Miracle-dz/UniFAR-FTUs huggingface dataset:
18b-glycyrrhetinic acid increases blood pressure in rats through the action on myoendothelial gap junctions

18b-glycyrrhetinic acid (GA), a pentacyclic triterpenoid found in the roots of licorice plants (Glycyrrhiza glabra), posseses high biological activity, including anti-inflammatory and antimicrobial properties. Additionally, it effectively blocks myoendothelial gap junctions. Our experiments on adult Wistar rats have shown that GA administration via gavage in the dose 0.015 g per 100 g of body weight caused a significant increase in arterial blood pressure measured both invasively and non-invasively as early as on the 7th day after beginning of the treatment. Further administration of GA up to 21 days did not change blood pressure significantly. Thoracic aortic rings obtained from GA-treated animals demonstrated a decreased ability to relax in response to acetylcholine (ACh): the maximal dilatory response and the sensitivity of the vascular preparations to ACh measured as pD2 (-log ЕС50) were significantly suppressed compared to the relaxant responses of rings from untreated rats. GA externally applied to intact tissues at a concentration of 2×10-5 M inhibited the ACh-induced relaxation. The inhibition was more pronounced than that observed in vascular rings obtained from the GA-treated animals. Nevertheless, it was smaller than that observed under combined action of the nitric oxide synthase inhibitor, L-NAME, and indomethacin, indicating that GA affects preferentially EDHF-dependent component of the ACh-induced relaxation. These results may suggest that damage of myoendothelial gap junctions that provide electrical communication between the endothelium and the smooth muscle layers may, at least partially, cause the development of arterial hypertension under GA treatment.
Full‐waveform inversion with multisource frequency selection of marine streamer data

The theory and practice of multisource full‐waveform inversion of marine supergathers are described with a frequency‐selection strategy. The key enabling property of frequency selection is that it eliminates the crosstalk among sources, thus overcoming the aperture mismatch of marine multisource inversion. Tests on multisource full‐waveform inversion of synthetic marine data and also the Gulf of Mexico data show speedups of 4 × and 8 × , respectively, compared with conventional full‐waveform inversion.
Combat Experiences, Moral Injuries, Personal Values, and Mental Health Problems among German Soldiers

Verletzungen als Mediator zwischen Einsatzerleben und psychischen Erkrankungen betrachtet. Ergebnisse: Der einsatz-bezogene Stressor «Konfrontation mit Not, Leid, Gewalt in der Bevölkerung» zeigte einen direkten signifikanten Einfluss auf psychische Erkrankungen. Moralische Verletzungen stellten eine differenziell vermittelnde Variable zwischen dem Stressor «Konfrontation mit Not, Leid, Gewalt in der Bevölkerung» und den psychischen Syndromen «posttraumatische Belastungs-störung» und «Depressivität» einerseits und Alkoholmiss-brauch andererseits dar. Keine signifikanten Zusammenhänge ergaben sich zwischen traditionellen Werthaltungen und psychischen Erkrankungen sowie zwischen traditionellen Werthaltungen und moralischen Verletzungen. Schlussfolgerung: Einsatzerlebnisse mit moralischer Dimension scheinen eine beson-ders starke Rolle in Bezug auf psychische Erkrankungen bei Bundeswehrsoldaten zu spielen. Der Effekt wird über das Kons-trukt moralischer Verletzungen vermittelt. Diese Ergebnisse können zur Weiterentwicklung präventiver und therapeutischer Maßnahmen beitragen. Schwartz’s Portrait Values Questionnaire (PVQ) were completed by 191 soldiers of the German Armed Forces at the end of their mission in Afghanistan. Direct associations of traumatic combat experiences and traditional personal values with mental health outcome were examined by regression analysis. The mediating role of moral injuries between combat experiences and mental health problems was considered by mediation analysis. Results: The stressor ‘Confrontation with hardship, suffering, and violence among the population’ had a significant, direct impact on mental health problems. Moral injuries constituted a differential mediator between the stressor ‘Confrontation with hardship, suffering, and violence among the population’ and the mental syndromes ‘post-traumatic stress disorder’ and ‘depression’ on the one hand and alcohol abuse on the other hand. No significant associations were found between traditional personal values and mental health outcome as well as traditional personal values and moral injuries. Conclusion: Combat experiences with a moral dimension seem to be associated with mental illnesses in soldiers. This effect is mediated by the construct of moral injury. These results could contribute to further developments of pre-ventive and therapeutic approaches.
EVALUATION OF ANTI-ULCER ACTIVITY OF VILLORITA CYPRINOIDES EXTRACT (BLACK WATER CLAMS) AGAINST IMMOBILIZATION STRESS INDUCED ULCER IN ALBINO RATS

Villorita cyprinoides is a black water clam that belongs to a group of the genus Villorita ; species cyprinoides (Fam:Corbiculidae) were found in the backwaters of Kerala, mainly in Vembanad backwaters. Clams are considered to be nutritious and delicious and are fished in considerable quantities in some coastal places, known to scavenge and deactivate free radicals both in vitro and in vivo . Clams were used traditionally to treat dyspepsia, piles, general debility and some skin and lung diseases. The present study was undertaken to evaluate the anti-ulcer activity of in Villorita cyprinoides extract (Black water clams) against immobilization stress induced ulcer models in Wistar rats. The extract (100 mg/kg & 200 mg/kg) showed significant (P<0.001) reduction in gastric volume, free acidity and ulcer index as compared to control. The present study revealed that Villorita cyprinoides extract (VCE) has potential anti ulcer activity in stress induced ulcer models. These results may further suggest that Villorita cyprinoides extract was found to possess antiulcerogenic as well as ulcer healing properties, which might be due to its antisecretory activity by its antioxidant mechanism.
CrowdSem 2013 : Crowdsourcing the Semantic Web

This volume contains the papers presented at the 1st International Workshop on ”Crowdsourcing the Semantic Web” that was held in conjunction with the 12th International Semantic Web Conference (ISWC 2013), 21-25 October 2013, in Sydney, Australia. This interactive workshop takes stock of the emergent work and chart the research agenda with interactive sessions to brainstorm ideas and potential applications of collective intelligence to solving AI hard semantic web problems. Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-90641 Edited Scientific Work Published Version Originally published at: CrowdSem 2013: Crowdsourcing the Semantic Web. Edited by: Acosta, Maribel; Aroyo, Lora; Bernstein, Abraham; Lehrmann, Jens; Noy, Natasha; Simperl, Elena (2013). Aachen, Germany: CEUR-WS.org.
Grounding Aleatoric Uncertainty for Unsupervised Environment Design

Adaptive curricula in reinforcement learning (RL) have proven effective for producing policies robust to discrepancies between the train and test environment. Recently, the Unsupervised Environment Design (UED) framework generalized RL curricula to generating sequences of entire environments, leading to new methods with robust minimax regret properties. Problematically, in partially-observable or stochastic settings, optimal policies may depend on the ground-truth distribution over aleatoric parameters of the environment in the intended deployment setting, while curriculum learning necessarily shifts the training distribution. We formalize this phenomenon as curriculum-induced covariate shift (CICS), and describe how its occurrence in aleatoric parameters can lead to suboptimal policies. Directly sampling these parameters from the ground-truth distribution avoids the issue, but thwarts curriculum learning. We propose SAMPLR, a minimax regret UED method that optimizes the ground-truth utility function, even when the underlying training data is biased due to CICS. We prove, and validate on challenging domains, that our approach preserves optimality under the ground-truth distribution, while promoting robustness across the full range of environment settings.
HyperDbg: Reinventing Hardware-Assisted Debugging

Software analysis, debugging, and reverse engineering have a crucial impact in today's software industry. Efficient and stealthy debuggers are especially relevant for malware analysis. However, existing debugging platforms fail to address a transparent, effective, and high-performance low-level debugger due to their detectable fingerprints, complexity, and implementation restrictions. In this paper,\footnote[2]For the extended version of this paper which includes additional appendices, refer to: https://arxiv.org/abs/2207.05676 . we present a new hypervisor-assisted debugger for high-performance and stealthy debugging of user and kernel applications. To accomplish this, HyperDbg relies on state-of-the-art hardware features available in today's CPUs, such as VT-x and Extended Page Table (EPT). In contrast to other widely used existing debuggers, we design HyperDbg using a custom hypervisor, making it independent of OS functionality or API. We propose hardware-based instruction-level emulation and OS-level API hooking via extended page tables to increase the stealthiness. Our results of the dynamic analysis of 10,853 malware samples show that HyperDbg 's stealthiness allows debugging on average 22% and 26% more samples thanWinDbg andx64dbg, respectively. Moreover, in contrast to existing debuggers, HyperDbg is not detected by any of the 13 tested packers and protectors. We improve the performance over other debuggers by deploying a VMX-compatible script engine, eliminating unnecessary context switches. Our experiment on three concrete debugging scenarios shows that compared toWinDbg as the only kernel debugger, HyperDbg performs step-in, conditional breaks, and syscall recording, 2.98x, 1319x, and 2018x faster, respectively. We finally show real-world applications, such as a 0-day analysis, structure reconstruction for reverse engineering, software performance analysis, and code-coverage analysis.
Vision Transformer for NeRF-Based View Synthesis from a Single Input Image

Although neural radiance fields (NeRF) have shown impressive advances in novel view synthesis, most methods require multiple input images of the same scene with accurate camera poses. In this work, we seek to substantially reduce the inputs to a single unposed image. Existing approaches using local image features to reconstruct a 3D object often render blurry predictions at viewpoints distant from the source view. To address this, we propose to leverage both the global and local features to form an expressive 3D representation. The global features are learned from a vision transformer, while the local features are extracted from a 2D convolutional network. To synthesize a novel view, we train a multi-layer perceptron (MLP) network conditioned on the learned 3D representation to perform volume rendering. This novel 3D representation allows the network to reconstruct unseen regions without enforcing constraints like symmetry or canonical coordinate systems. Our method renders novel views from just a single input image, and generalizes across multiple object categories using a single model. Quantitative and qualitative evaluations demonstrate that the proposed method achieves state-of-the-art performance and renders richer details than existing approaches. https://cseweb.ucsd. edu/ %7eviscomp/projects/VisionNeRF/
Topological theory of phase transitions

The investigation of the Hamiltonian dynamical counterpart of phase transitions, combined with the Riemannian geometrization of Hamiltonian dynamics, has led to a preliminary formulation of a differential-topological theory of phase transitions. In fact, in correspondence of a phase transition there are peculiar geometrical changes of the mechanical manifolds that are found to stem from changes of their topology. These findings, together with two theorems, have suggested that a topological theory of phase transitions can be formulated to go beyond the limits of the existing theories. Among other advantages, the new theory applies to phase transitions in small N systems (that is, at nanoscopic and mesoscopic scales), and in the absence of symmetry-breaking. However, the preliminary version of the theory was incomplete and still falsifiable by counterexamples. The present work provides a relevant leap forward leading to an accomplished development of the topological theory of phase transitions paving the way to further developments and applications of the theory that can be no longer hampered.
Chern-Simons Gravity and Neutrino Self-Interactions

Dynamical Chern-Simons gravity (dCS) is a four-dimensional parity-violating extension of general relativity. Current models predict the eﬀect of this extension to be negligible due to large decay constants f close to the scale of grand uniﬁed theories. Here, we present a construction of dCS allowing for much smaller decay constants, ranging from sub-eV to Planck scales. Speciﬁcally, we show that if there exists a fermion species with strong self-interactions, the short-wavelength fermion modes form a bound state. This bound state can then undergo dynamical symmetry breaking and the resulting pseudoscalar develops Yukawa interactions with the remaining long-wavelength fermion modes. Due to this new interaction, loop corrections with gravitons then realize a linear coupling between the pseudoscalar and the gravitational Chern-Simons term. The strength of this coupling is set by the Yukawa coupling constant divided by the fermion mass. Therefore, since self-interacting fermions with small masses are ideal, we identify neutrinos as promising candidates. For example, if a neutrino has a mass m ν (cid:46) meV and the Yukawa coupling is order unity, the dCS decay constant can be as small as f ∼ 10 3 m ν (cid:46) eV. We discuss other potential choices for fermions.
Transferability-Guided Cross-Domain Cross-Task Transfer Learning

We propose two novel transferability metrics fast optimal transport-based conditional entropy (F-OTCE) and joint correspondence OTCE (JC-OTCE) to evaluate how much the source model (task) can benefit the learning of the target task and to learn more generalizable representations for cross-domain cross-task transfer learning. Unlike the original OTCE metric that requires evaluating the empirical transferability on auxiliary tasks, our metrics are auxiliary-free such that they can be computed much more efficiently. Specifically, F-OTCE estimates transferability by first solving an optimal transport (OT) problem between source and target distributions and then uses the optimal coupling to compute the negative conditional entropy (NCE) between the source and target labels. It can also serve as an objective function to enhance downstream transfer learning tasks including model finetuning and domain generalization (DG). Meanwhile, JC-OTCE improves the transferability accuracy of F-OTCE by including label distances in the OT problem, though it incurs additional computation costs. Extensive experiments demonstrate that F-OTCE and JC-OTCE outperform state-of-the-art auxiliary-free metrics by 21.1% and 25.8% , respectively, in correlation coefficient with the ground-truth transfer accuracy. By eliminating the training cost of auxiliary tasks, the two metrics reduce the total computation time of the previous method from 43 min to 9.32 and 10.78 s, respectively, for a pair of tasks. When applied in the model finetuning and DG tasks, F-OTCE shows significant improvements in the transfer accuracy in few-shot classification experiments, with up to 4.41% and 2.34% accuracy gains, respectively.
WeShort: Out-of-distribution Detection With Weak Shortcut structure

Neural networks have achieved impressive performance for data in the distribution which is the same as the training set but can produce an overconfident incorrect result for the data these networks have never seen. Therefore, it is essential to detect whether inputs come from out-of-distribution(OOD) in order to guarantee the safety of neural networks deployed in the real world. In this paper, we propose a simple and effective post-hoc technique, WeShort, to reduce the overconfidence of neural networks on OOD data. Our method is inspired by the observation of the internal residual structure, which shows the separation of the OOD and in-distribution (ID) data in the shortcut layer. Our method is compatible with different OOD detection scores and can generalize well to different architectures of networks. We demonstrate our method on various OOD datasets to show its competitive performances and provide reasonable hypotheses to explain why our method works. On the ImageNet benchmark, Weshort achieves state-of-the-art performance on the false positive rate (FPR95) and the area under the receiver operating characteristic (AUROC) on the family of post-hoc methods.
A Dynamic Programming Framework for Optimal Planning of Redundant Robots Along Prescribed Paths With Kineto-Dynamic Constraints

Offline optimal planning of trajectories for redundant robots along prescribed task space paths is usually broken down into two consecutive processes: first, the task space path is inverted to obtain a joint space path, then, the latter is parametrized with a time law. If the two processes are separated, they cannot optimize the same objective function, ultimately providing sub-optimal results. In this paper, a unified approach is presented where dynamic programming is the underlying optimization technique. Its flexibility allows accommodating arbitrary constraints and objective functions, thus providing a generic framework for optimal planning of real systems. To demonstrate its applicability to a real world scenario, the framework is instantiated for time-optimality on Franka Emika's Panda robot. The well-known issues associated with the execution of non-smooth trajectories on a real controller are partially addressed at planning level, through the enforcement of constraints, and partially through post-processing of the optimal solution. The experiments show that the proposed framework is able to effectively exploit kinematic redundancy to optimize the performance index defined at planning level and generate feasible trajectories that can be executed on real hardware with satisfactory results.
RUSH: Robust Contrastive Learning via Randomized Smoothing

Recently, adversarial training has been incorporated in self-supervised contrastive pre-training to augment label efficiency with exciting adversarial robustness. However, the robustness came at a cost of expensive adversarial training. In this paper, we show a surprising fact that contrastive pre-training has an interesting yet implicit connection with robustness, and such natural robustness in the pre trained representation enables us to design a powerful robust algorithm against adversarial attacks, RUSH, that combines the standard contrastive pre-training and randomized smoothing. It boosts both standard accuracy and robust accuracy, and significantly reduces training costs as compared with adversarial training. We use extensive empirical studies to show that the proposed RUSH outperforms robust classifiers from adversarial training, by a significant margin on common benchmarks (CIFAR-10, CIFAR-100, and STL-10) under first-order attacks. In particular, under $\ell_{\infty}$-norm perturbations of size 8/255 PGD attack on CIFAR-10, our model using ResNet-18 as backbone reached 77.8% robust accuracy and 87.9% standard accuracy. Our work has an improvement of over 15% in robust accuracy and a slight improvement in standard accuracy, compared to the state-of-the-arts.
The nature of excitons in PPDT2FBT:PCBM solar cells: Role played by PCBM

In organic semiconductor-based bulk heterojunction solar cells, the presence of an acceptor increases the formation of charge-transfer (CT) excitons, thereby leading to higher exciton dissociation probabilities. In this work we used steady-state electroabsorption (EA) measurements to probe the change in the nature of excitons as the blend composition of the solar cell active layer material was varied. We investigated blends of poly[(2,5-bis(2-hexyldecyloxy)phenylene)-alt-(5,6-difluoro-4,7-di(thiophen-2-yl)benzo[c]-[1,2,5]thiadiazole)] (PPDT2FBT) and (6,6)-phenyl C71 butyric acid methyl ester (PCBM). Analysis of the EA spectra showed that in the presence of a fullerene-based acceptor, like PCBM, CT characteristics of the excitons were modified, though no new CT signature was observed in the blend. Enhancement in the CT characteristics in the blend was reflected in photoluminescence (PL) measurements of the blends, where PL quenching of ∼63% was observed for 1% PCBM. The quenching reaches saturation at about 20% PCBM. However, efficiency of the device increased with a PCBM percentage beyond 20%. Maximum efficiency was obtained for the blend with 50% PCBM, among the blend compositions studied in this work, indicating the optimum concentration of PCBM for best power conversion efficiency to be around that value. When we compared the experimental results with simulations, the variation of the device efficiency with PCBM percentage was shown to arise from multiple factors, such as an increase in the polarizability and dipole moment of excitons, and the efficiency of the carrier collection from the bulk of the active layer.
Camera Pose Auto-Encoders for Improving Pose Regression

Absolute pose regressor (APR) networks are trained to estimate the pose of the camera given a captured image. They compute latent image representations from which the camera position and orientation are regressed. APRs provide a different tradeoff between localization accuracy, runtime, and memory, compared to structure-based localization schemes that provide state-of-the-art accuracy. In this work, we introduce Camera Pose Auto-Encoders (PAEs), multilayer perceptrons that are trained via a Teacher-Student approach to encode camera poses using APRs as their teachers. We show that the resulting latent pose representations can closely reproduce APR performance and demonstrate their effectiveness for related tasks. Specifically, we propose a light-weight test-time optimization in which the closest train poses are encoded and used to refine camera position estimation. This procedure achieves a new state-of-the-art position accuracy for APRs, on both the CambridgeLandmarks and 7Scenes benchmarks. We also show that train images can be reconstructed from the learned pose encoding, paving the way for integrating visual information from the train set at a low memory cost. Our code and pre-trained models are available at https://github.com/yolish/camera-pose-auto-encoders.
On globally solving nonconvex trust region subproblem via projected gradient method

The trust region subproblem (TRS) is to minimize a possibly nonconvex quadratic function over a Euclidean ball. There are typically two cases for (TRS), the so-called ``easy case'' and ``hard case''. Even in the ``easy case'', the sequence generated by the classical projected gradient method (PG) may converge to a saddle point at a sublinear local rate, when the initial point is arbitrarily selected from a nonzero measure feasible set. To our surprise, when applying (PG) to solve a cheap and possibly nonconvex reformulation of (TRS), the generated sequence initialized with {\it any} feasible point almost always converges to its global minimizer. The local convergence rate is at least linear for the ``easy case'', without assuming that we have possessed the information that the ``easy case'' holds. We also consider how to use (PG) to globally solve equality-constrained (TRS).