag based on taggers' reliability. Using our framework, we study the behavior of existing approaches under malicious attacks and the impact of a moderator and our ranking method.
New directions in single polymer dynamics

Single polymer techniques are a powerful set of molecular-level tools that enable the direct observation of polymer chain dynamics under highly non-equilibrium conditions. In this way, single polymer methods have been used to uncover fundamentally new information regarding the static and dynamic properties of polymeric materials. However, to achieve the full potential of these new methods, single polymer techniques must be further advanced to enable the study of polymers with complex architectures, heterogeneous chemistries, flexible backbones, and intermolecular interactions in entangled solutions, which reaches far beyond the current state-of-the-art. In this article, we explore recent developments in the area of single polymer physics, including single molecule force spectroscopy and fluorescence microscopy, and we further highlight exciting new directions in the field. VC 2013 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys.
Phosphorylated PP2A (tyrosine 307) is associated with Alzheimer neurofibrillary pathology

Down‐regulation of protein phosphatase 2A (PP2A) is thought to play a critical role in tau hyperphosphorylation in Alzheimer's disease (AD). In vitro phosphorylation of PP2A catalytic subunit at Y307 efficiently inactivates PP2A. A specific antibody against phosphorylated (p) PP2A (Y307) (PP2Ac‐Yp307) was used to investigate possible PP2A down‐regulation by known pathophysiological changes associated with AD, such as Aβ accumulation and oestrogen deficiency. Immunohistochemistry and immunofluorescence confocal microscopy showed an aberrant accumulation of PP2Ac‐Yp307 in neurons that bear pretangles or tangles in the susceptible brain regions, such as the entorhinal cortical cortex and the hippocampus. Experimentally, increased PP2Ac‐Yp307 was observed in mouse N2a neuroblastoma cells that stably express the human amyloid precursor protein with Swedish mutation (APPswe) compared with wild‐type, and in the brains of transgenic APPswe/ presenilin (PS1, A246E) mice, which corresponded to the increased tau phosphorylation. Treating N2a cells with Aβ25–35 mimicked the changes of PP2Ac‐Yp307 and tau phosphorylation in N2a APPswe cells. Knockout of oestrogen receptor (ER) α or ERβ gave similar changes of PP2Ac‐Yp307 level and tau phosphorylation in the mouse brain. Taken together, these findings suggest that increased PP2A phosphorylation (Y307) can be mediated by Aβ deposition or oestrogen deficiency in the AD brain, and consequently compromise dephosphorylation of abnormally hyperphosphorylated tau, and lead to neurofibrillary tangle formation.
Erosion rate of lunar soil under a landing rocket, part 1: identifying the rate-limiting physics

Multiple nations are planning activity on the Moon's surface, and to deconflict lunar operations we must understand the sandblasting damage from rocket exhaust blowing soil. Prior research disagreed over the scaling of the erosion rate, which determines the magnitude of the damage. Reduced gravity experiments and two other lines of evidence now indicate that the erosion rate scales with the kinetic energy flux at the bottom of the laminar sublayer of the gas. Because the rocket exhaust is so fast, eroded particles lifted higher in the boundary layer do not impact the surface for kilometers (if at all; some leave the Moon entirely), so there is no saltation in the vicinity of the gas. As a result, there is little transport of gas kinetic energy from higher in the boundary layer down to the surface, so the emission of soil into the gas is a surprisingly low energy process. In low lunar gravity, a dominant source of resistance to this small energy flux turns out to be the cohesive energy density of the lunar soil, which arises primarily from particles in the 0.3 to 3 micron size range. These particles constitute only a tiny fraction of the mass of lunar soil and have been largely ignored in most studies, so they are poorly characterized.
Simulation of tissue atrophy using a topology preserving transformation model

We propose a method to simulate atrophy and other similar volumetric change effects on medical images. Given a desired level of atrophy, we find a dense warping deformation that produces the corresponding levels of volumetric loss on the labeled tissue using an energy minimization strategy. Simulated results on a real brain image indicate that the method generates realistic images of tissue loss. The method does not make assumptions regarding the mechanics of tissue deformation, and provides a framework where a pre-specified pattern of atrophy can readily be simulated. Furthermore, it provides exact correspondences between images prior and posterior to the atrophy that can be used to evaluate provisional image registration and atrophy quantification algorithms.
A Transformer-Based Framework for Payload Malware Detection and Classification

As malicious cyber threats become more sophisticated in breaching computer networks, the need for effective intrusion detection systems (IDSs) becomes crucial. Techniques such as Deep Packet Inspection (DPI) have been introduced to allow IDSs analyze the content of network packets, providing more context for identifying potential threats. IDSs traditionally rely on using anomaly-based and signature-based detection techniques to detect unrecognized and suspicious activity. Deep learning techniques have shown great potential in DPI for IDSs due to their efficiency in learning intricate patterns from the packet content being transmitted through the network. In this paper, we propose a revolutionary DPI algorithm based on transformers adapted for the purpose of detecting malicious traffic with a classifier head. Transformers learn the complex content of sequence data and generalize them well to similar scenarios thanks to their self-attention mechanism. Our proposed method uses the raw payload bytes that represent the packet contents and is deployed as man-in-the-middle. The payload bytes are used to detect malicious packets and classify their types. Experimental results on the UNSW-NB15 and CIC-IOT23 datasets demonstrate that our transformer-based model is effective in distinguishing malicious from benign traffic in the test dataset, attaining an average accuracy of 79\% using binary classification and 72\% on the multi-classification experiment, both using solely payload bytes.
Learning Piecewise Residuals of Control Barrier Functions for Safety of Switching Systems using Multi-Output Gaussian Processes

Control barrier functions (CBFs) have recently been introduced as a systematic tool to ensure safety by establishing set invariance. When combined with a control Lyapunov function (CLF), they form a safety-critical control mechanism. However, the effectiveness of CBFs and CLFs is closely tied to the system model. In practice, model uncertainty can jeopardize safety and stability guarantees and may lead to undesirable performance. In this paper, we develop a safe learning-based control strategy for switching systems in the face of uncertainty. We focus on the case that a nominal model is available for a true underlying switching system. This uncertainty results in piecewise residuals for each switching surface, impacting the CLF and CBF constraints. We introduce a batch multi-output Gaussian process (MOGP) framework to approximate these piecewise residuals, thereby mitigating the adverse effects of uncertainty. A particular structure of the covariance function enables us to convert the MOGP-based chance constraints CLF and CBF into second-order cone constraints, which leads to a convex optimization. We analyze the feasibility of the resulting optimization and provide the necessary and sufficient conditions for feasibility. The effectiveness of the proposed strategy is validated through a simulation of a switching adaptive cruise control system.
Distributed Feedback Optimization of Linear Multi-agent Systems

Feedback optimization is an increasingly popular control paradigm to optimize dynamical systems, accounting for control objectives that concern the system's operation at steady-state. Existing feedback optimization techniques heavily rely on centralized system and controller architectures, and thus suffer from scalability and privacy issues when systems become large-scale. In this paper, we propose and study a distributed architecture for feedback optimization, in which each agent updates its local control state by combining the average of its neighbors with a local negative-gradient step. Under convexity and smoothness assumptions, we establish convergence of the control method to a fixed point. By reinforcing the assumptions to restricted strong convexity of the cost, we show that our algorithm converges linearly to a neighborhood of the optimal point, where the size of the neighborhood depends on the choice of the stepsize. Simulations corroborate the theoretical results.
Learning in PINNs: Phase transition, total diffusion, and generalization

We investigate the learning dynamics of fully-connected neural networks through the lens of gradient signal-to-noise ratio (SNR), examining the behavior of first-order optimizers like Adam in non-convex objectives. By interpreting the drift/diffusion phases in the information bottleneck theory, focusing on gradient homogeneity, we identify a third phase termed ``total diffusion", characterized by equilibrium in the learning rates and homogeneous gradients. This phase is marked by an abrupt SNR increase, uniform residuals across the sample space and the most rapid training convergence. We propose a residual-based re-weighting scheme to accelerate this diffusion in quadratic loss functions, enhancing generalization. We also explore the information compression phenomenon, pinpointing a significant saturation-induced compression of activations at the total diffusion phase, with deeper layers experiencing negligible information loss. Supported by experimental data on physics-informed neural networks (PINNs), which underscore the importance of gradient homogeneity due to their PDE-based sample inter-dependence, our findings suggest that recognizing phase transitions could refine ML optimization strategies for improved generalization.
Attention-aware semantic relevance predicting Chinese sentence reading

In recent years, several influential computational models and metrics have been proposed to predict how humans comprehend and process sentence. One particularly promising approach is contextual semantic similarity. Inspired by the attention algorithm in Transformer and human memory mechanisms, this study proposes an ``attention-aware'' approach for computing contextual semantic relevance. This new approach takes into account the different contributions of contextual parts and the expectation effect, allowing it to incorporate contextual information fully. The attention-aware approach also facilitates the simulation of existing reading models and evaluate them. The resulting ``attention-aware'' metrics of semantic relevance can more accurately predict fixation durations in Chinese reading tasks recorded in an eye-tracking corpus than those calculated by existing approaches. The study's findings further provide strong support for the presence of semantic preview benefits in Chinese naturalistic reading. Furthermore, the attention-aware metrics of semantic relevance, being memory-based, possess high interpretability from both linguistic and cognitive standpoints, making them a valuable computational tool for modeling eye-movements in reading and further gaining insight into the process of language comprehension. Our approach underscores the potential of these metrics to advance our comprehension of how humans understand and process language, ultimately leading to a better understanding of language comprehension and processing.
Regular extreme semisimple Lie algebras

A subalgebra of a semisimple Lie algebra is wide if every simple module of the semisimple Lie algebra remains indecomposable when restricted to the subalgebra. A subalgebra is narrow if the restrictions of all non-trivial simple modules to the subalgebra have proper decompositions. A semisimple Lie algebra is regular extreme if any regular subalgebra of the semisimple Lie algebra is either narrow or wide. Douglas and Repka previously showed that the simple Lie algebras of type $A_n$ are regular extreme. In this article, we show that, in fact, all simple Lie algebras are regular extreme. Finally, we show that no non-simple, semisimple Lie algebra is regular extreme.
Spatial modeling in cell biology at multiple levels

Most modeling and simulation approaches applied in cell biology assume a homogeneous distribution of particles in space, although experimental studies reveal the importance of space to understand the dynamics of cells. There are already numerous spatial approaches focusing on the simulation of cells. Recently, they have been complemented by a set of spatial modeling languages whose operational semantics are tied partly to existing simulation algorithms. These modeling languages allow an explicit description of spatial phenomena, and facilitate analysis of the temporal spatial dynamics of cells by a clear separation between model, semantics, and simulator. With the supported level of abstraction, each of those offers a different perception of the spatial phenomena under study. In this paper, we give an overview of existing modeling formalisms and discuss some ways of combining approaches to tackle the problem the computational costs induced by spatial dynamics.
Outcome of patients with pregnancy during or after breast cancer: a review of the recent literature.

BACKGROUND An increasing number of young women are delaying childbearing; hence, more are diagnosed with breast cancer (bca) before having a family. No clear recommendations are currently available for counselling such a population on the safety of carrying a pregnancy during bca or becoming pregnant after treatment for bca. METHODS Using a Web-based search of PubMed we reviewed the recent literature about bca and pregnancy. Our objective was to report outcomes for patients diagnosed with bca during pregnancy, comparing them with outcomes for non-pregnant women, and to evaluate prognosis in women diagnosed with and treated for bca who subsequently became pregnant. RESULTS "Pregnancy and bca" should be divided into two entities. Pregnancy-associated bca tends to be more aggressive and advanced in stage at diagnosis than bca in control groups; hence, it has a poorer prognosis. With respect to pregnancy after bca, there is, despite the bias in reported studies and meta-analyses, no clear evidence for a different or worse disease outcome in bca patients who become pregnant after treatment compared with those who do not. CONCLUSIONS Pregnancy-associated bca should be treated as aggressively as and according to the standards applicable in nonpregnant women; pregnancy after bca does not jeopardize outcome. The guidelines addressing risks connected to pregnancy and bca lack a high level of evidence for better counselling young women about pregnancy considerations and preventing unnecessary abortions. Ideally, evidence from large prospective randomized trials would set better guidelines, and yet the complexity of such studies limits their feasibility.
Aftershocks as a time independant phenomenon

Sequences of aftershocks following Omori's empirical law are observed after most major earthquakes, as well as in laboratory-scale fault-mimicking experiments. Nevertheless, the origin of this memory effect is still unclear. In this letter, we present an analytical framework for treating labquake and earthquake catalogs on an equal footing. Using this analysis method, we show that when memory is considered to be in deformation and not in time, all data collapse onto a single master curve, showing that the timescale is entirely fixed by the inverse of the strain rate.
Constitutive Modeling of Brain Tissue: Current Perspectives

Modeling the mechanical response of the brain has become increasingly important over the past decades. Although mechanical stimuli to the brain are small under physiological conditions, mechanics plays a significant role under pathological conditions including brain development, brain injury, and brain surgery. Well calibrated and validated constitutive models for brain tissue are essential to accurately simulate these phenomena. A variety of constitutive models have been proposed over the past three decades, but no general consensus on these models exists. Here, we provide a comprehensive and structured overview of state-of-the-art modeling of the brain tissue. We categorize the different features of existing models into time-independent, time-dependent, and history-dependent contributions. To model the time-independent, elastic behavior of the brain tissue, most existing models adopt a hyperelastic approach. To model the time-dependent response, most models either use a convolution integral approach or a multiplicative decomposition of the deformation gradient. We evaluate existing constitutive models by their physical motivation and their practical relevance. Our comparison suggests that the classical Ogden model is a well-suited phenomenological model to characterize the timeindependent behavior of the brain tissue. However, no consensus exists for mechanistic, physics-based models, neither for the time-independent nor for the time-dependent response. We anticipate that this review will provide useful guidelines for selecting the appropriate constitutive model for a specific application and for refining, calibrating, and validating future models that will help us to better understand the mechanical behavior of the human brain. [DOI: 10.1115/1.4032436]
Human airway macrophages are metabolically reprogrammed by IFN-γ resulting in glycolysis dependent functional plasticity

Airway macrophages (AM) are the predominant immune cell in the lung and play a crucial role in preventing infection, making them a target for host directed therapy. Macrophage effector functions are associated with cellular metabolism. A knowledge gap remains in understanding metabolic reprogramming and functional plasticity of distinct human macrophage subpopulations, especially in lung resident AM. We examined tissue-resident AM and monocyte derived macrophages (MDM; as a model of blood derived macrophages) in their resting state and after priming with IFN-γ or IL-4 to model the Th1/Th2 axis in the lung. Human macrophages, regardless of origin, had a strong induction of glycolysis in response to IFN-γ or upon stimulation. IFN-γ significantly enhanced cellular energetics in both AM and MDM by upregulating both glycolysis and oxidative phosphorylation. Upon stimulation, AM do not decrease oxidative phosphorylation unlike MDM which shift to Warburg metabolism. IFN-γ priming promoted cytokine secretion in AM. Blocking glycolysis with 2-deoxyglucose significantly reduced IFN-γ driven cytokine production in AM, indicating that IFN-γ induces functional plasticity in human AM, which is mechanistically mediated by glycolysis. Directly comparing responses between macrophages, AM were more responsive to IFN-γ priming and dependent on glycolysis for cytokine secretion than MDM. Interestingly, TNF production was under the control of glycolysis in AM and not in MDM. MDM exhibited glycolysis-dependent upregulation of HLA-DR, CD40 and CD86, whereas IFN-γ upregulated HLA-DR and CD40 on AM independently of glycolysis. These data indicate that human AM are functionally plastic and respond to IFN-γ in a manner distinct from MDM. These data provide evidence that human AM are a tractable target for inhalable immunomodulatory therapies for respiratory diseases.
Dynamic trees and dynamic point location

This paper describes new methods for maintaining a point-location data structure for a dynamically changing monotone subdivision $\cal S$. The main approach is based on the maintenance of two interlaced spanning trees, one for $\cal S$ and one for the graph-theoretic planar dual of $\cal S$. Queries are answered by using a centroid decomposition of the dual tree to drive searches in the primal tree. These trees are maintained via the link-cut trees structure of Sleator and Tarjan [J. Comput. System Sci., 26 (1983), pp. 362--381], leading to a scheme that achieves vertex insertion/deletion in O(log n) time, insertion/deletion of k-edge monotone chains in O(log n + k) time, and answers queries in O(log2 n) time, with O(n) space, where n is the current size of subdivision $\cal S$. The techniques described also allow for the dual operations expand and contract to be implemented in O(log n) time, leading to an improved method for spatial point location in a 3-dimensional convex subdivision. In addition, the interlaced-tree approach is applied to on-line point location (where one builds $\cal S$ incrementally), improving the query bound to $O(\log n\log\log n)$ time and the update bounds to O(1)amortized time in this case. This appears to be the first on-line method to achieve a polylogarithmic query time and constant update time.