Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Picus® Sonic Tomograph to assess the health state of trees of monumental sizes

The aim of the study was to examine the usefulness of The Picus 3 Sonic Tomograph to assess the health state of trees of monumental sizes, which are an important part of the cultural landscape. In the study there were selected tree species Tilia cordata, Gingko biloba and Ulmus glabra growing in the vicinity of Lublin in different habitat conditions. Changes in the internal structure of the tree trunks were shown on the colour tomograms. Two tested specimens of Tilia cordata located in Radawiec Duży were intended to be cut down due to the construction of the ring road of Lublin. This made it possible to compare the obtained tomograms with the actual state of the inside of tree trunks after cutting down the examined trees. The results of Picus tomography Ulmus glabra and Gingko biloba growing in the historic park in Czesławice were confirmed by a visual assessment of trees. In each case the number of installed sensors influenced the image resolution and accuracy of the obtained tomograms. Diagnostics using sound waves is important in assessing the state of health of each tree, but it is especially recommended for testing of valuable trees. It allows for early diagnosis and selection of such protective actions to reduce to a minimum the destructive impact study on the body of the plant. key words: Gingko biloba, Tilia cordata, Ulmus glabra, trees of monumental sizes, PiCUS Sonic Tomography
Effect of neem oil treated urea supplementation on growth performance of heifers

The aim of this study was to evaluate neem oil treated urea supplementation on growth performance of Hariana heifers. The effect of 5, 10 and 20% (w/w) neem oil treatment of urea, uncoated urea and commercially available neem coated urea were included @1% (DM basis) in total mixed ration (concentrate: roughage, 50:50) by replacing mustard cake in an in vitro rumen fermentation study. Total gas production, true organic matter and dry matter degradability, ammonia nitrogen, partitioning factor and microbial biomass production remained similar (p>0.05) in all the treatment groups. A 20% (w/w) dose level of neem oil treated urea was selected for in vivo experiment. Twenty four heifers of 1.5 to 2 years of age were randomly blocked into 4 groups (Control, T 1 , T 2 and T 3 ) having 6 animals in each (BW basis). Animals in Control, T 1 , T 2 and T 3 groups were fed basal ration (concentrate: roughage 50:50 without urea), basal ration having concentrate with uncoated urea 2%, neem coated urea 2% (commercial) and neem oil coated urea 2% (20% w/w) on DM basis, respectively. Average daily gain, weight gain and body condition score were not impacted by neem oil treated urea supplementation. Nutrient intake, feed conversion ratio and nutrient digestibility also remained similar (p>0.05) in all the experimental groups. Similarly, total plasma protein, albumin, globulin, plasma urea nitrogen, creatinine, Ca and P concentration were also not impacted by neem oil treated urea supplementation and it had no adverse effect on liver function test. It may be concluded that commercially available neem coated urea or 20% (w/w) neem oil treated urea can be included at 1% level in total mixed ration of growing ruminants without any adverse effect on animal performance.
CCIDE_A_256284 313..318

1Department of Oral Disease, Faculty of Medicine, Universitas Sebelas Maret, Surakarta, Indonesia; 2Department of Conservative Dentistry, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia; 3Department of Oral Medicine, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia Background: Endodontic treatment on dental caries with dental granuloma on the periapical has the potential to flare. The symptoms of acute flare in dental granuloma are strongly related to the activation of macrophages that produce pro-inflammatory cytokine through nuclear factor kappa B (NFκB). However, the role of NFκB in dental granuloma has not been widely explained. Purpose: To compare NFκB expression in dental granuloma and dental nongranuloma in the periapical of dental caries. Methods: The periapical lesion of dental granuloma and dental nongranuloma was obtained from patients with dental caries who were subjected to tooth extraction. The periapical lesion was confirmed by periapical radiography and then differentiated by histological and immunohistochemistry by analyzing the NFκB expression. Results: Most dental granulomas were found in females (66.67%) between the ages 36 and 45 (61.11%). Dental granuloma showed higher NFκB expression compared to dental nongranuloma lesions in tooth caries (p=0.021). Conclusion: The dental granuloma tissue of dental caries showed an increase in NFκB expression. Understanding the role of NFκB might provide additional insights into the process of periapical lesion development.
Change in Liver Blood Flow and Blood Content in Dogs during Direct and Reflex Alteration of Hepatic Sympathetic Nerve Activity

A mean decrease of 60% in liver blood volume was recorded by a plethysmographic technique during electrical stimulation of the hepatic nerves in anesthetized, vagotomized dogs. A decrease in pressure in the vascularly isolated carotid sinus to 40 mm Hg, from a mean control of 144 mm Hg, decreased liver blood volume by a mean of 16%; arterial blood pressure increased by a mean of 77 mm Hg. Carotid sini's hypotension was accompanied by respective mean increases of 16% and 1.4% in hepatic arterial and portal venous blood flows, and of 45% and 22% in arterial and portal resistances. Increase in sinus pressure to 240 mm Hg increased liver blood volume by a mean of 20%; arterial blood pressure decreased by 90 mm Hg. Sinus hypertension was accompanied by respective mean decreases of 10% and 1.5% in hepatic arterial and portal venous blood flows, and of 44% and 18% in arterial and portal resistances. Interruption of afferent vagal traffic from cardiopulmonary receptors was maximally effective in decreasing liver blood volume at a carotid sinus pressure of 40 mm Hg and was ineffective at carotid sinus pressures greater than 160 mm Hg. Combined withdrawal of carotid and cardiopulmonary vasomotor inhibition decreased liver blood volume by 42%; of this 37% was due to the cardiopulmonary and 63% to the carotid baroreflex. The study showed the canine liver to function as a blood reservoir by active mobilization of a portion of its blood volume.
’ Response : Bezafibrate and Simvastatin : Different Beneficial Effects for Different Therapeutic Aims

found, as would be expected, that body mass index (BMI) was a significant predictor of HOMA score (P 0.0002) but that resistin adjusted for BMI was not a significant predictor of HOMA score (P 0.11). There was no effect of gender or age on this outcome. Our findings are opposite of those in the recent report from Silha et al. (4), which found a significant correlation between serum resistin and HOMA score in a small group of subjects. However, our findings are in agreement with two other reports that found no relationship between resistin and HOMA score using large study groups (2, 3). As we suggest in our paper, it is possible that resistin is a marker for, or contributes to, insulin resistance in a specific population. Silha and Murphy (1) also wish to know the outcome of the multiple regression analysis after removal of the five subjects being treated for diabetes, the one subject with missing age, and the two outlying data points. After removal of this data, BMI remains a significant predictor of HOMA score (P 0.001), and resistin adjusted for BMI is not a significant predictor of HOMA score (P 0.15). Again gender and age had no effect on this relationship. We therefore remain confident in our statistical analysis and interpretation of the results that resistin is not a significant predictor of insulin resistance in humans. However, we agree with Silha and Murphy that larger studies of the role of resistin in regulating insulin resistance in humans are needed because, as we noted in our manuscript, the power to detect a significant effect of resistin on HOMA score was low. We are currently pursuing a larger study that will address this issue.
Congruence modules and the Wiles-Lenstra-Diamond numerical criterion in higher codimensions

We define a congruence module ΨA(M) associated to a surjective O-algebra morphism λ : A → O, with O a discrete valuation ring, A a complete noetherian local O-algebra regular at p, the kernel of λ, and M a finitely generated A-module. We establish a numerical criterion for M to have a free direct summand over A of positive rank. It is in terms of the lengths of ΨA(M) and the torsion part of p/p . It generalizes results of Wiles, Lenstra, and Diamond, that deal with the case when the codimension of p is zero. Number theoretic applications include integral (non-minimal) R = T theorems in situations of positive defect conditional on certain standard conjectures. Here R is a deformation ring parametrizing certain Galois representations and T is a Hecke algebra. An example of a positive defect situation is that of proving modularity lifting for 2-dimensional l-adic Galois representations over an imaginary quadratic field. The proofs combine our commutative algebra results with a generalization due to Calegari and Geraghty of the patching method of Wiles and Taylor–Wiles and level raising arguments that go back to Ribet. The results provide new evidence in favor of the intriguing, and as yet fledgling, torsion analog of the classical Langlands correspondence.
On Scaled Methods for Saddle Point Problems

Methods with adaptive scaling of different features play a key role in solving saddle point problems, primarily due to Adam's popularity for solving adversarial machine learning problems, including GANS training. This paper carries out a theoretical analysis of the following scaling techniques for solving SPPs: the well-known Adam and RmsProp scaling and the newer AdaHessian and OASIS based on Hutchison approximation. We use the Extra Gradient and its improved version with negative momentum as the basic method. Experimental studies on GANs show good applicability not only for Adam, but also for other less popular methods.
How cooling influences circumbinary discs

Circumbinary disc observations and simulations show large, eccentric inner cavities. Recent work has shown that the shape and size of these cavities depend on the aspect ratio and viscosity of the disc, as well as the binary eccentricity and mass ratio. It has been further shown that, for gaps created by planets, the cooling timescale significantly affects the shape and size of the gap. In this study, we consider the effect of different cooling models on the cavity shape in a circumbinary disc. We compare locally isothermal and radiatively cooled disc models to ones with a parametrised cooling timescale ($\beta$-cooling), implemented in 2D numerical simulations for varying binary eccentricities. While the shape of the cavity for radiative and locally isothermal models remains comparable, the inner disc structure changes slightly, leading to a change in the precession rate of the disc. With $\beta$-cooled models, the shape and size of the cavity changes dramatically towards values of $\beta$=1. Based on our findings, we introduce a parametrised $\beta$ model that accounts for the shorter cooling timescale inside the cavity while adequately reproducing the results of the radiative model, and we highlight that accurate treatment of the thermodynamics inside the cavity has a significant impact in modelling circumbinary systems.
Improving generalization by mimicking the human visual diet

We present a new perspective on bridging the generalization gap between biological and computer vision -- mimicking the human visual diet. While computer vision models rely on internet-scraped datasets, humans learn from limited 3D scenes under diverse real-world transformations with objects in natural context. Our results demonstrate that incorporating variations and contextual cues ubiquitous in the human visual training data (visual diet) significantly improves generalization to real-world transformations such as lighting, viewpoint, and material changes. This improvement also extends to generalizing from synthetic to real-world data -- all models trained with a human-like visual diet outperform specialized architectures by large margins when tested on natural image data. These experiments are enabled by our two key contributions: a novel dataset capturing scene context and diverse real-world transformations to mimic the human visual diet, and a transformer model tailored to leverage these aspects of the human visual diet. All data and source code can be accessed at https://github.com/Spandan-Madan/human_visual_diet.
SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation

Adapting to a continuously evolving environment is a safety-critical challenge inevitably faced by all autonomous-driving systems. Existing image- and video-based driving datasets, however, fall short of capturing the mutable nature of the real world. In this paper, we introduce the largest multi-task synthetic dataset for autonomous driving, SHIFT. It presents discrete and continuous shifts in cloudiness, rain and fog intensity, time of day, and vehicle and pedestrian density. Featuring a comprehensive sensor suite and annotations for several mainstream perception tasks, SHIFT allows to investigate how a perception systems' performance degrades at increasing levels of domain shift, fostering the development of continuous adaptation strategies to mitigate this problem and assessing the robustness and generality of a model. Our dataset and benchmark toolkit are publicly available at www.vis. xyz/shift.
An Improved Normed-Deformable Convolution for Crowd Counting

In recent years, crowd counting has become an important issue in computer vision. In most methods, the density maps are generated by convolving with a Gaussian kernel from the ground-truth dot maps which are marked around the center of human heads. Due to the fixed geometric structures in CNNs and indistinct head-scale information, the head features are obtained incompletely. Deformable Convolution is proposed to exploit the scale-adaptive capabilities for CNN features in the heads. By learning the coordinate offsets of the sampling points, it is tractable to improve the ability to adjust the receptive field. However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information. To handle the non-uniformed sampling, an improved Normed-Deformable Convolution (i.e.,NDConv) implemented by Normed-Deformable loss (i.e.,NDloss) is proposed in this paper. The offsets of the sampling points which are constrained by NDloss tend to be more even. Then, the features in the heads are obtained more completely, leading to better performance. Especially, the proposed NDConv is a light-weight module which shares similar computation burden with Deformable Convolution. In the extensive experiments, our method outperforms state-of-the-art methods on ShanghaiTech A, ShanghaiTech B, UCF-QNRF, and UCF_CC_50 dataset, achieving 61.4, 7.8, 91.2, and 167.2 MAE, respectively.
A Simple Baseline for BEV Perception Without LiDAR

: Building 3D perception systems for autonomous vehicles that do not rely on LiDAR is a critical research problem because of the high expense of LiDAR systems compared to cameras and other sensors. Current methods use multi-view RGB data collected from cameras around the vehicle and neurally “lift” features from the perspective images to the 2D ground plane, yielding a “bird’s eye view” (BEV) feature representation of the 3D space around the vehicle. Recent research focuses on the way the features are lifted from images to the BEV plane. We instead propose a simple baseline model, where the “lifting” step simply averages features from all projected image locations, and ﬁnd that it outperforms the current state-of-the-art in BEV vehicle segmentation. Our ablations show that batch size, data augmentation, and input resolution play a large part in performance. Additionally, we reconsider the utility of radar input, which has previously been either ignored or found non-helpful by recent works. With a simple RGB-radar fusion module, we obtain a sizable boost in performance, approaching the accuracy of a LiDAR-enabled system. 1
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey

Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security and robustness of AI models. Yet the more primitive adversarial attacks might be physically infeasible or require some resources that are hard to access like the training data, which motivated the emergence of patch attacks. In this survey, we provide a comprehensive overview to cover existing techniques of adversarial patch attacks, aiming to help interested researchers quickly catch up with the progress in this field. We also discuss existing techniques for developing detection and defences against adversarial patches, aiming to help the community better understand this field and its applications in the real world.
Maximum Likelihood Training for Score-Based Diffusion ODEs by High-Order Denoising Score Matching

Score-based generative models have excellent performance in terms of generation quality and likelihood. They model the data distribution by matching a parameterized score network with first-order data score functions. The score network can be used to define an ODE ("score-based diffusion ODE") for exact likelihood evaluation. However, the relationship between the likelihood of the ODE and the score matching objective is unclear. In this work, we prove that matching the first-order score is not sufficient to maximize the likelihood of the ODE, by showing a gap between the maximum likelihood and score matching objectives. To fill up this gap, we show that the negative likelihood of the ODE can be bounded by controlling the first, second, and third-order score matching errors; and we further present a novel high-order denoising score matching method to enable maximum likelihood training of score-based diffusion ODEs. Our algorithm guarantees that the higher-order matching error is bounded by the training error and the lower-order errors. We empirically observe that by high-order score matching, score-based diffusion ODEs achieve better likelihood on both synthetic data and CIFAR-10, while retaining the high generation quality.
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts

Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts. In this context, it can be advantageous to defer certain instances to a single human expert when they are difficult to predict for the ML model. While previous work has focused on scenarios with one distinct human expert, in many real-world situations several human experts with varying capabilities may be available. In this work, we propose an approach that trains a classification model to complement the capabilities of multiple human experts. By jointly training the classifier together with an allocation system, the classifier learns to accurately predict those instances that are difficult for the human experts, while the allocation system learns to pass each instance to the most suitable team member—either the classifier or one of the human experts. We evaluate our proposed approach in multiple experiments on public datasets with “synthetic” experts and a real-world medical dataset annotated by multiple radiologists. Our approach outperforms prior work and is more accurate than the best human expert or a classifier. Furthermore, it is flexibly adaptable to teams of varying sizes and different levels of expert diversity.
Observability of HOFNARs at SRG/eROSITA

Neutron stars can appear as sources of different nature. In this paper we address the observability of a hypothetical class of neutron stars—HOt and Fast Non-Accreting Rotators, HOFNARs. These objects are heated due to the r-mode instability. With surface temperatures ∼106 K they are expected to be thermal soft X-ray emitters. We perform a population synthesis modeling of HOFNARs to predict the number of potentially detectable sources in the eROSITA all-sky survey. For surface temperatures ∼106 K we obtain ∼500 sources above the detection limit 0.01 cts s−1 and ∼100 easier identifiable sources with >0.1 cts s−1. Temperatures ≳1.2 × 106 K start to be in contradiction with non-detection of HOFNARs by ROSAT. Only for T ≲ 5 × 105 K numbers predicted for eROSITA turn out to be so low that identification does not look possible. We conclude that eROSITA has good chances to discover HOFNARs, if they exist. Non-detection will put very stringent limits on the properties of this type of neutron stars.
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning

We study the asynchronous stochastic gradient descent algorithm for distributed training over $n$ workers which have varying computation and communication frequency over time. In this algorithm, workers compute stochastic gradients in parallel at their own pace and return those to the server without any synchronization. Existing convergence rates of this algorithm for non-convex smooth objectives depend on the maximum gradient delay $\tau_{\max}$ and show that an $\epsilon$-stationary point is reached after $\mathcal{O}\! \left(\sigma^2\epsilon^{-2}+ \tau_{\max}\epsilon^{-1}\right)$ iterations, where $\sigma$ denotes the variance of stochastic gradients. In this work (i) we obtain a tighter convergence rate of $\mathcal{O}\! \left(\sigma^2\epsilon^{-2}+ \sqrt{\tau_{\max}\tau_{avg}}\epsilon^{-1}\right)$ without any change in the algorithm where $\tau_{avg}$ is the average delay, which can be significantly smaller than $\tau_{\max}$. We also provide (ii) a simple delay-adaptive learning rate scheme, under which asynchronous SGD achieves a convergence rate of $\mathcal{O}\! \left(\sigma^2\epsilon^{-2}+ \tau_{avg}\epsilon^{-1}\right)$, and does not require any extra hyperparameter tuning nor extra communications. Our result allows to show for the first time that asynchronous SGD is always faster than mini-batch SGD. In addition, (iii) we consider the case of heterogeneous functions motivated by federated learning applications and improve the convergence rate by proving a weaker dependence on the maximum delay compared to prior works. In particular, we show that the heterogeneity term in convergence rate is only affected by the average delay within each worker.
On the Identifiability of Nonlinear ICA: Sparsity and Beyond

Nonlinear independent component analysis (ICA) aims to recover the underlying independent latent sources from their observable nonlinear mixtures. How to make the nonlinear ICA model identifiable up to certain trivial indeterminacies is a long-standing problem in unsupervised learning. Recent breakthroughs reformulate the standard independence assumption of sources as conditional independence given some auxiliary variables (e.g., class labels and/or domain/time indexes) as weak supervision or inductive bias. However, nonlinear ICA with unconditional priors cannot benefit from such developments. We explore an alternative path and consider only assumptions on the mixing process, such as Structural Sparsity. We show that under specific instantiations of such constraints, the independent latent sources can be identified from their nonlinear mixtures up to a permutation and a component-wise transformation, thus achieving nontrivial identifiability of nonlinear ICA without auxiliary variables. We provide estimation methods and validate the theoretical results experimentally. The results on image data suggest that our conditions may hold in a number of practical data generating processes.