Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Stimulus–Response Compatibility on Cortical Motor Activation

Stimulus-response compatibility (SRC) is a general term describing the relationship between a triggering stimulus and its associated motor response. The relationship between stimulus and response can be manipulated at the level of the set of stimulus and response characteristics (set-level) or at the level of the mapping between the individual elements of the stimulus and response sets (element-level). We used functional magnetic resonance imaging (fMRI) to investigate the effects of SRC on functional activation in cortical motor areas. Using behavioral tasks to separately evaluate set- and element-level compatibility, and their interaction, we measured the volume of functional activation in 11 cortical motor areas, in the anterior frontal cortex, and in the superior temporal lobe. Element-level compatibility effects were associated with significant activation in the pre-supplementary motor area (preSMA), the dorsal (PMd) and ventral (PMv) premotor areas, and the parietal areas (inferior, superior, intraparietal sulcus, precuneus). The activation was lateralized to the right hemisphere for most of the areas. Set-level compatibility effects resulted in significant activation in the inferior frontal gyri, anterior cingulate and cingulate motor areas, the PMd, PMv, preSMA, the parietal areas (inferior, superior, intraparietal sulcus, precuneus), and in the superior temporal lobe. Activation in the majority of these areas was lateralized to the left hemisphere. Finally, there was an interaction between set and element-level compatibility in the middle and superior frontal gyri, in an area co-extensive with the dorsolateral prefrontal cortex, suggesting that this area provided the neural substrate for common processing stages, such as working memory and attention, which are engaged when both levels of SRC are manipulated at once.
Harnessing Object and Scene Semantics for Large-Scale Video Understanding

Large-scale action recognition and video categorization are important problems in computer vision. To address these problems, we propose a novel object-and scene-based semantic fusion network and representation. Our semantic fusion network combines three streams of information using a three-layer neural network: (i) frame-based low-level CNN features, (ii) object features from a state-of-the-art large-scale CNN object-detector trained to recognize 20K classes, and (iii) scene features from a state-of-the-art CNN scene-detector trained to recognize 205 scenes. The trained network achieves improvements in supervised activity and video categorization in two complex large-scale datasets - ActivityNet and FCVID, respectively. Further, by examining and back propagating information through the fusion network, semantic relationships (correlations) between video classes and objects/scenes can be discovered. These video class-object/video class-scene relationships can in turn be used as semantic representation for the video classes themselves. We illustrate effectiveness of this semantic representation through experiments on zero-shot action/video classification and clustering.
Fibrinolytic treatment of ST-elevation myocardial infarction

Summary Primary percutaneous coronary intervention (PPCI) is the preferred reperfusion therapy in ST-elevation myocardial infarction (STEMI), as long as it can be delivered within 90-120 minutes from patient’s first medical contact, and is the leading reperfusion strategy in most European countries. However, as PPCI cannot be offered in a timely manner to all patients, fibrinolytic therapy (FT) is the recommended choice in patients with an anticipated delay to PPCI of >90-120 minutes, presenting early after symptom onset and without contra-indications. FT should preferably be started in the pre-hospital setting. Following FT, all patients should be transferred to a PCI-center for rescue PCI or routine coronary angiography with PCI as indicated. Such a pharmaco-invasive strategy, combining FT with invasive treatment, has recently been shown to be non-inferior to PPCI in patients living in areas with long transfer delays to PCI (>60 minutes). In this overview, we will briefly present the evidence for the benefit of FT in STEMI, and discuss the role of FT in the current era of PPCI as well as the optimal treatment following pharmacologic reperfusion.
Serotonin Depletion In Vivo Inhibits the Branching of Olfactory Projection Neurons in the Lobster Deutocerebrum

Serotonin depletion during embryogenesis has been shown previously to retard the growth of the olfactory and accessory lobes of the lobster deutocerebrum (Benton et al., 1997). The present study was undertaken to determine whether morphological changes in the interneurons innervating these lobes contribute to this growth retardation. We examined the effects of in vivoserotonin depletion using 5,7-dihydroxytryptamine (5,7-DHT) on the morphology of the olfactory projection neurons, one of two major classes of interneurons that innervate both lobes. Intracellular dye fills of olfactory projection neurons in normal embryos showed that each neuron extensively innervates either the olfactory or accessory lobe before projecting to neuropil regions in the protocerebrum. In embryos injected with 5,7-DHT, however, the deutocerebral arbors of 13.5% of the olfactory projection neurons examined were either markedly reduced compared with normal neurons or absent. Affected neurons also exhibited a number of additional aberrant morphological features suggesting that these neurons represent cells that were affected during their initial morphogenesis. Olfactory projection neurons with aberrant morphologies were also encountered, although less frequently (7.5% of the neurons examined), in control (sham-injected) embryos indicating that the sham injections can affect the development of the brain. This observation provides insights into the nature of effects seen in control embryos in previous experiments (Benton et al., 1997). The results of the present study indicate that in vivo serotonin depletion inhibits the branching of olfactory projection neurons and suggest, therefore, that one of the functions of serotonin during normal development is to promote the ingrowth of these neurons into the deutocerebral neuropils.
Large N Dynamics of Dimensionally Reduced 4D SU(N) Super Yang-Mills Theory

We perform Monte Carlo simulations of a supersymmetric matrix model, which is obtained by dimensional reduction of 4D SU(N) super Yang-Mills theory. The model can be considered as a four-dimensional counterpart of the IIB matrix model. We extract the space-time structure represented by the eigenvalues of bosonic matrices. In particular we compare the large N behavior of the space-time extent with the result obtained from a low energy effective theory. We measure various Wilson loop correlators which represent string amplitudes and we observe a nontrivial universal scaling in N . We also observe that the Eguchi-Kawai equivalence to ordinary gauge theory does hold at least within a finite range of scale. Comparison with the results for the bosonic case clarifies the rôle of supersymmetry in the large N dynamics. It does affect the multi-point correlators qualitatively, but the Eguchi-Kawai equivalence is observed even in the bosonic case. e-mail address : ambjorn@nbi.dk e-mail address : konstant@kiritsis.physics. uoc.gr e-mail address : bietenho@nordita.dk e-mail address : hotta@hep1.c. u-tokyo.ac. jp ¶ Permanent address : Department of Physics, Nagoya University, Nagoya 464-8602, Japan, e-mail address : nisimura@nbi.dk
Assigning XTAG Trees to VerbNet

This paper presents the mappings between the syntactic information of our broad-coverage domain-independent verb lexicon, VerbNet, to Xtag trees. This mapping between complementary resources allowed us to increase the syntactic coverage of our verb lexicon by capturing transformations of the basic syntactic description of the verbs present in VerbNet. In addition, having these two resources mapped allows the semantic predicates present in our lexicon to be used to disambiguate Xtag verb senses.
Selection and the effect of temperature on scutellar bristle number in Drosophila.

ELECTION of a genotype which gives rise to similar phenotypes in different environments is one evolutionary strategy which may result in high adaptedness of a population to the environments in which it lives. Presumably, the stability of certain traits is the outcome of a long history of natural selection tending towards an adaptive phenotype. Several investigators have attempted to select strains of Drosophila for phenotypic stability of variable traits (FALCONER 1957; THODAY 1959,1960; RENDEL and SHELDON 1960; PROUT 1962; SCHARLOO 1964). Interestingly, the stability of a trait with respect to small (microenvironmental) and to more drastic (macroenvironmental) variations generally seemed to be independent. When selection reduced phenotypic variance in a given environment, responsiveness to macroenvironmental variables was not affected. Likewise, selection for decreased responsiveness to major environmental differences did not increase microenvironmental buffering ( WADDINGTON 1960; KINDRED 1965). RENDEL and SHELDON (1960) obtained different results. They succeeded in reducing the phenotypic variance of scutellar bristle number around a mean of two bristles in scute Drosophila. This reduction in variance at 25°C was accompanied by lowered sensitivity of bristle number to temperature changes. The experiments reported in the present article involved selection for altered temperature responsiveness of scutellar bristle number and were designed to study the relation between microenvironmental and macroenvironmental buffering. These experiments are in some ways similar to those of KINDRED (1965) although they were done independently.
On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond

This paper aims to clearly distinguish between Stochastic Gradient Descent with Momentum (SGDM) and Adam in terms of their convergence rates. We demonstrate that Adam achieves a faster convergence compared to SGDM under the condition of non-uniformly bounded smoothness. Our findings reveal that: (1) in deterministic environments, Adam can attain the known lower bound for the convergence rate of deterministic first-order optimizers, whereas the convergence rate of Gradient Descent with Momentum (GDM) has higher order dependence on the initial function value; (2) in stochastic setting, Adam's convergence rate upper bound matches the lower bounds of stochastic first-order optimizers, considering both the initial function value and the final error, whereas there are instances where SGDM fails to converge with any learning rate. These insights distinctly differentiate Adam and SGDM regarding their convergence rates. Additionally, by introducing a novel stopping-time based technique, we further prove that if we consider the minimum gradient norm during iterations, the corresponding convergence rate can match the lower bounds across all problem hyperparameters. The technique can also help proving that Adam with a specific hyperparameter scheduler is parameter-agnostic, which hence can be of independent interest.
Effects of Azithromycin, Metronidazole, Amoxicillin, and Metronidazole plus Amoxicillin on an In Vitro Polymicrobial Subgingival Biofilm Model

ABSTRACT Chronic periodontitis is one of the most prevalent human diseases and is caused by dysbiosis of the subgingival microbiota. Treatment involves primarily mechanical disruption of subgingival biofilms and, in certain cases, adjunctive use of systemic antibiotic therapy. In vitro biofilm models have been developed to study antimicrobial agents targeting subgingival species. However, these models accommodate a limited number of taxa, lack reproducibility, and have low throughput. We aimed to develop an in vitro multispecies biofilm model that mimics subgingival plaque, to test antimicrobial agents. Biofilms were cultivated using the Calgary Biofilm Device and were exposed to amoxicillin (AMX), metronidazole (MTZ), azithromycin (AZM), and AMX-MTZ at four different concentrations for 12, 24, or 36 h. Chlorhexidine (CHX) (0.12%) was used as the positive control. The compositions of the biofilms were analyzed by checkerboard DNA-DNA hybridization, and the percent reduction in biofilm metabolic activity was determined using 2,3,5-triphenyltetrazolium chloride and spectrophotometry. Thirty-five of the 40 species used in the inoculum were consistently recovered from the resulting in vitro biofilms. After 36 h of exposure at the 1:27 dilution, AMX-MTZ reduced metabolic activity 11% less than CHX (q = 0.0207) but 54% more than AMX (q = 0.0031), 72% more than MTZ (q = 0.0031), and 67% more than AZM (q = 0.0008). Preliminary evidence of a synergistic interaction between AMX and MTZ was also observed. In summary, we developed reproducible biofilms with 35 subgingival bacterial species, and our results suggested that the combination of AMX and MTZ had greater antimicrobial effects on these in vitro multispecies biofilms than expected on the basis of the independent effects of the drugs.
Model order reduction of deep structured state-space models: A system-theoretic approach

With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification. The recently introduced deep structured state-space models (SSM), which feature linear dynamical blocks as key constituent components, offer high predictive performance. However, the learned representations often suffer from excessively large model orders, which render them unsuitable for control design purposes. The current paper addresses this challenge by means of system-theoretic model order reduction techniques that target the linear dynamical blocks of SSMs. We introduce two regularization terms which can be incorporated into the training loss for improved model order reduction. In particular, we consider modal $\ell_1$ and Hankel nuclear norm regularization to promote sparsity, allowing one to retain only the relevant states without sacrificing accuracy. The presented regularizers lead to advantages in terms of parsimonious representations and faster inference resulting from the reduced order models. The effectiveness of the proposed methodology is demonstrated using real-world ground vibration data from an aircraft.
Impact of Azimuth Ambiguities on Interferometric Performance

The impact of azimuth ambiguities on interferometric performance in terms of phase bias and standard deviation of the interferometric phase is analyzed, resorting to the interferogram statistics for jointly circular Gaussian processes. The theoretical results are validated through simulation and compared with measurements on a TanDEM-X interferogram, affected by azimuth ambiguities.
A numerical framework for phoretic particles

We develop a numerical a framework to study phoretic particle dynamics in two dimensions. The particles are modeled as chemically active rigid circles, which can emit or absorb a solute into surrounding fluid. The interaction between particles and solute induces a slip flow on particle surfaces, and the solute is advected by the fluid flow and diffuses with a constant diffusivity. The fluid-structure interaction is resolved by a boundary integral method accelerated by Ewald-like decomposition. The sharp resolution of moving boundaries for solute kinetics is performed thanks to an overlapping mesh method. The framework is validated separately for the Stokes problem and the advection--diffusion problem, reaching relatively high order of accuracy. Moreover, we employ the framework to more general problems, including particles in nearly infinite domain and straight channels, and multiparticle motions.
DragAPart: Learning a Part-Level Motion Prior for Articulated Objects

We introduce DragAPart, a method that, given an image and a set of drags as input, can generate a new image of the same object in a new state, compatible with the action of the drags. Differently from prior works that focused on repositioning objects, DragAPart predicts part-level interactions, such as opening and closing a drawer. We study this problem as a proxy for learning a generalist motion model, not restricted to a specific kinematic structure or object category. To this end, we start from a pre-trained image generator and fine-tune it on a new synthetic dataset, Drag-a-Move, which we introduce. Combined with a new encoding for the drags and dataset randomization, the new model generalizes well to real images and different categories. Compared to prior motion-controlled generators, we demonstrate much better part-level motion understanding.
SiMBA: Simplified Mamba-Based Architecture for Vision and Multivariate Time series

Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains. However, recent literature highlights issues with attention networks, including low inductive bias and quadratic complexity concerning input sequence length. State Space Models (SSMs) like S4 and others (Hippo, Global Convolutions, liquid S4, LRU, Mega, and Mamba), have emerged to address the above issues to help handle longer sequence lengths. Mamba, while being the state-of-the-art SSM, has a stability issue when scaled to large networks for computer vision datasets. We propose SiMBA, a new architecture that introduces Einstein FFT (EinFFT) for channel modeling by specific eigenvalue computations and uses the Mamba block for sequence modeling. Extensive performance studies across image and time-series benchmarks demonstrate that SiMBA outperforms existing SSMs, bridging the performance gap with state-of-the-art transformers. Notably, SiMBA establishes itself as the new state-of-the-art SSM on ImageNet and transfer learning benchmarks such as Stanford Car and Flower as well as task learning benchmarks as well as seven time series benchmark datasets. The project page is available on this website ~\url{https://github.com/badripatro/Simba}.
BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation

3D instance segmentation (3DIS) is a crucial task, but point-level annotations are tedious in fully supervised settings. Thus, using bounding boxes (bboxes) as annotations has shown great potential. The current mainstream approach is a two-step process, involving the generation of pseudo-labels from box annotations and the training of a 3DIS network with the pseudo-labels. However, due to the presence of intersections among bboxes, not every point has a determined instance label, especially in overlapping areas. To generate higher quality pseudo-labels and achieve more precise weakly supervised 3DIS results, we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (BSNet), which devises a novel pseudo-labeler called Simulation-assisted Transformer. The labeler consists of two main components. The first is Simulation-assisted Mean Teacher, which introduces Mean Teacher for the first time in this task and constructs simulated samples to assist the labeler in acquiring prior knowledge about overlapping areas. To better model local-global structure, we also propose Local-Global Aware Attention as the decoder for teacher and student labelers. Extensive experiments conducted on the ScanNetV2 and S3DIS datasets verify the superiority of our designs. Code is available at \href{https://github.com/peoplelu/BSNet}{https://github.com/peoplelu/BSNet}.
EphA2 receptor tyrosine kinase regulates endothelial cell migration and vascular assembly through phosphoinositide 3-kinase-mediated Rac1 GTPase activation

Angiogenesis is critical for vascular remodeling during development and contributes to the pathogenesis of diseases such as cancer. Targeted disruption of several EphB class receptor tyrosine kinases results in vascular remodeling defects during embryogenesis. The role of EphA class receptors in vascular remodeling, however, is not well-characterized. We recently demonstrated that global inhibition of EphA receptors disrupts endothelial migration induced by ephrin, VEGF or tumor-derived signals, though the specific target remained undefined. Here, we report that EphA2 regulates endothelial cell assembly and migration through phosphoinositide (PI) 3-kinase-mediated activation of Rac1 GTPase in two model systems: primary bovine and murine pulmonary microvascular endothelial cells. EphA2-deficient endothelial cells fail to undergo vascular assembly and migration in response to ephrin-A1 in vitro. Ephrin-A1 stimulation induces PI3-kinase-dependent activation of Rac1 in wild-type endothelial cells, whereas EphA2-deficient cells fail to activate Rac1 upon stimulation. Expression of dominant negative PI3-kinase or Rac1 inhibits ephrin-A1-induced endothelial cell migration. Consistent with in vitro data, EphA2-deficient mice show a diminished angiogenic response to ephrin-A1 in vivo. Moreover, EphA2-deficient endothelial cells fail to assemble in vivo when transplanted into recipient mice. These data suggest that EphA2 is an essential regulator of post-natal angiogenesis.
Relationship between Obstructive Sleep Apnea Severity and Sleep, Depression and Anxiety Symptoms in Newly-Diagnosed Patients

Obstructive sleep apnea (OSA) occurs in at least 10% of the population, and leads to higher morbidity and mortality; however, relationships between OSA severity and sleep or psychological symptoms are unclear. Existing studies include samples with wide-ranging comorbidities, so we assessed relationships between severity of OSA and common sleep and psychological disturbances in recently diagnosed OSA patients with minimal co-morbidities. We studied 49 newly diagnosed, untreated OSA patients without major co-morbidities such as mental illness, cardiovascular disease, or stroke; subjects were not using psychoactive medications or tobacco (mean ± std age: 46.8±9.1 years; apnea/hyponea index [AHI]: 32.1±20.5 events/hour; female/male: 12/37; weight <125 kg). We evaluated relationships between the AHI and daytime sleepiness (Epworth Sleepiness Scale; ESS), sleep quality (Pittsburg Sleep Quality Index; PSQI), depressive symptoms (Beck Depression Inventory-II; BDI), and anxiety symptoms (Beck Anxiety Inventory; BAI), as well as sex and body mass index (BMI). AHI was similar in females and males. Mean levels of all symptoms were above normal thresholds, but AHI was not correlated with age, ESS, PSQI, BDI, or BAI; only BMI was correlated with OSA severity. No differences in mean AHI appeared when subjects were grouped by normal versus elevated values of ESS, PSQI, BDI, or BAI. Consistent with other studies, a strong link between OSA severity and psychological symptoms did not appear in these newly diagnosed patients, suggesting that mechanisms additional to the number and frequency of hypoxic events and arousals occurring with apneas contribute to adverse health effects in OSA. OSA patients presenting with mild or moderate severity, and no major co-morbidities will not necessarily have low levels of sleep or psychological disturbances.