Miracle-dz/UniFAR-FTUs huggingface dataset:
NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis

We address the problem of generating realistic 3D motions of humans interacting with objects in a scene. Our key idea is to create a neural interaction field attached to a specific object, which outputs the distance to the valid interaction manifold given a human pose as input. This interaction field guides the sampling of an object-conditioned human motion diffusion model, so as to encourage plausible contacts and affordance semantics. To support interactions with scarcely available data, we propose an automated synthetic data pipeline. For this, we seed a pre-trained motion model, which has priors for the basics of human movement, with interaction-specific anchor poses extracted from limited motion capture data. Using our guided diffusion model trained on generated synthetic data, we synthesize realistic motions for sitting and lifting with several objects, outperforming alternative approaches in terms of motion quality and successful action completion. We call our framework NIFTY: Neural Interaction Fields for Trajectory sYnthesis.
Multiplicative update rules for accelerating deep learning training and increasing robustness

Even nowadays, where Deep Learning (DL) has achieved state-of-the-art performance in a wide range of research domains, accelerating training and building robust DL models remains a challenging task. To this end, generations of researchers have pursued to develop robust methods for training DL architectures that can be less sensitive to weight distributions, model architectures and loss landscapes. However, such methods are limited to adaptive learning rate optimizers, initialization schemes, and clipping gradients without investigating the fundamental rule of parameters update. Although multiplicative updates have contributed significantly to the early development of machine learning and hold strong theoretical claims, to best of our knowledge, this is the first work that investigate them in context of DL training acceleration and robustness. In this work, we propose an optimization framework that fits to a wide range of optimization algorithms and enables one to apply alternative update rules. To this end, we propose a novel multiplicative update rule and we extend their capabilities by combining it with a traditional additive update term, under a novel hybrid update method. We claim that the proposed framework accelerates training, while leading to more robust models in contrast to traditionally used additive update rule and we experimentally demonstrate their effectiveness in a wide range of task and optimization methods. Such tasks ranging from convex and non-convex optimization to difficult image classification benchmarks applying a wide range of traditionally used optimization methods and Deep Neural Network (DNN) architectures.
Efficient goal-oriented global errorestimation for BDF-type methods usingdiscrete adjoints

This thesis develops estimation techniques for the global error that occurs during the approximation of solutions of Initial Value Problems (IVPs) on given intervals by multistep integration methods based on Backward Differentiation Formulas (BDF). To this end, discrete adjoints obtained by adjoint Internal Numerical Differentiation (IND) of the nominal integration scheme are used. For this purpose, a bridge between BDF methods and Petrov-Galerkin Finite Element (FE) methods is built by a novel functional-analytic framework. Goal-oriented global error estimators are derived in analogy to the Dual Weighted Residual methodology in Galerkin methods for Partial Differential Equations. Their asymptotic behavior, their accuracy in BDF methods with variable order and stepsize as well as their applicability for global error control are investigated. The novel results presented in this thesis include: i) a functional-analytic framework for IVPs in Ordinary Differential Equations (ODEs) in the Banach space of continuously differentiable functions. This framework is needed since the classical Hilbert space setting is not suitable to analyze the relation between the discrete values of the adjoint IND scheme and the solution of the adjoint IVP. The new framework gives rise to the definition of weak solutions of adjoint IVPs. ii) a Petrov-Galerkin FE discretization of the function spaces that allows to transform the variational formulations of the IVP and of its adjoint IVP into finite dimensional problems. The equivalence of these finite dimensional problems to BDF methods with variable but prescribed order and stepsize and their adjoint IND schemes is shown. Thus, the FE approximation of the weak adjoint is determined by the discrete values of the adjoint IND scheme and discretization and differentiation commute in the developed framework. iii) a proof that the values of the adjoint IND scheme corresponding to a BDF method with constant order and stepsize converge to the solution of the adjoint IVP on the open interval. In addition, a proof is given that demonstrates the convergence of the FE approximation to the weak solution of the adjoint IVP on the entire interval. iv) goal-oriented global error estimators for BDF methods that weight, for each integration step, a local error quantity with the corresponding value of the adjoint IND scheme and yields in sum an accurate and efficient estimate for the actual error. As local error quantity defect integrals and local truncation errors are employed, respectively. v) strategies for goal-oriented global error control in BDF methods that either adapt the locally acting relative tolerance or the given integration scheme using the stepwise error indicators. vi) an ODE model of an exothermic, self-accelerating chemical reaction with mass transfer carried out in a discontinuous Stirred Tank Reactor. With this real-world example from chemical engineering the applicability and reliability of the novel techniques for the approximation of weak adjoints and for the simulation with goal-oriented global error control are shown.
On the dynamic asymptotic dimension of \'etale groupoids

We investigate the dynamic asymptotic dimension for \'etale groupoids introduced by Guentner, Willett and Yu. In particular, we establish several permanence properties, including estimates for products and unions of groupoids. We also establish invariance of the dynamic asymptotic dimension under Morita equivalence. In the second part of the article, we consider a canonical coarse structure on an \'etale groupoid and compare the asymptotic dimension of the resulting coarse space with the dynamic asymptotic dimension of the underlying groupoid.
A simple deterministic near-linear time approximation scheme for transshipment with arbitrary positive edge costs

We describe a simple deterministic near-linear time approximation scheme for uncapacitated minimum cost flow in undirected graphs with real edge weights, a problem also known as transshipment. Specifically, our algorithm takes as input a (connected) undirected graph $G = (V, E)$, vertex demands $b \in \mathbb{R}^V$ such that $\sum_{v \in V} b(v) = 0$, positive edge costs $c \in \mathbb{R}_{>0}^E$, and a parameter $\varepsilon>0$. In $O(\varepsilon^{-2} m \log^{O(1)} n)$ time, it returns a flow $f$ such that the net flow out of each vertex is equal to the vertex's demand and the cost of the flow is within a $(1 + \varepsilon)$ factor of optimal. Our algorithm is combinatorial and has no running time dependency on the demands or edge costs. With the exception of a recent result presented at STOC 2022 for polynomially bounded edge weights, all almost- and near-linear time approximation schemes for transshipment relied on randomization in two main ways: 1) to embed the problem instance into low-dimensional space and 2) to randomly pick target locations to send flow so nearby opposing demands can be satisfied. Our algorithm instead deterministically approximates the cost of routing decisions that would be made if the input were subject to a random tree embedding. To avoid computing the $\Omega(n^2)$ vertex-vertex distances that an approximation of this kind suggests, we also limit the available routing decisions using distances explicitly stored in the well-known Thorup-Zwick distance oracle.
SummaryMixing: A Linear-Complexity Alternative to Self-Attention for Speech Recognition and Understanding

Modern speech processing systems rely on self-attention. Unfortunately, token mixing with self-attention takes quadratic time in the length of the speech utterance, slowing down inference as well as training and increasing memory consumption. Cheaper alternatives to self-attention for ASR have been developed, but they fail to consistently reach the same level of accuracy. This paper, therefore, proposes a novel linear-time alternative to self-attention. It summarises an utterance with the mean over vectors for all time steps. This single summary is then combined with time-specific information. We call this method"SummaryMixing". Introducing SummaryMixing in state-of-the-art ASR models makes it feasible to preserve or exceed previous speech recognition performance while lowering the training and inference times by up to 28$\%$ and reducing the memory budget by a factor of two. The benefits of SummaryMixing can also be generalized to other speech-processing tasks, such as speech understanding.
Graph Positional and Structural Encoder

Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node ordering. This renders PSEs essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for a variety of graph prediction tasks is a challenging and unsolved problem. Here, we present the graph positional and structural encoder (GPSE), a first-ever attempt to train a graph encoder that captures rich PSE representations for augmenting any GNN. GPSE can effectively learn a common latent representation for multiple PSEs, and is highly transferable. The encoder trained on a particular graph dataset can be used effectively on datasets drawn from significantly different distributions and even modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly improve the performance in certain tasks, while performing on par with those that employ explicitly computed PSEs in other cases. Our results pave the way for the development of large pre-trained models for extracting graph positional and structural information and highlight their potential as a viable alternative to explicitly computed PSEs as well as to existing self-supervised pre-training approaches.
Structural, chemical, and dynamical trends in graphene grain boundaries

Grain boundaries are topological defects that often have a disordered character. Disorder implies that understanding general trends is more important than accurate investigations of individual grain boundaries. Here we present trends in the grain boundaries of graphene. We use density-functional tight-binding method to calculate trends in energy, atomic structure (polygon composition), chemical reactivity (dangling bond density), corrugation heights (inflection angles), and dynamical properties (vibrations), as a function of lattice orientation mismatch. The observed trends and their mutual interrelations are plausibly explained by structure, and supported by past experiments.
Understanding AKT-mediated chemoresistance: the relationship between ion channels and AKT activation

Overcoming chemoresistance is a challenge for multiple chemotherapeutics agents like cisplatin. ABC transporters such as MDR1 or MRPs and PI3K/AKT pathway have been proposed as actors of chemoresistance in several cancers. In this review we describe two downstream targets of Akt: c-myc and p53 in the chemoresistance. We suggest a potential link between p53, c-myc and ABC transporters expression. Consequently a link between Akt and ABC transporters-mediated chemoresistance may exist. Finally we show that Akt activation may be Orai-dependent and/or TRPC-dependent, suggesting that these ion channels could constitute a therapeutic target in cancer.
Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation

Heterogeneous Face Recognition (HFR) aims to match face images across different domains, such as thermal and visible spectra, expanding the applicability of Face Recognition (FR) systems to challenging scenarios. However, the domain gap and limited availability of large-scale datasets in the target domain make training robust and invariant HFR models from scratch difficult. In this work, we treat different modalities as distinct styles and propose a framework to adapt feature maps, bridging the domain gap. We introduce a novel Conditional Adaptive Instance Modulation (CAIM) module that can be integrated into pre-trained FR networks, transforming them into HFR networks. The CAIM block modulates intermediate feature maps, to adapt the style of the target modality effectively bridging the domain gap. Our proposed method allows for end-to-end training with a minimal number of paired samples. We extensively evaluate our approach on multiple challenging benchmarks, demonstrating superior performance compared to state-of-the-art methods. The source code and protocols for reproducing the findings will be made publicly available.
KINEMATIC ANALYSIS OF RESIST-AND-RELEASE SPRINT RUNNING

The objective of this study was to compare the kinematic characteristics of athlete's movements at the resistance and release phases of resist-and-release sprint running and free maximal sprint running. Five female sprinters and heptathletes and three male sprinters took part in the study. Videotaping was done at 240 Hz. 2D video analysis was performed using SkillSpector software. It was found that horizontal takeoff velocity, step length, ground contact time, knee angle at touchdown and trunk tilt at touchdown and takeoff had significant differences between release and resistance phases of resist-andrelease sprint running, along with constant speed phase of free sprint running. Most studied characteristics were the same in both the release phase of resist-and-release sprint running and in the acceleration phase of free sprint running.
Delinquent by the Dozen: Youth From Larger Families Engage in More Delinquency—Fact or Myth?

Studies suggest that there is a linear and positive relationship between family size and delinquency. However, questions exist about this assessment. Drawing on prior scholarship and analyses, we revisit and test the family size-delinquency relationship. We also test whether the effect varies by sibling relatedness. Results indicate no consistent or large criminogenic effect of larger families on delinquency and that the relationship is considerably more complicated than what prior work has identified. Some beneficial effects are identified for children in full biological households, some criminogenic effects are identified for children from non-full biological households, and the relationships are curvilinear. Findings underscore the need to revisit research on family size and delinquency and to take into account sibling relatedness.
Comparative analysis of preinduction cervical ripening and induction of labour in a Polish and German hospital

Abstract Introduction: The aim of preinduction is to accelerate cervical ripening, which results in a reduction in the number of cesarean deliveries and unsuccessful inductions, and shortens the hospitalization time for both women and infants. Induction of labour is an obstetric procedure which involves the stimulation of contractions prior to the spontaneous onset of labour. Materials and methods: The research was conducted retrospectively and involved the analysis of medical records of 80 pregnant women staying in the Clinic of Obstetrics and Gynecology in the city of Szczecin, Poland, and 117 pregnant women hospitalized in the Clinic of Obstetrics and Gynecology in the town of Schwedt/Oder, Germany. Results: The most common indications for preinduction cervical ripening and induction of labour in the Polish and German hospitals were oligohydramnios, macrosomia, suspected placental insufficiency, pregnancy between 41 + 0 and 41 + 6 weeks of gestation, intrauterine growth restriction and premature rupture of the membranes. The main indications for preinduction cervical ripening and induction of labour included premature rupture of the membranes (Polish hospital) and pregnancy between 41 + 0 and 41 + 6 weeks of pregnancy (German hospital). In the German hospital, there were more cases of preinduction and induction resulting in vaginal delivery – 85.47%, whereas in the Polish hospital the percentage was lower at 68.75%. The number of cesarean deliveries was substantially higher in the Polish hospital (31.25%) than in the German hospital (14.53%). In the Polish hospital, the average duration from the moment of applying a method to the onset of regular contractions was 113 min shorter than in the German hospital. The average duration of vaginal delivery for women in the German hospital, totalled 236 min, which was 42 min shorter than for women at the Polish hospital. The largest percentage of scores on the Apgar scale at the first, third and fifth min after birth was within the normal range and indicated good health conditions of infants with scores ranging from 8–10 points in both hospitals. Significantly more infants in the German hospital received pH from the umbilical cord within the limits of the norm which marks the welfare of neonates (7.20–7.45). The average change in the evaluation of the cervix marked in the Bishop score was higher in the German hospital, making 3.2 points, compared to 0.7 points in the Polish hospital. The duration of hospitalisation in the Polish hospital was shorter than that in the German hospital (3.4 days, with a median of 3 days). The number of days of preinduction and induction was shorter in Poland (1.2 day). The levels of haemoglobin were comparable in the case of the patients from both hospitals. Among the postpartum complications, cervical rupture and episiotomy were significantly more common in the Polish hospital, while second-degree rupture of the perineum in the German hospital.
Peculiarities of some candidate gene polymorphisms in Parkinson’s disease

Abstract Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder among elderly individuals, after Alzheimer’s disease. This complex disorder manifests with multiple motor and non-motor features and is often diagnosed only after the onset of significant pathological symptoms. Based on the presumption that epigenetic mechanisms are involved in neurodevelopment and synaptic transmission, it is hypothesized that epigenetic alterations are closely connected to the development of PD. Several studies have reported that genetic risk factors are involved in PD susceptibilities. These factors play a prominent role in the predisposition and development of PD and may be closely linked to different metabolic pathways. In this review, we revise associations between single nucleotide polymorphisms (SNPs) and PD pathogenesis.
Sensitivity and validation of porous membrane electrical cell substrate impedance spectroscopy (PM-ECIS) for measuring endothelial barrier properties

Conventional trans-endothelial electrical resistance (TEER) setups are invasive and cannot directly measure monolayer integrity in co-culture. These limitations are addressed by porous membrane electrical cell-substrate impedance sensing (PM-ECIS), which measures barrier integrity in cell monolayers grown directly on permeable membranes patterned with electrodes. Here we advance the design and utility of PM-ECIS by investigating its sensitivity to working electrode size and correlation to TEER. Gold electrodes were fabricated on porous membrane inserts using hot embossing and UV lithography, with working electrode diameters of 250, 500, and 750 µm within the same insert. Frequency scans of confluent primary endothelial monolayers showed normalized resistance peaking at 4 kHz, consistent with traditional solid substrate ECIS. Sensitivity to resistance changes (4 kHz) during endothelial barrier formation was inversely proportional to electrode size, with the smallest electrodes being the most sensitive (p<0.001). Similarly, smaller electrodes were most sensitive to changes in impedance (40 kHz) corresponding to cell spreading and proliferation (p<0.001). Barrier disruption with EGTA was detectable by 250 and 750 µm (p<0.01), and 500 µm electrodes (p=0.058). Resistances measured by PM-ECIS vs. TEER for sodium chloride solutions were positively and significantly correlated for all electrode sizes (r>0.9; p<0.0001), but only with 750 µm electrodes for endothelial monolayers (r=0.71; p=0.058). These data inform design and selection of PM-ECIS electrodes for specific applications, and support PM-ECIS as a promising alternative to conventional TEER due to its capacity for direct, non-invasive, and real-time assessment of cells cultured on porous membranes.
Comparison of Clinico-Pathological Features Between Epithelial Ovarian Cancer Patients With and Without Endometriosis: A Cross-Sectional Study

Objectives: Women with endometriosis have a high risk of developing ovarian carcinoma that may occur due to endometriosis lesions. There is few research have so far focused on the clinical factors in patients with endometriosis-associated ovarian cancer (EAOC). Accordingly, this study aimed at comparing the demographic and obstetric characteristics between ovarian cancer with and without endometriosis Materials and Methods: This cross-sectional study was conducted on 20 EAOC patients and 140 non-EAOC individuals who had gone under surgery from 2011-17 at Al-Zahra hospital. Clinico-pathological characteristics of the two groups including first group only had malignant epithelial ovarian tumor (non-EAOC) and second group had both malignant epithelial ovarian tumor and endometriosis (EAOC). P value less than 0.05 was considered statistically significant. Results: EAOC cases were significantly younger (P=0.002) and had lower number of pregnancy (P=0.002), parity (P=0.004), and term pregnancy (P=0.005) than non-EAOC patients. A large proportion of EAOC cases had clear cell and endometrioid histopathology in comparison to non-EAOC individuals (P<0.001) and most of the tumors in these cases were unilateral (P=0.01). Conclusions: We found that age, parity, gravidity, and term pregnancy as well as laterality and histopathologic type of epithelial ovarian cancers vary in EAOC and non-EAOC individuals. Further research is required to identify these differences.
Shear horizontal waves in a nonlinear elastic layer overlying a rigid substratum

In this work, the propagation of shear horizontal (SH) waves in a homogeneous, isotropic and compressible nonlinear hyper-elastic layer having finite thickness is studied. The upper surface of the layer is assumed to be free from traction and the lower boundary is rigidly fixed. These waves are dispersive like the Love waves. The problem is examined by a perturbation method that balances the nonlinearity and dispersion in the analysis. A nonlinear Schrodinger equation is derived describing the nonlinear self modulation of the waves. Then, the effect of nonlinear properties of the material on the propagation characteristics and on the existence of solitary waves are discussed.