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We address a task of local trajectory planning for the mobile robot in the presence of static and dynamic obstacles. Local trajectory is obtained as a numerical solution of the Model Predictive Control (MPC) problem. Collision avoidance may be provided by adding repulsive potential of the obstacles to the cost function of MPC. We develop an approach, where repulsive potential is estimated by the neural model. We propose and explore three possible strategies of handling dynamic obstacles. First, environment with dynamic obstacles is considered as a sequence of static environments. Second, the neural model predict a sequence of repulsive potential at once. Third, the neural model predict future repulsive potential step by step in autoregressive mode. We implement these strategies and compare it with CIAO* and MPPI using BenchMR framework. First two strategies showed higher performance than CIAO* and MPPI while preserving safety constraints. The third strategy was a bit slower, however it still satisfy time limits. We deploy our approach on Husky UGV mobile platform, which move through the office corridors under proposed MPC local trajectory planner. The code and trained models are available at \url{https://github.com/CognitiveAISystems/Dynamic-Neural-Potential-Field}.

Detecting the specificity of cancer cells to distinguish them from normal ones is an important step in the general framework of cancer diagnosis. A routine example of such diagnosis in cancerous tissues implies using microscope analysis of fixed, paraffined, and colored slices such as the H&E stain (1). Such a method, which takes place after surgery, is based on carefully analyzing the cell's size and shape. Often, this approach is performed in parallel with more modern genetic tests. Recent research has hypothesized that extrachromosomal circular DNA (eccDNA) could be considered a new hallmark of cancer (4). Thus, this research aims to check if using a simple, label-free microscope dynamic analysis performed on living cancer cells would allow efficient and simpler detection of cancer cells.

Though extensively studied since the emergence of zincblende cubic A(B,C) semiconductor alloys (zb-SCA) in the sixties, the phonon mode behavior of Ga(As,P) remains a subject of debate to this day. Recently Zollner and co-authors reported on a thorough far-infrared (IR) ellipsometry study of the vibrational spectra of Ga(As,P) spanning the composition domain. They suggest that their experimental data are better described by the cluster model than by our percolation model. On the basis of Zollner experimental data, kindly made available to us, we derive an improved version of the percolation model that explains the phonon mode behavior of Ga(As,P) beyond existing CM and PM approaches, with ab initio phonon calculations in support., Comment: 23 pages, 6 figures

Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. The differentiability of our model allows its usage within the MPC solver. It is computationally hard to solve problems with a very high number of parameters. Therefore, our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings, which reduce problem dimensionality by two orders of magnitude. The reference data for network training are generated based on algorithmic calculation of a signed distance function. Comparative experiments showed that the proposed approach is comparable with existing local planners: it provides trajectories with outperforming smoothness, comparable path length, and safe distance from obstacles. Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning. The code for our approach is presented at https://github.com/cog-isa/NPField together with demo video.

This work compares two tomographic imaging technologies, time-domain full-field optical coherence tomography (FFOCT) working in reflection and optical transmission tomography (OTT), using a new optical setup that combines both. We show that, due to forward-scattering properties, the axial sectioning and contrast in OTT can be optimized by tuning illumination. The influence of sample scattering and thickness are discussed. We illustrate the comparison of the two methods in static (morphology) and dynamic (metabolic contrast) regimes using cell cultures, tissues and entire organisms emphasizing the advantages of both approaches.

The Cd1-xZnxTe semiconductor alloy is a regular system regarding its macroscopic mechanic properties in that its experimental bulk modulus exhibits a linear x-dependence, in line with ab initio predictions. Complexity arises at the bond scale, referring to the intricate Cd1-xZnxTe percolation-type Raman pattern [T. Alhaddad et al., Journal of Applied Physics 133, 065701 (2023)]. This offers an appealing benchmark to test various phonon coupling processes at diverse length scales in a compact multi-oscillator assembly, presently tuned by pressure. At x around 0, an inter-bond long-range/macro electric coupling between the matrix and impurity polar phonons is detuned under pressure. Inversely, at x around 1, an intra-bond short-range/nano mechanic coupling is enforced between the two Zn Te apolar sub-phonons stemming from same and alien percolation-type environments. The pressure-induced macro/nano polar/apolar coupling/decoupling processes are compared within a model of two coupled electric/mechanic harmonic oscillators in terms of a compromise between proximity to resonance and strength of coupling, impacting the degree of mode mixing, with ab initio (apolar case) and analytical (polar case) Raman calculations in support. Notably, the free mechanic coupling at x around 1 opposes the achievement of a phonon exceptional point, manifesting the inhibition of mechanic coupling, earlier evidenced with similar bonds for x smaller than 0.5. Hence, the pressure dependence of a given bond vibration in a disordered alloy basically differs depending on whether the bond is matrix-like, i.e., self-connected in bulk (free coupling), or dispersed, i.e., self-connected in a chain (inhibited coupling). This features pressure-tunable percolation-based on-off phonon switches in complex media.

Following a well-established practice of publishing commentaries to articles of other authors who work on materials that were earlier studied by them (n.b. six published comments[1-6]), Marek Szafra\'nski(MS) and Andrzej Katrusiak (AK) have filed on the preprint server arXiv a manuscript entitled "On the giant deformation and ferroelectricity of guanidinium nitrate"[7] with comments on our article "Exceptionally high work density of a ferroelectric dynamic organic crystal around room temperature" published in Nature Communications (2022, 13, 2823).[8] Both in the submitted comment as well as in the required (by the journal) direct communication with us preceding its posting, MS and AK have expressed dissatisfaction with the choice of literature references in our article, for which they felt that their previous work on this material has not been cited to a sufficient extent. In their comment, they summarize their other remarks on our article as "the structural determinations of GN [guanidinium nitrate] crystals, their phase transitions and associated giant deformation, as well as its detailed structural mechanism, the molecular dynamics and dielectric properties were reported before, while the semiconductivity, ferroelectricity, and fatigue resistance of the GN [guanidinium nitrate] crystals cannot be confirmed."[7] Apart from the sentiments of MS and AK on our choice of cited literature, we find their comments on the scientific content of our article to be strongly biased towards their own results and unfounded. Below, we provide a detailed response to their comments., Comment: 13 pages, 1 figure

There is an increasing need for label free methods that could reveal intracellular structures and dynamics. In this context, we develop a new optical tomography method working in transmission - Full-field optical transmission tomography (FF-OTT). The method can measure the forward scattering signals and reveal the metabolic time-dependent signals in living cells. FF-OTT is a common path interferometer taking advantage of the Gouy phase shift - a {\pi} phase shift that the light wave experiences around the focus. By modulating position of the focus one can alter the phase of the scattered light. Demodulation of images with different phases rejects the background and enhances the light from the depth-of-focus, thus producing an optical section. We test FF-OTT by imaging single-cell diatoms and ex vivo biological samples. In fresh samples, we show that the intracellular motions create visible intensity fluctuations in FF-OTT so that the method is able to reveal a metabolic dynamic contrast. FF-OTT was found to be an efficient label free technique that can be readily implemented thanks to a robust common-path speckle-free interferometer design using an incoherent light source.

In traffic management, it is a very important issue to shorten the response time by detecting the incidents (accident, vehicle breakdown, an object falling on the road, etc.) and informing the corresponding personnel. In this study, an anomaly detection framework for road junctions is proposed. The final judgment is based on the trajectories followed by the vehicles. Trajectory information is provided by vehicle detection and tracking algorithms on visual data streamed from a fisheye camera. Deep learning algorithms are used for vehicle detection, and Kalman Filter is used for tracking. To observe the trajectories more accurately, the detected vehicle coordinates are transferred to the bird's eye view coordinates using the lens distortion model prediction algorithm. The system determines whether there is an abnormality in trajectories by comparing historical trajectory data and instantaneous incoming data. The proposed system has achieved 84.6% success in vehicle detection and 96.8% success in abnormality detection on synthetic data. The system also works with a 97.3% success rate in detecting abnormalities on real data.

In this paper, we provide a guideline for using the Neural Network Dependability Kit (NNDK) during the development process of NN models, and show how the algorithm is applied in two image classification use cases. The case studies demonstrate the usage of the dependability kit to obtain insights about the NN model and how they informed the development process of the neural network model. After interpreting neural networks via the different metrics available in the NNDK, the developers were able to increase the NNs' accuracy, trust the developed networks, and make them more robust. In addition, we obtained a novel application-oriented technique to provide supporting evidence for an NN's classification result to the user. In the medical image classification use case, it was used to retrieve case images from the training dataset that were similar to the current patient's image and could therefore act as a support for the NN model's decision and aid doctors in interpreting the results., Comment: 10 pages, 7 Figures including 2 appendices Main Content: 5 pages, 1 Figure

There is a growing interest in social robots to be considered in the therapy of children with autism due to their effectiveness in improving the outcomes. However, children on the spectrum exhibit challenging behaviors that need to be considered when designing robots for them. A child could involuntarily throw a small social robot during meltdown and that could hit another person's head and cause harm (e.g. concussion). In this paper, the application of soft materials is investigated for its potential in attenuating head's linear acceleration upon impact. The thickness and storage modulus of three different soft materials were considered as the control factors while the noise factor was the impact velocity. The design of experiments was based on Taguchi method. A total of 27 experiments were conducted on a developed dummy head setup that reports the linear acceleration of the head. ANOVA tests were performed to analyze the data. The findings showed that the control factors are not statistically significant in attenuating the response. The optimal values of the control factors were identified using the signal-to-noise (S/N) ratio optimization technique. Confirmation runs at the optimal parameters (i.e. thickness of 3 mm and 5 mm) showed a better response as compared to other conditions. Designers of social robots should consider the application of soft materials to their designs as it help in reducing the potential harm to the head.

We investigated the ability of diamond nanoparticles (nanodiamonds, NDs) to deliver small interfering RNA (siRNA) in Ewing sarcoma cells, in the perspective of in vivo anti-cancer nucleic acid drug delivery. siRNA was adsorbed onto NDs previously coated with cationic polymer. Cell uptake of NDs has been demonstrated by taking advantage of NDs intrinsic fluorescence coming from embedded color center defects. Cell toxicity of these coated NDs was shown to be low. Consistent with the internalization efficacy, we have shown a specific inhibition of EWS/Fli-1 gene expression at the mRNA and protein level by the ND vectorized siRNA in a serum containing medium.

In this paper we define a two-variable, generic Hecke algebra, H, for each complex reflection group G(b,1,n). The algebra H specializes to the group algebra of G(b,1,n) and also to an endomorphism algebra of a representation of GL(n,q) induced from a solvable subgroup. We construct Kazhdan-Lusztig "R-polynomials" for H and show that they may be used to define a partial order on G(b,1,n). Using a generalization of Deodhar's notion of distinguished subexpressions we give a closed formula for the R-polynomials. After passing to a one-variable quotient of the ring of scalars, we construct Kazhdan-Lusztig polynomials for H that reduce to the usual Kazhdan-Lusztig polynomials for the symmetric group when b=1., Comment: 25 pages; This is a substantive revision. A construction of a Kazhdan-Lusztig C basis and Kazhdan-Lusztig polynomials for H was added. A lot of typos were fixed. Some new typos might have been introduced