Miracle-dz/UniFAR-FTUs huggingface dataset:
Damped energy-norm a posteriori error estimates for fully discrete approximations of the wave equation using C2-reconstructions

We derive a posteriori error estimates for the the scalar wave equation discretized in space by continuous finite elements and in time by the explicit leapfrog scheme. Our analysis combines the idea of invoking extra time-regularity for the right-hand side, as previously introduced in the space semi-discrete setting, with a novel, piecewise quartic, globally twice-differentiable time-reconstruction of the fully discrete solution. Our main results show that the proposed estimator is reliable and efficient in a damped energy norm. These properties are illustrated in a series of numerical examples.
Decoherence-free algebras in quantum dynamics

In this Article we analyze the algebraic properties of the asymptotic dynamics of finite-dimensional open quantum systems in the Heisenberg picture. In particular, a natural product (Choi-Effros product) can be defined in the asymptotic regime. Motivated by this structure, we introduce a new space called the Choi-Effros decoherence-free algebra. Interestingly, this space is both a C* -algebra with respect to the composition product, and a B* -algebra with respect to the Choi-Effros product. Moreover, such space admits a direct-sum decomposition revealing a clear relationship with the attractor subspace of the dynamics. In particular, the equality between the attractor subspace and the Choi-Effros decoherence-free algebra is a necessary and sufficient condition for a faithful dynamics. Finally, we show how all the findings do not rely on complete positivity but on the much weaker Schwarz property.
Synaptic Homeostasis and Restructuring across the Sleep-Wake Cycle

Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.
Weak exactness and amalgamated free product of von Neumann algebras

We show that the amalgamated free product of weakly exact von Neumann algebras is weakly exact. This is done by using a universal property of Toeplitz-Pimsner algebras and a locally convex topology on bimodules of von Neumann algebras, which is used to characterize weakly exact von Neumann algebras.
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai

In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectively. Existing sequential recommendation models often fail to capture long-term dependencies or are too complex, complicating the fulfillment of Meituan Waimai's unique business needs. To better model user interests, we consider selecting relevant sub-sequences from users' extensive historical behaviors based on their preferences. In this specific scenario, we've noticed that the contexts in which users interact have a significant impact on their preferences. For this purpose, we introduce a novel method called Context-based Fast Recommendation Strategy to tackle the issue of long sequences. We first identify contexts that share similar user preferences with the target context and then locate the corresponding PoIs based on these identified contexts. This approach eliminates the necessity to select a sub-sequence for every candidate PoI, thereby avoiding high time complexity. Specifically, we implement a prototype-based approach to pinpoint contexts that mirror similar user preferences. To amplify accuracy and interpretability, we employ JS divergence of PoI attributes such as categories and prices as a measure of similarity between contexts. A temporal graph integrating both prototype and context nodes helps incorporate temporal information. We then identify appropriate prototypes considering both target contexts and short-term user preferences. Following this, we utilize contexts aligned with these prototypes to generate a sub-sequence, aimed at predicting CTR and CTCVR scores with target attention. Since its inception in 2023, this strategy has been adopted in Meituan Waimai's display recommender system, leading to a 4.6% surge in CTR and a 4.2% boost in GMV.
A novel fault attack against ECDSA

A novel fault attack against ECDSA is proposed in this work. It allows to retrieve the secret signing key, by means of injecting faults during the computation of the signature primitive. The proposed method relies on faults injected during a multiplication employed to perform the signature recombination at the end of the ECDSA signing algorithm. Exploiting the faulty signatures, it is possible to reduce the size of the group of the discrete logarithm problem warranting the security margin up to a point where it is computationally treatable. The amount of faulty signatures requested to perform the attack is relatively small, ranging from 4 to a few tenths. The key retrieval can be applied to any key length, like those standardised by NIST, including the ones mandated for top secret documents by NSA suite B. The required post processing of the obtained faulty values is practical on a common consumer grade desktop. The procedure does not rely on any particular structure of the employed curve and may easily be extended to the regular DSA based on modular arithmetics.
Liposomes in dermatology today

Liposomes are vesicles consisting of spherical phospholipid bi‐layers with specific properties making them useful for topical application of drugs. Liposome research has expanded considerably over the last 30 years and nowadays, it is possible to construct a wide range of liposomes varying in size, phospholipids composition and surface characteristics to suit the specific application for which they are intended. In dermatology, the topical application of liposomes has proven to be of therapeutic value. Liposomes can be used as carriers for hydrophilic as well as lipophilic therapeutic agents because of their amphipathic character. They may improve stabilization of instable drugs by encapsulating them and serve as penetration enhancers facilitating the transport of compounds that otherwise cannot penetrate the skin. Liposomes help in reducing skin irritation by sustaining the release of drugs and by hydration of the epidermis. They also have the potential to target drugs into the pilosebaceous structures and hence they have an additional advantage for treatment of hair follicle‐associated disorders. Clinical data indicate that 5‐ALA encapsulated in liposomes improves the quality of Fluorescence Diagnosis by ALA‐induced Porphyrins (FD) and optimizes the results of Photodynamic Therapy (PDT).
The effects of graded caloric restriction: XII. Comparison of The effects of graded caloric restriction: XII. Comparison of mouse to human impact on cellular senescence in the colon mouse to human impact on cellular senescence in the colon

Calorie restriction (CR) is an effective strategy to delay the onset and progression of aging phenotypes in a variety of organisms. Several molecular players are involved in the anti-aging effects of CR, but mechanisms of regulation are poorly understood. Cellular senescence — a cellular state of irreversible growth arrest — is considered a basic mechanism of aging. Senescent cells accumulate with age and promote a number of age-related pathologies. Whether environmental conditions such as diet affect the accumulation of cellular senescence with age is still unclear. Here, we show that a number of classical transcriptomic markers of senescent cells are reduced in adult but relatively young mice under CR. Moreover, we demonstrate that such senescence markers are not induced in the colon of middle-age human volunteers under CR in comparison with age-matched volunteers consuming normal Western diets. Our data support the idea that the improvement in health span observed in different organisms under CR might be partly due to a reduction in the number of senescent cells.
Towards Producing Formally Checkable Security Proofs, Automatically

First-order logic models of security for cryptographic protocols, based on variants of the Dolev-Yao model, are now well-established tools. Given that we have checked a given security protocol pi using a given first-order prover, how hard is it to extract a formally checkable proof of it, as required in, e.g., common criteria at evaluation level 7? We demonstrate that this is surprisingly hard: the problem is non-recursive in general. On the practical side, we show how we can extract finite models M from a set S of clauses representing pi, automatically, in two ways. We then define a model-checker testing M |= S, and show how we can instrument it to output a formally checkable proof, e.g., in Coq. This was implemented in the h1 tool suite. Experience on a number of protocols shows that this is practical.
Power-Law Input-Output Transfer Functions Explain the Contrast-Response and Tuning Properties of Neurons in Visual Cortex

We develop a unified model accounting simultaneously for the contrast invariance of the width of the orientation tuning curves (OT) and for the sigmoidal shape of the contrast response function (CRF) of neurons in the primary visual cortex (V1). We determine analytically the conditions for the structure of the afferent LGN and recurrent V1 inputs that lead to these properties for a hypercolumn composed of rate based neurons with a power-law transfer function. We investigate what are the relative contributions of single neuron and network properties in shaping the OT and the CRF. We test these results with numerical simulations of a network of conductance-based model (CBM) neurons and we demonstrate that they are valid and more robust here than in the rate model. The results indicate that because of the acceleration in the transfer function, described here by a power-law, the orientation tuning curves of V1 neurons are more tuned, and their CRF is steeper than those of their inputs. Last, we show that it is possible to account for the diversity in the measured CRFs by introducing heterogeneities either in single neuron properties or in the input to the neurons. We show how correlations among the parameters that characterize the CRF depend on these sources of heterogeneities. Comparison with experimental data suggests that both sources contribute nearly equally to the diversity of CRF shapes observed in V1 neurons.
Apollon modulates chemosensitivity in human esophageal squamous cell carcinoma

Patients with esophageal squamous cell carcinoma (ESCC) are often diagnosed with advanced diseases that respond poorly to chemotherapy. Here we reported that Apollon, a membrane-associated inhibitor of apoptosis protein, was overexpressed in ESCC cell lines and clinical ESCC tissues, and Apollon overexpression clinically correlated with poor response to chemotherapy (P = 0.001), and short overall survival (P = 0.021). Apollon knockdown increased cisplatin/docetaxel-induced apoptosis, mitochondrial dysfunction and cytochrome c release in two ESCC cell lines. Apollon knockdown potentiated cisplatin/docetaxel-induced long-term cell growth inhibition, and enhanced chemosensitivity of ESCC cells to cisplatin/docetaxel in xenograft tumor models. Apollon knockdown also enhanced cisplatin/docetaxel-induced activation of caspase-8 (extrinsic pathway) and caspase-9 (intrinsic pathway) in ESCC cells and xenograft tumor models. Mechanism studies revealed that the effect of Apollon on chemosensitivity is mainly mediated by Smac. Apollon expression strongly and negatively correlated with Smac expression in clinical ESCC tissues (P = 0.001). Apollon targeted Smac for degradation in ESCC cells. The effect of Apollon on chemosensitivity was reversed by Smac knockdown in ESCC cells. Taken together, our data show association of Apollon expression with chemotherapeutic response in ESCC, and provide a strong rationale for combining Apollon antagonism with chemotherapy to treat ESCC.
On the weak and strong field effects in antiscalar background

The triumph of general relativity under the banner"gravity is geometry"began with confirming the crucial effects within the Solar system and proceeded recently to the strong-field shadow effect for the compact object in the center of the Milky Way. Here, we examine some of those phenomena for the Einstein-scalar equations in the antiscalar regime to reveal the difference from vacuum both in weak and strong fields. As a result, we find that for week-field perihelion shift the difference between vacuum and antiscalar cases proves to be observationally imperceptible in practice, even for S-cluster stars with high eccentricities, and even if accumulated over a century. In strong-field case, we reconsider the shadow effect (this time without involving complex-valued scalar field) as the most perspective from an observational viewpoint. Even though the resulting difference is quite appreciable (about 5%), no conclusion can be made until the mass of the central object is known with the accuracy an order of magnitude higher than the currently available.
A Framework and Architecture for Multi-Robot Coordination

In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment, with applications ranging from scouting and reconnaissance, to search and rescue, to manipulation tasks, to cooperative localization and mapping, and formation control. Our software framework allows a modular and hierarchical approach to programming deliberative and reactive behaviors in autonomous operation. Formal definitions for sequential composition, hierarchical composition, and parallel composition allow the bottom-up development of complex software systems. We demonstrate the algorithms and software on an experimental testbed that involves a group of carlike robots, each using a single omnidirectional camera as a sensor without explicit use of odometry.
Proposal of a general framework to categorize continuous predictor variables

The use of discretized variables in the development of prediction models is a common practice, in part because the decision-making process is more natural when it is based on rules created from segmented models. Although this practice is perhaps more common in medicine, it is extensible to any area of knowledge where a predictive model helps in decision-making. Therefore, providing researchers with a useful and valid categorization method could be a relevant issue when developing prediction models. In this paper, we propose a new general methodology that can be applied to categorize a predictor variable in any regression model where the response variable belongs to the exponential family distribution. Furthermore, it can be applied in any multivariate context, allowing to categorize more than one continuous covariate simultaneously. In addition, a computationally very efficient method is proposed to obtain the optimal number of categories, based on a pseudo-BIC proposal. Several simulation studies have been conducted in which the efficiency of the method with respect to both the location and the number of estimated cut-off points is shown. Finally, the categorization proposal has been applied to a real data set of 543 patients with chronic obstructive pulmonary disease from Galdakao Hospital's five outpatient respiratory clinics, who were followed up for 10 years. We applied the proposed methodology to jointly categorize the continuous variables six-minute walking test and forced expiratory volume in one second in a multiple Poisson generalized additive model for the response variable rate of the number of hospital admissions by years of follow-up. The location and number of cut-off points obtained were clinically validated as being in line with the categorizations used in the literature.
Capability, Quality, and Performance of Offshore IS Vendors: A Theoretical Framework and Empirical Investigation

Information systems (IS) offshoring has become a widespread practice and a strategic sourcing choice for many firms. While much has been written by researchers about the factors that lead to successful offshoring arrangements from the client's viewpoint, the vendor's perspective has been largely scarce. The vendor perspective is equally important as offshore IS vendors need to make important decisions in terms of delivering operational and strategic performance and aligning their resources and processes in order to meet or exceed targeted outcomes. In this article, we propose and test a three-level capability–quality–performance (CQP) theoretical framework to understand vendor outcomes and their antecedents. The first level of the framework represents three vendor capabilities: relationship management, contract management, and information technology management. The second level has three mediating variables representing process quality: partnership, service, and deliverable quality. The third level has three dependent variables representing vendor outcomes: operational performance, strategic performance, and satisfaction. The model was tested with 188 vendor firms from India and China, the two most popular destinations for IS offshoring. Results support the CQP framework; vendor capabilities are significant predictors of intermediate quality measures, which in turn affect vendor outcomes. Implications of the study findings to both theory development and IS offshore vendor strategic decision making are discussed.
Optimisation of Gyrokinetic Microstability Using Adjoint Methods

Microinstabilities drive turbulent fluctuations in inhomogeneous, magnetized plasmas. In the context of magnetic confinement fusion devices, this leads to an enhanced transport of particles, momentum, and energy, thereby degrading confinement. In this work, we elaborate on the application of the adjoint method to efficiently determine the variation of linear growth rates for plasma microstabilities concerning a general set of external parameters within the local $\delta \! f$-gyrokinetic model. We then offer numerical verification of this approach. When coupled with gradient-based techniques, this methodology can facilitate the optimization process for the microstability of the confined plasmas across a high-dimensional parameter space. We present a numerical demonstration wherein the ion-temperature gradient (ITG) instability growth rate in a tokamak plasma is minimized with respect to flux surface shaping parameters. The adjoint method approach demonstrates a significant computational speed-up compared to a finite-difference gradient calculation.
Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts.

OBJECTIVE To investigate whether a computer vision system is comparable with humans in detecting early retinal lesions of diabetic retinopathy using color fundus photographs. METHODS A computer system has been developed using image processing and pattern recognition techniques to detect early lesions of diabetic retinopathy (hemorrhages and microaneurysms, hard exudates, and cotton-wool spots). Color fundus photographs obtained from American Indians in Oklahoma were used in developing and testing the system. A set of 369 color fundus slides were used to train the computer system using 3 diagnostic categories: lesions present, questionable, or absent (Y/Q/N). A different set of 428 slides were used to test and evaluate the system, and its diagnostic results were compared with those of 2 human experts-the grader at the University of Wisconsin Fundus Photograph Reading Center (Madison) and a general ophthalmologist. The experiments included comparisons using 3 (Y/Q/N) and 2 diagnostic categories (Y/N) (questionable cases excluded in the latter). RESULTS In the training phase, the agreement rates, sensitivity, and specificity in detecting the 3 lesions between the retinal specialist and the computer system were all above 90%. The kappa statistics were high (0.75-0.97), indicating excellent agreement between the specialist and the computer system. In the testing phase, the results obtained between the computer system and human experts were consistent with those of the training phase, and they were comparable with those between the human experts. CONCLUSIONS The performance of the computer vision system in diagnosing early retinal lesions was comparable with that of human experts. Therefore, this mobile, electronically easily accessible, and noninvasive computer system, could become a mass screening tool and a clinical aid in diagnosing early lesions of diabetic retinopathy.
Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.