Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct Handheld Burst Imaging to Simulated Defocus

A shallow depth-of-field image keeps the subject in focus, and the foreground and background contexts blurred. This effect requires much larger lens apertures than those of smartphone cameras. Conventional methods acquire RGB-D images and blur image regions based on their depth. However, this approach is not suitable for reflective or transparent surfaces, or finely detailed object silhouettes, where the depth value is inaccurate or ambiguous.We present a learning-based method to synthesize the de-focus blur in shallow depth-of-field images from handheld bursts acquired with a single small aperture lens. Our deep learning model directly produces the shallow depth-of-field image, avoiding explicit depth-based blurring. The simulated aperture diameter equals the camera translation during burst acquisition. Our method does not suffer from artifacts due to inaccurate or ambiguous depth estimation, and it is well-suited to portrait photography.
FedSS: Federated Learning with Smart Selection of clients

Federated learning provides the ability to learn over heterogeneous user data in a distributed manner while preserving user privacy. However, its current client selection technique is a source of bias as it discriminates against slow clients. For starters, it selects clients that satisfy certain network and system-specific criteria, thus not selecting slow clients. Even when such clients are included in the training process, they either struggle with the training or are dropped altogether for being too slow. Our proposed idea looks to find a sweet spot between fast convergence and heterogeneity by looking at smart client selection and scheduling techniques.
Plurisigned hermitian metrics

Let ( X, ω ) be a compact hermitian manifold of dimension n . We study the asymptotic behavior of Monge-Amp`ere volumes R X ( ω + dd c ϕ ) n , when ω + dd c ϕ varies in the set of hermitian forms that are dd c -cohomologous to ω . We show that these Monge-Amp`ere volumes are uniformly bounded if ω is ”strongly pluripositive”, and that they are uniformly positive if ω is ”strongly plurinegative”. This motivates the study of the existence of such plurisigned hermitian metrics. We analyze several classes of examples (complex parallelisable manifolds, twistor spaces, Vaisman manifolds) admitting such metrics, showing that they cannot coexist. We take a close look at 6-dimensional nilmanifolds which admit a left-invariant complex structure, showing that each of them admit a plurisigned hermitian metric, while only few of them admit a pluriclosed metric. We also study 6-dimensional solvmanifolds with trivial canonical bundle.
Polarization axis fluctuations in a single mode VCSEL laser at 780nm

We present a transmission method to determine the polarization axis fluctuations for VCSEL (Vertical Cavity Surface Emitting Laser). The research is focused mainly on commercial lasers operatingat 780 nm, designed forlaser spectroscopy. The angle fluctuation of the laser linear axis polarization iselaborated to be less than 150 mrad. Measurement errors of this method are studied and calculated. The measurement uncertainty is ? 1,4 mrad andthis is equivalent to ? 5? of arc.Changes of the linear polarization axis depending on the laser current and temperaturearepresented. Full Text: PDF References T. Ohtoshi et al., "Dependence of optical gain on crystal orientation in surface? emitting lasers with strained quantum wells", Appl. Phys. Lett.63, 1886 (1994). CrossRef K. Tateno et al., "Growth of vertical-cavity surface-emitting laser structures on GaAs (311)B substrates by metalorganic chemical vapor deposition", Appl. Phys. Lett.70, 3395 (1997). CrossRef O. Tadanaga et al., "An 850-nm InAlGaAs strained quantum-well vertical-cavity surface-emitting laser grown on GaAs (311)B substrate with high-polarization stability", IEEE Photon. Tech. Lett.12, 942 (2000) CrossRef K. Choquette, R. Leibenguth, "Control of vertical-cavity laser polarization with anisotropic transverse cavity geometries", IEEE Photon. Tech. Lett. 6, 40 (1994) CrossRef B. Weigl et al., "High-performance oxide-confined GaAs VCSELs", IEEE J. Sel. Top.Quantum Electron. 3, 409 (1997) CrossRef P. Dowd et al., "Complete polarisation control of GaAs gain-guided top-surface emitting vertical cavity lasers", Electron. Lett. 33, 1315 (1997) CrossRef M. Grabherr et al., "Volume production of polarization controlled single-mode VCSELs", Proc. SPIE,6908, 690803 (2008) CrossRef R. Roy et al., "First-Passage-Time Distributions under the Influence of Quantum Fluctuations in a Laser", Phys. Rev. Lett.45, 1486 (1980) CrossRef J. Martin-Regalado et al., "Polarization switching in vertical-cavity surface emitting lasers observed at constant active region temperature", Appl. Phys. Lett. 70, 3350 (1997) CrossRef M. B. Willemsen et al., "Polarization Switching of a Vertical-Cavity Semiconductor Laser as a Kramers Hopping Problem", Phys. Rev. Lett. 82, 4815 (1999) CrossRef M. B. Willemsen et al., "Anatomy of a Polarization Switch of a Vertical-Cavity Semiconductor Laser", Phys. Rev. Lett. 84, 4337 (2000) CrossRef T. Mukaihara et al., "Excess intensity noise originated from polarization fluctuation in vertical-cavity surface-emitting lasers", IEEE Photon. Technol. Lett. 7, 1113 (1995) CrossRef
Einstein-Yang-Mills-Aether Theory with Nonlinear Axion Field: Decay of Color Aether and the Axionic Dark Matter Production

We establish a nonlinear version of the SU(N)-symmetric theory, which describes self-consistently the interaction between the gravitational, gauge, vector and pseudoscalar (axion) fields. In the context of this theory the SU(N)-symmetric multiplet of vector fields is associated with the color aether, the decay of which in the early Universe produced the canonic dynamic aether and the axionic dark matter. The SU(N)-symmetric Yang–Mills field, associated with the color aether, forms the source, which transfers the energy of the decaying color aether to the axion field. The nonlinear modification of the model uses explicitly the requirement of discrete symmetry, prescribed by the axion field, and is based on the analogy with a nonlinear physical pendulum. We show that in the framework of this nonlinear regular model, the axion field can grow to an arbitrarily large value, thus explaining the abundance of the axionic dark matter in the Universe.
On uniform-in-time diffusion approximation for stochastic gradient descent

The diffusion approximation of stochastic gradient descent (SGD) in current literature is only valid on a finite time interval. In this paper, we establish the uniform-in-time diffusion approximation of SGD, by only assuming that the expected loss is strongly convex and some other mild conditions, without assuming the convexity of each random loss function. The main technique is to establish the exponential decay rates of the derivatives of the solution to the backward Kolmogorov equation. The uniform-in-time approximation allows us to study asymptotic behaviors of SGD via the continuous stochastic differential equation (SDE) even when the random objective function $f(\cdot;\xi)$ is not strongly convex.
Snow Mask Guided Adaptive Residual Network for Image Snow Removal

Image restoration under severe weather is a challenging task. Most of the past works focused on removing rain and haze phenomena in images. However, snow is also an extremely common atmospheric phenomenon that will seriously affect the performance of high-level computer vision tasks, such as object detection and semantic segmentation. Recently, some methods have been proposed for snow removing, and most methods deal with snow images directly as the optimization object. However, the distribution of snow location and shape is complex. Therefore, failure to detect snowflakes / snow streak effectively will affect snow removing and limit the model performance. To solve these issues, we propose a Snow Mask Guided Adaptive Residual Network (SMGARN). Specifically, SMGARN consists of three parts, Mask-Net, Guidance-Fusion Network (GF-Net), and Reconstruct-Net. Firstly, we build a Mask-Net with Self-pixel Attention (SA) and Cross-pixel Attention (CA) to capture the features of snowflakes and accurately localized the location of the snow, thus predicting an accurate snow mask. Secondly, the predicted snow mask is sent into the specially designed GF-Net to adaptively guide the model to remove snow. Finally, an efficient Reconstruct-Net is used to remove the veiling effect and correct the image to reconstruct the final snow-free image. Extensive experiments show that our SMGARN numerically outperforms all existing snow removal methods, and the reconstructed images are clearer in visual contrast. All codes will be available.
SRRT: Search Region Regulation Tracking

Dominant trackers generate a ﬁxed-size rectangular region based on the previous prediction or initial bounding box as the model input, i . e . , search region. While this manner obtains improved tracking efﬁciency, a ﬁxed-size search region lacks ﬂexibility and is likely to fail in cases, e . g . , fast motion and distractor interference. Trackers tend to lose the target object due to the limited search region or be interfered by distractors due to excessive search region. In this work, we pro-pose a novel tracking paradigm, called Search Region Regulation Tracking (SRRT), which applies a proposed search region regulator to estimate an optimal search region dynamically for every frame. To adapt the object’s appearance variation during tracking, we further propose a locking-state determined updating strategy for reference frame updating. Our SRRT framework is very concise without fancy design, yet achieves evident improvements on the baselines and competitive results with other state-of-the-art trackers on seven challenging benchmarks. On the large-scale LaSOT benchmark, our SRRT improves SiamRPN++ and TransT with the absolute gains of 4.6% and 3.1% in terms of AUC.
Robust optimal investment and risk control for an insurer with general insider information

In this paper, we study the robust optimal investment and risk control problem for an insurer who owns the insider information about the ﬁnancial market and the insurance market under model uncertainty. Both ﬁnancial risky asset process and insurance risk process are assumed to be very general jump diffusion processes. The insider information is of the most general form rather than the initial enlargement type. We use the theory of forward integrals to give the ﬁrst half characterization of the robust optimal strategy and transform the anticipating stochastic differential game problem into the nonanticipative stochastic differential game problem. Then we adopt the stochastic maximum principle to obtain the total characterization of the robust strategy. We discuss the two typical situations when the insurer is ‘small’ and ‘large’ by Malliavin calculus. For the ‘small’ insurer, we obtain the closed-form solution in the continuous case and the half closed-form solution in the case with jumps. For the ‘large’ insurer, we reduce the problem to the quadratic backward stochastic differential equation (BSDE) and obtain the closed-form solution in the continuous case without model uncertainty. We discuss some impacts of the model uncertainty, insider information and the ‘large’ insurer on the optimal strategy.
Fuzzy Clustering by Hyperbolic Smoothing

We propose a novel method for building fuzzy clusters of large data sets, using a smoothing numerical approach. The usual sum-of-squares criterion is relaxed so the search for good fuzzy partitions is made on a continuous space, rather than a combinatorial space as in classical methods [8]. The smoothing allows a conversion from a strongly non-diﬀerentiable problem into diﬀerentiable subproblems of optimization without constraints of low dimension, by using a diﬀerentiable function of inﬁnite class. For the implementation of the algorithm we used the statistical software 𝑅 and the results obtained were compared to the traditional fuzzy 𝐶 –means method, proposed by Bezdek [1].
Learning to Order for Inventory Systems with Lost Sales and Uncertain Supplies

We consider a stochastic lost-sales inventory control system with lead time L over a planning horizon T. Supply is uncertain, and it is a function of the order quantity (because of random yield/capacity, etc.). We aim to minimize the T-period cost, a problem that is known to be computationally intractable even under known distributions of demand and supply. In this paper, we assume that both the demand and supply distributions are unknown and develop a computationally efficient online learning algorithm. We show that our algorithm achieves a regret (i.e., the performance gap between the cost of our algorithm and that of an optimal policy over T periods) of [Formula: see text] when [Formula: see text]. We do so by (1) showing that our algorithm’s cost is higher by at most [Formula: see text] for any [Formula: see text] compared with an optimal constant-order policy under complete information (a widely used algorithm) and (2) leveraging the latter’s known performance guarantee from the existing literature. To the best of our knowledge, a finite sample [Formula: see text] (and polynomial in L) regret bound when benchmarked against an optimal policy is not known before in the online inventory control literature. A key challenge in this learning problem is that both demand and supply data can be censored; hence, only truncated values are observable. We circumvent this challenge by showing that the data generated under an order quantity q2 allow us to simulate the performance of not only q2 but also, q1 for all [Formula: see text], a key observation to obtain sufficient information even under data censoring. By establishing a high-probability coupling argument, we are able to evaluate and compare the performance of different order policies at their steady state within a finite time horizon. Because the problem lacks convexity, commonly used learning algorithms, such as stochastic gradient decent and bisection, cannot be applied, and instead, we develop an active elimination method that adaptively rules out suboptimal solutions. This paper was accepted by Victor Martínez-de-Albéniz, operations management. Funding: This work is supported by the National Science Foundation [Grant CCF-2312205]. Z. Zhou also acknowledges the New York University’s 2024 Center for Global Economy and Business [Faculty Research Grant] and New York University [Research Catalyst Prize]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022. 02476 .
Monad interleaving: a construction of the operad for Leinster's weak $\omega$-categories

We show how to “interleave” the monad for operads and the monad for contractions on the category Coll of collections, to construct the monad for the operads-with-contraction of Leinster. We ﬁrst decom-pose the adjunction for operads and the adjunction for contractions into a chain of adjunctions each of which acts on only one dimension of the underlying globular sets at a time. We then exhibit mutual stability conditions that enable us to alternate the dimension-by-dimension free functors. Hence we give an explicit construction of a left adjoint for the forgetful functor OWC −→ Coll , from the category of operads-with-contraction to the category of collections. By applying this to the initial (empty) collection, we obtain explicitly an initial operad-with-contraction, whose algebras are by deﬁnition the weak ω -categories of Leinster.
BotNet Intrusion Detection System in Internet of Things with Developed Deep Learning

The rapid growth of technology has led to the creation of computing networks. The applications of the Internet of Things are becoming more and more visible with the expansion and development of sensors and the use of a series of equipment to connect to the Internet. Of course, the growth of any network will also provide some challenges. The main challenge of IoT like any other network is its security. In the field of security, there are issues such as attack detection, authentication, encryption and the so on. One of the most important attack is cyber-attacks that disrupt the network usage. One of the most important attacks on the IoT is BotNet attack. The most important challenges of this topic include very high computational complexity, lack of comparison with previous methods, lack of scalability, high execution time, lack of review of the proposed approach in terms of accuracy to detect and classify attacks and intrusions. Using intrusion detection systems for the IoT is an important step in identifying and detecting various attacks. Therefore, an algorithm that can solve these challenges has provided a near-optimal method. Using training-based models and algorithms such as Deep Dearning-Reinforcement Learning and XGBoost learning in combination (DRL-XGBoost) models can be an interesting approach to overcoming previous weaknesses. The data of this research is Bot-IoT-2018.
A Study on the Effectiveness of Using Telepresence and Multiple Cameras in Remote Physical Therapy

The present research investigates the effectiveness of using a telep- resence system compared to a video conferencing system and the effectiveness of using two cameras compared to one camera for remote physical therapy. The telepresence system that was used is Telegie, which allows users to see a place in 3D through a VR head- set. Using two cameras with a video conferencing system allows users to see a place from multiple angles and provides additional spa- tial information. These two approaches of providing users additional spatial information were examined and compared in the context of remote physical therapy through a user study with 11 physical therapists who had subject matter expertise and 76 participants who acted as physical therapy patients. We conducted detailed interviews of therapists at the end of their participation. Results showed that none of the main effects of using telepresence and using two cameras were supported via t-tests. However, additional analyses using linear mixed models with individual differences of participants controlled revealed a marginally signiﬁcant positive effect of using two cam- eras on assessment scores from the therapists. The ﬁndings of this paper indicate that video ﬁdelity of remote communication systems matters and therefore suggest telepresence systems should provide sufﬁcient video clarity. Spatial ability of patients was found as a strong predictor of therapist assessments.
On the peculiar rotational evolution of PSR B0950+08

The long-term rotational evolution of the old, isolated pulsar, PSR B0950+08 is intriguing in that its spin-down rate displays sinusoidal-like oscillations due to alternating variations, both in magnitude and sign, of the second time derivative of the pulse frequency. We show that the large internal temperature to pinning energy ratio towards the base of the crust implied by the recent high surface temperature measurement of PSR B0950+08 leads to linear creep interaction between vortex lines and pinning sites to operate in this pulsar. Vortex lines assume a parabolic shape due to pinning to nuclear clusters and finite tension of vortices acts as a restoring force which tends to bring a vortex back to its straight shape. The resulting low frequency oscillations of vortex lines combined with the time variable coupling between the internal superfluid components and the external pulsar braking torque give rise to an oscillatory spin-down rate. We apply this model to PSR B0950+08 observations for several external torque models. Our model has potential to constrain the radial extension of the closed magnetic field region in the outer core of neutron stars from the oscillation period of the spin-down rate.
TArC: Tunisian Arabish Corpus, First complete release

In this paper we present the final result of a project focused on Tunisian Arabic encoded in Arabizi, the Latin-based writing system for digital conversations. The project led to the realization of two integrated and independent tools: a linguistic corpus and a neural network architecture created to annotate the former with various levels of linguistic information (code-switching classification, transliteration, tokenization, POS-tagging, lemmatization). We discuss the choices made in terms of computational and linguistic methodology and the strategies adopted to improve our results. We report on the experiments performed in order to outline our research path. Finally, we explain the reasons why we believe in the potential of these tools for both computational and linguistic researches.
PromptEM: Prompt-tuning for Low-resource Generalized Entity Matching

Entity Matching (EM), which aims to identify whether two entity records from two relational tables refer to the same real-world entity, is one of the fundamental problems in data management. Traditional EM assumes that two tables are homogeneous with the aligned schema, while it is common that entity records of different formats (e.g., relational, semi-structured, or textual types) involve in practical scenarios. It is not practical to unify their schemas due to the different formats. To support EM on format-different entity records, Generalized Entity Matching (GEM) has been proposed and gained much attention recently. To do GEM, existing methods typically perform in a supervised learning way, which relies on a large amount of high-quality labeled examples. However, the labeling process is extremely labor-intensive, and frustrates the use of GEM. Low-resource GEM, i.e., GEM that only requires a small number of labeled examples, becomes an urgent need. To this end, this paper, for the first time, focuses on the low-resource GEM and proposes a novel low-resource GEM method, termed as PromptEM. PromptEM has addressed three challenging issues (i.e., designing GEM-specific prompt-tuning, improving pseudo-labels quality, and running efficient self-training) in low-resource GEM. Extensive experimental results on eight real benchmarks demonstrate the superiority of PromptEM in terms of effectiveness and efficiency.
Motion Planning and Tracking Control of Unmanned Underwater Vehicles: Technologies, Challenges and Prospects

The motion planning and tracking control techniques of unmanned underwater vehicles (UUV) are fundamentally significant for efficient and robust UUV navigation, which is crucial for underwater rescue, facility maintenance, marine resource exploration, aquatic recreation, etc. Studies on UUV motion planning and tracking control have been growing rapidly worldwide, which are usually sorted into the following topics: task assignment of the multi-UUV system, UUV path planning, and UUV trajectory tracking. This paper provides a comprehensive review of conventional and intelligent technologies for motion planning and tracking control of UUVs. Analysis of the benefits and drawbacks of these various methodologies in literature is presented. In addition, the challenges and prospects of UUV motion planning and tracking control are provided as possible developments for future research.