Miracle-dz/UniFAR-FTUs huggingface dataset:
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models

High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL). However, since the emergence of the ImageNet dataset and the AlexNet model in 2012, the size of new open-source labeled vision datasets has remained roughly constant. Consequently, only a minority of publications in the computer vision community tackle supervised learning on datasets that are orders of magnitude larger than Imagenet. In this paper, we survey computer vision research domains that study the effects of such large datasets on model performance across different vision tasks. We summarize the community's current understanding of those effects, and highlight some open questions related to training with massive datasets. In particular, we tackle: (a) The largest datasets currently used in computer vision research and the interesting takeaways from training on such datasets; (b) The effectiveness of pre-training on large datasets; (c) Recent advancements and hurdles facing synthetic datasets; (d) An overview of double descent and sample non-monotonicity phenomena; and finally, (e) A brief discussion of lifelong/continual learning and how it fares compared to learning from huge labeled datasets in an offline setting. Overall, our findings are that research on optimization for deep learning focuses on perfecting the training routine and thus making DL models less data hungry, while research on synthetic datasets aims to offset the cost of data labeling. However, for the time being, acquiring non-synthetic labeled data remains indispensable to boost performance.
Speech2AffectiveGestures: Synthesizing Co-Speech Gestures with Generative Adversarial Affective Expression Learning

We present a generative adversarial network to synthesize 3D pose sequences of co-speech upper-body gestures with appropriate affective expressions. Our network consists of two components: a generator to synthesize gestures from a joint embedding space of features encoded from the input speech and the seed poses, and a discriminator to distinguish between the synthesized pose sequences and real 3D pose sequences. We leverage the Mel-frequency cepstral coefficients and the text transcript computed from the input speech in separate encoders in our generator to learn the desired sentiments and the associated affective cues. We design an affective encoder using multi-scale spatial-temporal graph convolutions to transform 3D pose sequences into latent, pose-based affective features. We use our affective encoder in both our generator, where it learns affective features from the seed poses to guide the gesture synthesis, and our discriminator, where it enforces the synthesized gestures to contain the appropriate affective expressions. We perform extensive evaluations on two benchmark datasets for gesture synthesis from the speech, the TED Gesture Dataset and the GENEA Challenge 2020 Dataset. Compared to the best baselines, we improve the mean absolute joint error by 10-33%, the mean acceleration difference by 8-58%, and the Fréchet Gesture Distance by 21-34%. We also conduct a user study and observe that compared to the best current baselines, around 15.28% of participants indicated our synthesized gestures appear more plausible, and around 16.32% of participants felt the gestures had more appropriate affective expressions aligned with the speech.
Electric-field control of exchange interactions

The impact of an applied electric field on the exchange coupling parameters has been investigated based on first-principles electronic structure calculations by means of the KKR Green function method. The calculations have been performed for a Fe film, free-standing and deposited on two different substrates, having 1 monolayer (ML) thickness to minimize the effect of screening of the electric field typical for metallic systems. By comparing the results for the free-standing Fe ML with those for Fe on the various substrates, we could analyze the origin of the field-induced change of the exchange interactions. Compared to thefree-standing Fe ML, in particular rather pronounced changes have been found for the Fe/Pt(111) system due to the localized electronic states at the Fe/Pt interface, which are strongly affected by the electric field and which play an important role for the Fe-Fe exchange interactions.
Automated Pest Detection With DNN on the Edge for Precision Agriculture

Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer’s intervention.
Reliable Semantic Segmentation with Superpixel-Mix

Along with predictive performance and runtime speed, reliability is a key requirement for real-world semantic segmentation. Reliability encompasses robustness, predictive uncertainty and reduced bias. To improve reliability, we introduce Superpixel-mix, a new superpixel-based data augmentation method with teacher-student consistency training. Unlike other mixing-based augmentation techniques, mixing superpixels between images is aware of object boundaries, while yielding consistent gains in segmentation accuracy. Our proposed technique achieves state-of-the-art results in semi-supervised semantic segmentation on the Cityscapes dataset. Moreover, Superpixel-mix improves the reliability of semantic segmentation by reducing network uncertainty and bias, as conﬁrmed by competitive results under strong distributions shift (adverse weather, image corruptions) and when facing out-of-distribution data.
Universal energy-dependent pseudopotential for the two-body problem of confined ultracold atoms

The two-body scattering amplitude and energy spectrum of confined ultracold atoms are of fundamental importance for both theoretical and experimental studies of ultracold atom physics. For many systems, one can efficiently calculate these quantities via the zero-range Huang-Yang pseudopotential (HYP), in which the interatomic interaction is characterized by the scattering length a. Furthermore, when the scattering length is dependent on the kinetic energy εr of two-atom relative motion, i.e., a = a(εr), the results are applicable for a broad energy region. However, when the free Hamiltonian of atomic internal state (e.g., the Zeeman Hamiltonian) does not commute with the inter-atomic interaction, or the center-of-mass (CoM) motion is coupled to the relative motion, the generalization of this technique is still lacking. In this work we solve this problem and construct a reasonable energy-dependent multi-channel HYP, which is characterized by a “scattering length operator” âeff , for the above complicated cases. Here âeff is an operator for atomic internal states and CoM motion, and depends on both the total two-atom energy and the external field as well as the trapping parameter. The effects from the internal-state or CoM-relative motion coupling can be self-consistently taken into account by âeff . We further show a method based on the quantum defect theory, with which âeff can be analytically derived for systems with van der Waals inter-atomic interaction. To demonstrate our method, we calculate the spectrum of two ultracold fermionic alkaline-earth-like atoms (in electronic S0 (|g〉) and P0 (|e〉) states, respectively) confined in an optical lattice. By comparing our results with the recent experimental measurements for two Yb atoms and two Yb atoms, we calibrate the scattering lengths a± with respect to anti-symmetric and symmetric nuclear-spin states to be a+ = 2012(19)a0 and a− = 193(4)a0 for Yb, and a+ = 232(3)a0 and a− = 372(1)a0 for Yb.
Multilevel Knowledge Transfer for Cross-Domain Object Detection

Domain shift is a well known problem where a model trained on a particular domain (source) does not perform well when exposed to samples from a different domain (target). Unsupervised methods that can adapt to domain shift are highly desirable as they allow effective utilization of the source data without requiring additional annotated training data from the target. Practically, obtaining sufficient amount of annotated data from the target domain can be both infeasible and extremely expensive. In this work, we address the domain shift problem for the object detection task. Our approach relies on gradually removing the domain shift between the source and the target domains. The key ingredients to our approach are -- (a) mapping the source to the target domain on pixel-level; (b) training a teacher network on the mapped source and the unannotated target domain using adversarial feature alignment; and (c) finally training a student network using the pseudo-labels obtained from the teacher. Experimentally, when tested on challenging scenarios involving domain shift, we consistently obtain significantly large performance gains over various recent state of the art approaches.
StyleGAN-NADA

Can a generative model be trained to produce images from a specific domain, guided only by a text prompt, without seeing any image? In other words: can an image generator be trained "blindly"? Leveraging the semantic power of large scale Contrastive-Language-Image-Pre-training (CLIP) models, we present a text-driven method that allows shifting a generative model to new domains, without having to collect even a single image. We show that through natural language prompts and a few minutes of training, our method can adapt a generator across a multitude of domains characterized by diverse styles and shapes. Notably, many of these modifications would be difficult or infeasible to reach with existing methods. We conduct an extensive set of experiments across a wide range of domains. These demonstrate the effectiveness of our approach, and show that our models preserve the latent-space structure that makes generative models appealing for downstream tasks. Code and videos available at: stylegan-nada.github. io/
Subcritical and supercritical bifurcations in axisymmetric viscoelastic pipe flows

Abstract Axisymmetric viscoelastic pipe flow of Oldroyd-B fluids has been recently found to be linearly unstable by Garg et al. (Phys. Rev. Lett., vol. 121, 2018, 024502). From a nonlinear point of view, this means that the flow can transition to turbulence supercritically, in contrast to the subcritical Newtonian pipe flows. Experimental evidence of subcritical and supercritical bifurcations of viscoelastic pipe flows have been reported, but these nonlinear phenomena have not been examined theoretically. In this work, we study the weakly nonlinear stability of this flow by performing a multiple-scale expansion of the disturbance around linear critical conditions. The perturbed parameter is the Reynolds number with the others being unperturbed. A third-order Ginzburg–Landau equation is derived with its coefficient indicating the bifurcation type of the flow. After exploring a large parameter space, we found that polymer concentration plays an important role: at high polymer concentrations (or small solvent-to-solution viscosity ratio $\beta \lessapprox 0.785$), the nonlinearity stabilizes the flow, indicating that the flow will bifurcate supercritically, while at low polymer concentrations ($\beta \gtrapprox 0.785$), the flow bifurcation is subcritical. The results agree qualitatively with experimental observations where critical $\beta \approx 0.855$. The pipe flow of upper convected Maxwell fluids can be linearly unstable and its bifurcation type is also supercritical. At a fixed value of $\beta$, the Landau coefficient scales with the inverse of the Weissenberg number ($Wi$) when $Wi$ is sufficiently large. The present analysis provides a theoretical understanding of the recent studies on the supercritical and subcritical routes to the elasto-inertial turbulence in viscoelastic pipe flows.
A Model for the Small-Scale Turbulence Production Term in the Local Supersaturation Balance at a Cloud Top Boundary

The supersaturation local balance equation is considered at a cloud boundary, and the structure of its turbulent production term is inferred from numerical experiments. The transient decay of an unstable-stratified, shearless-mixing layer at a warm cloud top is modeled as an initial value problem, in which the turbulent velocity, the active scalars, and the droplet dynamics are solved concurrently. The evolution of the cloud-clear air interface and the droplets therein is investigated via direct numerical simulations. Both initial monodisperse and polydisperse droplet size distribution cases are treated. We attempt to model the turbulent production of supersaturation defined as the difference between the time derivative of the supersaturation and the condensation terms in the mixing layer. The planar averages of the supersaturation production term are clearly correlated with the planar covariance of the computed horizontal velocity derivative and supersaturation fields, thus suggesting that a quasi-linear relationship may exist between these quantities. The value of the nondimensional proportionality constant is deduced by i) modeling the production term as the interaction of two sinusoidal perturbations with local and global small-scale frequencies, and ii) spatially averaging the production term and the covariance of the supersaturation and the longitudinal velocity derivatives. It has been verified that the value of such proportionality constant does not vary considerably throughout the transient decay and is not sensitive to the initial droplet distribution dispersity.
One-pot liquid-phase synthesis of MoS2-WS2 van der waals heterostructures for broadband photodetection

Two dimensional (2D) van der Waals heterostructures (vdWHs) have unique potential in facilitating the stacking of layers of different 2D materials for optoelectronic devices with superior characteristics. However, the fabrication of large area all-2D heterostructures is still challenging towards realizing practical devices at a reduced cost. In the present work, we have demonstrated a rapid yet simple, impurity-free and efficient sonication-assisted chemical exfoliation approach to synthesize hybrid vdWHs based on 2D molybdenum disulphide (MoS2) and tungsten disulphide (WS2), with high yield. Microscopic and spectroscopic studies have confirmed the successful exfoliation of layered 2D materials and formation of their hybrid heterostructures. The co-existence of 2D MoS2 and WS2 in the vdWH hybrids is established by optical absorption and Raman shift measurements along with their chemical stiochiometry determined by x-ray photoelectron spectroscopy. The spectral response of the vdWH/Si (2D/3D) heterojunction photodetector fabricated using the as-synthesized material is found to exhibit broadband photoresponse compared to that of the individual 2D MoS2 and WS2 devices. The peak responsivity and detectivity are found to be as high as ∼2.15 A W−1 and 2.05 × 1011 Jones, respectively for an applied bias of –5 V. The ease of fabrication with appreciable performance of the chemically synthesized vdWH-based devices have revealed their potential use for large area optoelectronic applications on Si-compatible CMOS platforms.
YASMIN: a real-time middleware for COTS heterogeneous platforms

Commercial-off-the-shelf (COTS) heterogeneous platforms provide immense computational power, but are difficult to program and to correctly use when real-time requirements come into play: A sound configuration of the operating system scheduler is needed, and a suitable mapping of tasks to computing units must be determined. Flawed designs lead to sub-optimal system configurations and, thus, to wasted resources or even to deadline misses and system failures. We propose YASMIN, a middleware to schedule end-user applications with real-time requirements in user space and on behalf of the operating system. YASMIN combines an easy-to-use programming interface with portability across a wide range of architectures. It treats heterogeneity on COTS embedded platforms as a first-class citizen: YASMIN supports multiple functionally equivalent task implementations with distinct extra-functional behaviour. This enables the system designer to quickly explore different scheduling policies and task-to-core mappings, and thus, to improve overall system performance. In this paper, we present the design and implementation of YASMIN and provide an analysis of the scheduling overhead on an Odroid-XU4 platform. We demonstrate the merits of YASMIN on an industrial use-case involving a search-and-rescue drone.
Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control

Model-based reinforcement learning (MBRL) is believed to have much higher sample efficiency compared with model-free algorithms by learning a predictive model of the environment. However, the performance of MBRL highly relies on the quality of the learned model, which is usually built in a black-box manner and may have poor predictive accuracy outside of the data distribution. The deficiencies of the learned model may prevent the policy from being fully optimized. Although some uncertainty analysis-based remedies have been proposed to alleviate this issue, model bias still poses a great challenge for MBRL. In this work, we propose to leverage the prior knowledge of underlying physics of the environment, where the governing laws are (partially) known. In particular, we developed a physics-informed MBRL framework, where governing equations and physical constraints are used to inform the model learning and policy search. By incorporating the prior information of the environment, the quality of the learned model can be notably improved, while the required interactions with the environment are significantly reduced, leading to better sample efficiency and learning performance. The effectiveness and merit have been demonstrated over a handful of classic control problems, where the environments are governed by canonical ordinary/partial differential equations.
Revised event rates for extreme and extremely large mass-ratio inspirals

One of the main targets of the Laser Interferometer Space Antenna (LISA) is the detection of extreme mass-ratio inspirals (EMRIs) and extremely large mass-ratio inspirals (X-MRIs). Their orbits are expected to be highly eccentric and relativistic when entering the LISA band. Under these circumstances, the inspiral time-scale given by Peters’ formula loses precision and the shift of the last-stable orbit (LSO) caused by the massive black hole spin could influence the event rates estimate. We re-derive EMRIs and X-MRIs event rates by implementing two different versions of a Kerr loss-cone angle that includes the shift in the LSO, and a corrected version of Peters’ time-scale that accounts for eccentricity evolution, 1.5 post-Newtonian hereditary fluxes, and spin-orbit coupling. The main findings of our study are summarized as follows: (1) implementing a Kerr loss-cone changes the event rates by a factor ranging between 0.9 and 1.1; (2) by employing the usual definition of the loss-cone and the corrected version of Peters’ time-scale, we obtain event rates of about 8 to 30 times lower than the rates calculated with the usual Peters’ formula; (3) if we use an alternative version of the gravitational-wave merger time-scale, suitable for high eccentricities, the rates differ from the corrected values by a factor between 0.9 and 3.
Convergence rate for the incompressible limit of nonlinear diffusion–advection equations

The incompressible limit of nonlinear diffusion equations of porous medium type has attracted a lot of attention in recent years, due to its ability to link the weak formulation of cell-population models to free boundary problems of Hele-Shaw type. Although vast literature is available on this singular limit, little is known on the convergence rate of the solutions. In this work, we compute the convergence rate in a negative Sobolev norm and, upon interpolating with BV -uniform bounds, we deduce a convergence rate in appropriate Lebesgue spaces. 2010 Mathematics Subject Classification. 35K57; 35K65; 35Q92; 35B45;
Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders

On an artist’s profile page, music streaming services frequently recommend a ranked list of ”similar artists” that fans also liked. However, implementing such a feature is challenging for new artists, for which usage data on the service (e.g. streams or likes) is not yet available. In this paper, we model this cold start similar artists ranking problem as a link prediction task in a directed and attributed graph, connecting artists to their top-k most similar neighbors and incorporating side musical information. Then, we leverage a graph autoencoder architecture to learn node embedding representations from this graph, and to automatically rank the top-k most similar neighbors of new artists using a gravity-inspired mechanism. We empirically show the flexibility and the effectiveness of our framework, by addressing a real-world cold start similar artists ranking problem on a global music streaming service. Along with this paper, we also publicly release our source code and the industrial data from our experiments.
Validity and reliability of oral temperature compared to ingestible core temperature pill in free-living conditions

Complex thermodynamics of the human body and external factors may cause differences between oral and core body temperatures. We present a 19-subject study where continuous measurements of ingested core temperature pills are compared against spot measurements of oral temperature, both FDA-cleared devices, in free-living conditions. Based on measurements from 419 samples across 19 subjects, MAE of 0.30 ± 0.26 oC and 95% limits of agreement of -0.60 and 0.90 oC was observed between ingestible pill temperature and oral temperature. These results demonstrate that statistically significant differences (p<0.001) between oral and core temperature may arise in free-living conditions. Such clinically relevant differences in temperature measurements due to complex thermodynamics of heat exchange in the human body, measurement noise, and other external factors show the intricacies of interpreting body temperatures measured at different locations.