Miracle-dz/UniFAR-FTUs huggingface dataset:
Fourier Law and Diffusive Transport of Conserved Quantities

We review here some recent mathematical results about diffusive transport of energy and other conserved quantities, in particular for systems where the bulk Hamiltonian dynamics is perturbed by conservative stochastic terms. The presence of the stochastic dynamics allows to define the transport coefficients (thermal conductivity) and in some cases to prove the local equilibrium and the linear response argument necessary to obtain the diffusive equations governing the macroscopic evolution of the conserved quantities. Temperature profiles and other conserved quantities profiles in the non-equilibrium stationary states can be then understood from the non-stationary diffusive behaviour. We also review some results and open problems on the two step approach (by weak coupling or kinetic limits) to the heat equation, starting from mechanical models with only energy conserved.
Characterization of a GDP-Fucose Transporter and a Fucosyltransferase Involved in the Fucosylation of Glycoproteins in the Diatom Phaeodactylum tricornutum

Although Phaeodactylum tricornutum is gaining importance in plant molecular farming for the production of high-value molecules such as monoclonal antibodies, little is currently known about key cell metabolism occurring in this diatom such as protein glycosylation. For example, incorporation of fucose residues in the glycans N-linked to protein in P. tricornutum is questionable. Indeed, such epitope has previously been found on N-glycans of endogenous glycoproteins in P. tricornutum. Meanwhile, the potential immunogenicity of the α(1,3)-fucose epitope present on plant-derived biopharmaceuticals is still a matter of debate. In this paper, we have studied molecular actors potentially involved in the fucosylation of the glycoproteins in P. tricornutum. Based on sequence similarities, we have identified a putative P. tricornutum GDP-L-fucose transporter and three fucosyltransferase (FuT) candidates. The putative P. tricornutum GDP-L-fucose transporter coding sequence was expressed in the Chinese Hamster Ovary (CHO)-gmt5 mutant lacking its endogenous GDP-L-fucose transporter activity. We show that the P. tricornutum transporter is able to rescue the fucosylation of proteins in this CHO-gmt5 mutant cell line, thus demonstrating the functional activity of the diatom transporter and its appropriate Golgi localization. In addition, we overexpressed one of the three FuT candidates, namely the FuT54599, in P. tricornutum and investigated its localization within Golgi stacks of the diatom. Our findings show that overexpression of the FuT54599 leads to a significant increase of the α(1,3)-fucosylation of the diatom endogenous glycoproteins.
3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

Learning global features by aggregating information over multiple views has been shown to be effective for 3D shape analysis. For view aggregation in deep learning models, pooling has been applied extensively. However, pooling leads to a loss of the content within views, and the spatial relationship among views, which limits the discriminability of learned features. We propose 3DViewGraph to resolve this issue, which learns 3D global features by more effectively aggregating unordered views with attention. Specifically, unordered views taken around a shape are regarded as view nodes on a view graph. 3DViewGraph first learns a novel latent semantic mapping to project low-level view features into meaningful latent semantic embeddings in a lower dimensional space, which is spanned by latent semantic patterns. Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns. Finally, all spatial pattern correlations are integrated with attention weights learned by a novel attention mechanism. This further increases the discriminability of learned features by highlighting the unordered view nodes with distinctive characteristics and depressing the ones with appearance ambiguity. We show that 3DViewGraph outperforms state-of-the-art methods under three large-scale benchmarks.
Practical Bayesian Optimization with Threshold-Guided Marginal Likelihood Maximization

We propose a practical Bayesian optimization method, of which the surrogate function is Gaussian process regression with threshold-guided marginal likelihood maximization. Because Bayesian optimization consumes much time in finding optimal free parameters of Gaussian process regression, mitigating a time complexity of this step is critical to speed up Bayesian optimization. For this reason, we propose a simple, but straightforward Bayesian optimization method, assuming a reasonable condition, which is observed in many practical examples. Our experimental results confirm that our method is effective to reduce the execution time. All implementations are available in our repository.
Toward Scalable Fully Homomorphic Encryption Through Light Trusted Computing Assistance

It has been a long standing problem to securely outsource computation tasks to an untrusted party with integrity and confidentiality guarantees. While fully homomorphic encryption (FHE) is a promising technique that allows computations performed on the encrypted data, it suffers from a significant slow down to the computation. In this paper we propose a hybrid solution that uses the latest hardware Trusted Execution Environments (TEEs) to assist FHE by moving the bootstrapping step, which is one of the major obstacles in designing practical FHE schemes, to a secured SGX enclave. TEEFHE, the hybrid system we designed, makes it possible for homomorphic computations to be performed on smaller ciphertext and secret key, providing better performance and lower memory consumption. We make an effort to mitigate side channel leakages within SGX by making the memory access patterns totally independent from the secret information. The evaluation shows that TEEFHE effectively improves the software only FHE schemes in terms of both time and space.
Step and activity detection based on the orientation and scale attributes of the SURF algorithm

In recent years, the importance of location-based services and indoor positioning systems increased significantly for both, research and industry. Visual localization systems have the advantage of not depending on dedicated infrastructure and thus they are interesting for navigation within buildings. While there are already approaches which are using pre-recorded databases of reference images to obtain an absolute position for a given query image, suitable applications which are estimating the relative movement of pedestrians out of a first person perspective video are still missing. This paper presents a novel approach for a pedometer as well as for an activity detector using a such a first person perspective video stream of a pedestrian as input data. The system counts the number of steps and furthermore detects current activities of a user. Therefore, we analyze all video input data with the SURF algorithm in order to extract robust feature points. Especially the orientation and scaling properties of this feature points are used for an accurate measurement.
Decline in the frequency and benefits of multiple brooding in great tits as a consequence of a changing environment

For multiple-brooded species, the number of reproductive events per year is a major determinant of an individual's fitness. Where multiple brooding is facultative, its occurrence is likely to change with environmental conditions, and, as a consequence, the current rates of environmental change could have substantial impacts on breeding patterns. Here we examine temporal population-level trends in the proportion of female great tits (Parus major) producing two clutches per year (‘double brooding’) in four long-term study populations in The Netherlands, and show that the proportion of females that double brood has declined in all populations, with the strongest decline taking place in the last 30 years of the study. For one of the populations, for which we have data on caterpillar abundance, we show that the probability that a female produces a second clutch was related to the timing of her first clutch relative to the peak in caterpillar abundance, and that the probability of double brooding declined over the study period. We further show that the number of recruits from the second clutch decreased significantly over the period 1973–2004 in all populations. Our results indicate that adjustment to changing climatic conditions may involve shifts in life-history traits other than simply the timing of breeding.
Use of TaqMan 5′ Nuclease Real-Time PCR for Quantitative Detection of Mycoplasma genitalium DNA in Males with and without Urethritis Who Were Attendees at a Sexually Transmitted Disease Clinic

ABSTRACT Mycoplasma genitalium is a cause of nongonococcal urethritis, particularly in patients not infected with Chlamydia trachomatis. A quantitative 5′ nuclease assay (TaqMan PCR) was developed and validated. The assay detected a fragment of the MgPa adhesin gene by use of a TaqMan MGB (minor groove binder) probe and included an internal processing control to detect PCR inhibition. Urethral swab specimens and first-void urine samples from M. genitalium-positive men were examined, and the M. genitalium DNA load was correlated to symptoms and signs. The assay consistently detected <5 genome copies without cross-reactions with other mycoplasmas. Urine and urethral swab specimens from men with urethritis had higher M. genitalium DNA loads than specimens from men without urethritis. However, a very broad overlap of DNA loads between patients with and without urethritis was observed. Urethral swab specimens from patients with urethral discharge had a significantly higher DNA load than specimens from patients without discharge. This correlation was not found in first-void urine specimens.
SplitNet: Sim2Sim and Task2Task Transfer for Embodied Visual Navigation

We propose SplitNet, a method for decoupling visual perception and policy learning. By incorporating auxiliary tasks and selective learning of portions of the model, we explicitly decompose the learning objectives for visual navigation into perceiving the world and acting on that perception. We show improvements over baseline models on transferring between simulators, an encouraging step towards Sim2Real. Additionally, SplitNet generalizes better to unseen environments from the same simulator and transfers faster and more effectively to novel embodied navigation tasks. Further, given only a small sample from a target domain, SplitNet can match the performance of traditional end-to-end pipelines which receive the entire dataset
Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach

To strengthen data privacy and security, federated learning as an emerging machine learning technique is proposed to enable large-scale nodes, e.g., mobile devices, to distributedly train and globally share models without revealing their local data. This technique can not only significantly improve privacy protection for mobile devices, but also ensure good performance of the trained results collectively. Currently, most the existing studies focus on optimizing federated learning algorithms to improve model training performance. However, incentive mechanisms to motivate the mobile devices to join model training have been largely overlooked. The mobile devices suffer from considerable overhead in terms of computation and communication during the federated model training process. Without well-designed incentive, self-interested mobile devices will be unwilling to join federated learning tasks, which hinders the adoption of federated learning. To bridge this gap, in this paper, we adopt the contract theory to design an effective incentive mechanism for simulating the mobile devices with high-quality (i.e., high-accuracy) data to participate in federated learning. Numerical results demonstrate that the proposed mechanism is efficient for federated learning with improved learning accuracy.
Approximation error method can reduce artifacts due to scalp blood flow in optical brain activation imaging

Abstract. Diffuse optical tomography can image the hemodynamic response to an activation in the human brain by measuring changes in optical absorption of near-infrared light. Since optodes placed on the scalp are used, the measurements are very sensitive to changes in optical attenuation in the scalp, making optical brain activation imaging susceptible to artifacts due to effects of systemic circulation and local circulation of the scalp. We propose to use the Bayesian approximation error approach to reduce these artifacts. The feasibility of the approach is evaluated using simulated brain activations. When a localized cortical activation occurs simultaneously with changes in the scalp blood flow, these changes can mask the cortical activity causing spurious artifacts. We show that the proposed approach is able to recover from these artifacts even when the nominal tissue properties are not well known.
Development of Dyslexia: The Delayed Neural Commitment Framework

It is now evident that explanations of many developmental disorders need to include a network perspective. In earlier work, we proposed that developmental dyslexia (DD) is well-characterized in terms of impaired procedural learning within the language networks, with the cerebellum being the key structure involved. Here, we deepen the analysis to include the child’s developmental process of constructing these networks. The “Delayed Neural Commitment (DNC)” framework proposes that, in addition to slower skill acquisition, dyslexic children take longer to build (and to rebuild) the neural networks that underpin the acquisition of reading. The framework provides an important link backwards in time to the development of executive function networks and the earlier development of networks for language and speech. It is consistent with many theories of dyslexia while providing fruitful suggestions for further research at the genetic, brain, cognitive and behavioral levels of explanation. It also has significant implications for assessment and teaching.
Initial Condition Problem is Intractable in Cosmology

Determining the initial state of the universe is a challenging problem in quantum cosmology and we argue that the issue is intractable if the basic postulates of quantum mechanics are not modified in a nontrivial way. Namely a "standard" quantum theory of gravity is expected to resolve the big-bang singularity either by yielding a regular past eternal evolution or by a smooth finite beginning; in both cases the initial state can in principle be totally arbitrary. We illustrate this point in a minisuperspace, gauge fixed, deparametrized toy model where there is a smooth beginning of the universe provided by the matter Hamiltonian degenerating to the zero operator. This arbitrariness is the source of several debates in the literature, especially in relation to inflation, which can only be solved by a new paradigm involving initial conditions that necessarily alters the usual quantum mechanical treatment, but we argue that this is highly improbable.
Density Spectral Array for Seizure Identification in Critically Ill Children

Purpose: We evaluated the validity and interrater reliability of encephalographer interpretation of color density spectral array EEG for seizure identification was evaluated in critically ill children and explored predictors of accurate seizure identification. Methods: Conventional EEG tracings from 21 consecutive critically ill children were scored for electrographic seizures. Four 2-hour long segments from each subject were converted to 8-channel color density spectral array displays, yielding 84 images. Eight encephalographers received color density spectral array training and circled elements thought to represent seizures. Images were reviewed in random order (Group A) or with information regarding seizure presence in the initial 30 minutes and with subject images in order (Group B). Sensitivity, specificity, and interrater reliability were calculated. Factors associated with color density spectral array seizure identification were assessed. Results: Seizure prevalence was 43% on conventional EEG. Specificity was significantly higher for Group A than Group B (92.3% vs. 78.2%, P < 0.00). Sensitivity was not significantly different between Groups A and B (64.8% vs. 75%, P = 0.22). Interrater reliability was moderate in both groups. Ten percent of images were falsely classified as containing a seizure. Seizure duration ≥2 minutes predicted identification (P < 0.001). Conclusions: Color density spectral array may be a useful screening tool for seizure identification by encephalographers, but it does not identify all seizures and false positives occur.
Multilinear Time Invariant System Theory

In this paper, we provide a system theoretic treatment of a new class of multilinear time invariant (MLTI) systems in which the states, inputs and outputs are tensors, and the system evolution is governed by multilinear operators. The MLTI system representation is based on the Einstein product and even-order paired tensors. There is a particular tensor unfolding which gives rise to an isomorphism from this tensor space to the general linear group, i.e. group of invertible matrices. By leveraging this unfolding operation, one can extend classical linear time invariant (LTI) system notions including stability, reachability and observability to MLTI systems. While the unfolding based formulation is a powerful theoretical construct, the computational advantages of MLTI systems can only be fully realized while working with the tensor form, where hidden patterns/structures (e.g. redundancy/correlations) can be exploited for efficient representations and computations. Along these lines, we establish new results which enable one to express tensor unfolding based stability, reachability and observability criteria in terms of more standard notions of tensor ranks/decompositions. In addition, we develop the generalized CANDECOMP/PARAFAC decomposition and tensor train decomposition based model reduction framework, which can significantly reduce the number of MLTI system parameters. Further, we provide a review of relevant tensor numerical methods to facilitate computations associated with MLTI systems without requiring unfolding. We demonstrate our framework with numerical examples.
Factor Models for High-Dimensional Tensor Time Series

Abstract Large tensor (multi-dimensional array) data routinely appear nowadays in a wide range of applications, due to modern data collection capabilities. Often such observations are taken over time, forming tensor time series. In this article we present a factor model approach to the analysis of high-dimensional dynamic tensor time series and multi-category dynamic transport networks. This article presents two estimation procedures along with their theoretical properties and simulation results. We present two applications to illustrate the model and its interpretations.
Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views

Deep learning has achieved remarkable results in 3D shape analysis by learning global shape features from the pixel-level over multiple views. Previous methods, however, compute low-level features for entire views without considering part-level information. In contrast, we propose a deep neural network, called Parts4Feature, to learn 3D global features from part-level information in multiple views. We introduce a novel definition of generally semantic parts, which Parts4Feature learns to detect in multiple views from different 3D shape segmentation benchmarks. A key idea of our architecture is that it transfers the ability to detect semantically meaningful parts in multiple views to learn 3D global features. Parts4Feature achieves this by combining a local part detection branch and a global feature learning branch with a shared region proposal module. The global feature learning branch aggregates the detected parts in terms of learned part patterns with a novel multi-attention mechanism, while the region proposal module enables locally and globally discriminative information to be promoted by each other. We demonstrate that Parts4Feature outperforms the state-of-the-art under three large-scale 3D shape benchmarks.
The Direct Interaction between 53BP1 and MDC1 Is Required for the Recruitment of 53BP1 to Sites of Damage*

The DNA damage response mediators, 53BP1 and MDC1, play a central role in checkpoint activation and DNA repair. Here we establish that human 53BP1 and MDC1 interact directly through the tandem BRCT domain of MDC1 and residues 1288–1409 of 53BP1. Following induction of DNA double strand breaks the interaction is reduced, probably due to competition between γ-H2AX and 53BP1 for the binding of the tandem BRCT domain of MDC1. Furthermore, the MDC1 binding region of 53BP1 is required for focus formation by 53BP1. During mitosis the interaction between 53BP1 and MDC1 is enhanced. The interaction is augmented in a phospho-dependent manner, and the MDC1 binding region of 53BP1 is phosphorylated in vivo in mitotic cells; therefore, it is probably modulated by cell cycle-regulated kinases. Our results demonstrate that the 53BP1-MDC1 interaction per se is required for the recruitment of 53BP1 to sites of DNA breaks, which is known to be crucial for an efficient activation of the DNA damage response. Moreover, the results presented here suggest that the interaction between 53BP1 and MDC1 plays a role in the regulation of mitosis.
Multicharged black lens

We construct an asymptotically flat, stationary and biaxisymmetric supersymmetric black lens solution in five-dimensional U(1)^3 supergravity. It is shown that the spatial cross section of the horizon is topologically the lens space of L(n,1), and the spacetime is regular on/outside the event horizon. The black lens carries (3n+2) physical quantities, three electric charges, two angular momenta and 3(n-1) magnetic fluxes.
Error Exponent Bounds for the Bee-Identification Problem

Consider the problem of identifying a massive number of bees, uniquely labeled with barcodes, using noisy measurements. We formally introduce this `bee-identification problem', define its error exponent, and derive efficiently computable upper and lower bounds for this exponent. We show that joint decoding of barcodes provides a significantly better exponent compared to separate decoding followed by permutation inference. For low rates, we prove that the lower bound on the bee-identification exponent obtained using typical random codes (TRC) is strictly better than the corresponding bound obtained using a random code ensemble (RCE). Further, as the rate approaches zero, we prove that the upper bound on the bee-identification exponent meets the lower bound obtained using TRC with joint barcode decoding.
Adaptive Attention Span in Transformers

We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time. We show the effectiveness of our approach on the task of character level language modeling, where we achieve state-of-the-art performances on text8 and enwiki8 by using a maximum context of 8k characters.