Miracle-dz/UniFAR-FTUs huggingface dataset:
Unifying scene registration and trajectory optimization for learning from demonstrations with application to manipulation of deformable objects

Recent work [1], [2] has shown promising results in enabling robotic manipulation of deformable objects through learning from demonstrations. Their method computes a registration from training scene to test scene, and then applies an extrapolation of this registration to the training scene gripper motion to obtain the gripper motion for the test scene. The warping cost of scene-to-scene registrations is used to determine the nearest neighbor from a set of training demonstrations. Then once the gripper motion has been generalized to the test situation, they apply trajectory optimization [3] to plan for the robot motions that will track the predicted gripper motions. In many situations, however, the predicted gripper motions cannot be followed perfectly due to, for example, joint limits or obstacles. In this case the past work finds a path that minimizes deviation from the predicted gripper trajectory as measured by its Euclidean distance for position and angular distance for orientation. Measuring the error this way during the motion planning phase, however, ignores the underlying structure of the problem-namely the idea that rigid registrations are preferred to generalize from training scene to test scene. Deviating from the gripper trajectory predicted by the extrapolated registration effectively changes the warp induced by the registration in the part of the space where the gripper trajectories are. The main contribution of this paper is an algorithm that considers this effective final warp as the criterion to optimize for in a unified optimization that simultaneously considers the scene-to-scene warping and the robot trajectory (which were separated into two sequential steps by the past work). This results in an approach that adjusts to infeasibility in a way that adapts directly to the geometry of the scene and minimizes the introduction of additional warping cost. In addition, this paper proposes to learn the motion of the gripper pads, whereas past work considered the motion of a coordinate frame attached to the gripper as a whole. This enables learning more precise grasping motions. Our experiments, which consider the task of knot tying, show that both unified optimization and explicit consideration of gripper pad motion result in improved performance.
Supporting development of highly dependable software through continuous, automated, in-process, and individualized software measurement validation

5 Research plan 12 5.1 Overview of valid software measure development for MDS . 12 5.2 Five software measurement validation experiments . 13 5.3 Experimental methodology . 14 5.4 Deliverables . 15 5.5 Timeline . 16
Do round numbers always become reference points?: An examination by Japanese and Major League Baseball data

Conclusion: 1. The round number effect is replicated in Japanese baseball data. 2. The round number effect depends on people’s incentives or motivation Want to achieve “0.300” Research question: 1. Does the round number effect occur in Japanese baseball data? : Pope and Simonsohn’s (2011) findings are based solely on Major League Baseball (MLB) data, thus their replicabilities should be explored. 2. Does the round number effect depend on people’s motivation? : Whether “0.300” can be reference point might depends on batter’s ability because achieving the regulation numbers at bat is crucial to evaluation for the batting average “0.300. ”
Submitted to The Third Swedish Symposium on Deep Learning

In safety-critical applications, we wish to have a measure of uncertainty quantification—a measure of confidence calibration that reflects the underlying uncertainty. For neural network based classifiers, the output probabilities on the target classes are used typically as a way of uncertainty quantification. However, it has been observed that the standard training of neural networks, in particular when trained for the best accuracy, may produce uncalibrated probabilities—probability estimates that are not representative of the true likelihood. Building on our previous work on the matrix multilayer perceptron (Taghia et al., 2019), here, we propose a measure for uncertainty quantification that appears to be more reliable (calibrated) in comparison to the standard output probabilities from the network. 1 PROBLEM FORMULATION Consider a classification problem of m > 1 categories and let M = {1, . ,m}. We are given a training dataset consists of the inputs and the corresponding labels, Dtrain = {x, y}, x = {xi ∈ R}i=1, y = {yi ∈M}i=1. (1) Given enough data samples and a viable choice of loss function, the network learns to predict the label for an unseen test data sample x∗ ∈ Dtest. From a probabilistic classifier, it is expected to not only correctly identify the labels but also assign reliable probabilities to them. For a well calibrated classifier, these probabilities can then be directly interpreted as a measure of confidence level. Refer to Guo et al. (2017) for discussion on the calibration of the neural networks and to Vaicenavićius et al. (2019) for general discussion on evaluation of the model calibration in classification. It has been observed that the standard training of neural networks, for example using MLP, gives biased probabilities. This is particular true when the network is trained for the best accuracy (Guo et al., 2017). Here, we propose an approach for deriving a set of “modified probabilities” that show better calibration characteristics than the standard output probabilities from the network.
Active Roll-outs in MDP with Irreversible Dynamics

In Reinforcement Learning (RL), regret guarantees scaling with the square root of the time horizon have been shown to hold only for communicating Markov decision processes (MDPs) where any two states are connected. This essentially means that an algorithm can eventually recover from any mistake. However, real-world tasks usually include situations where taking a single "bad" action can permanently trap a learner in a suboptimal region of the state-space. Since it is provably impossible to achieve sub-linear regret in general multi-chain MDPs, we assume a weak mechanism that allows the learner to request additional information. Our main contribution is to address: (i) how much external information is needed, (ii) how and when to use it, and (iii) how much regret is incurred. We design an algorithm that minimizes requests for external information in the form of rollouts of a policy specified by the learner by actively requesting it only when needed. The algorithm provably achieves O(√ T) active regret after T steps in a large class of multi-chain MDPs, by only requesting O(log(T)) rollout transitions. The superiority of our algorithm to standard algorithms such as R-Max and UCRL is demonstrated in experiments on some illustrative grid-world examples. (a) (b) (c) Figure 1: Example of (a) a communicating MDP, (b) a unichain MDP with a single recurrent class, and (c) a multi-chain MDP with two recurrent classes. The circles represent states while the labeled edges represent transitions due to executing actions {a, b, c}.
MONO- AND BI-CISTRONIC CHIMERIC mRNAs IN ARABIDOPSIS AND RICE GENOMES

In contrast to prokaryotes, the proximity of genes in eukaryotic genomes has not previously been known to play any signiflcant role in their expression proflles. However, re- cently reported phenomena in human, mouse, yeast and a few plant species indicate such role: chimeric mRNAs and proteins produced via alternative transcription termination, splicing and translation of the Tail-to-Head (T2H) neighboring genes. To further verify this evidence at the genomic scale, we scanned the rice and Arabidopsis genomes for the presence of T2H gene pairs at a distance of less than 1000 bp. Our studies suggest that \non-stopping" transcription and alternative splicing of some of these T2H pairs may produce chimer transcripts. We ob- tained cDNA support for 100 Arabidopsis and 42 rice T2H pairs to be transcribed into chimeric mRNA(s). Analysis of 155 chimeric transcripts from both species revealed 71 mono-cistronic and 58 bi-cistronic mRNAs potentially encoding chimeric or separate proteins.
­­Evolution and genomic organization of the sHSP gene cluster in Arthropods

Background Conserved syntenic gene complexes are rare in Arthropods and likely only retained due to functional constraint. Numerous sHSPs have been identified in the genomes of insects, some of which are located clustered in close proximity. Previous phylogenetic analyses of these clustered sHSP have been limited to a small number of holometabolous insect species and have not determined the pattern of evolution of the clustered sHSP genes (sHSP-C) in insect or Arthropod lineages. Results Using eight genomes from representative insect orders and three non-insect arthropod genomes we have identified that a syntenic cluster of sHSPs (sHSP-C) is a hallmark of most Arthropod genomes. Using 11 genomes from Hymenopteran species our phylogenetic analyses have refined the evolution of the sHSP-C in Hymenoptera and found that the sHSP-C is order-specific with evidence of birth-and-death evolution in the hymenopteran lineage. Finally we have shown that the honeybee sHSP-C is co-ordinately expressed and is marked by genomic features, including H3K27me3 histone marks consistent with coordinate regulation, during honeybee ovary activation. Conclusions The syntenic sHSP-C is present in most insect genomes, and its conserved coordinate expression and regulation implies that it is an integral genomic component of environmental response in arthropods.
The impact of the COVID-19 pandemic on academic productivity

'Publish or perish' is an expression describing the pressure on academics to consistently publish research to ensure a successful career in academia. With a global pandemic that has changed the world, how has it changed academic productivity? Here we show that academics are posting just as many publications on the arXiv pre-print server as if there were no pandemic: 168,630 were posted in 2020, a +12.6% change from 2019 and $+1. 4σ$ deviation above the predicted 162,577 $\pm$ 4,393. However, some immediate impacts are visible in individual research fields. Conference cancellations have led to sharp drops in pre-prints, but laboratory closures have had mixed effects. Only some experimental fields show mild declines in outputs, with most being consistent on previous years or even increasing above model expectations. The most significant change is a 50% increase ($+8σ$) in quantitative biology research, all related to the COVID-19 pandemic. Some of these publications are by biologists using arXiv for the first time, and some are written by researchers from other fields (e.g., physicists, mathematicians). While quantitative biology pre-prints have returned to pre-pandemic levels, 20% of the research in this field is now focussed on the COVID-19 pandemic, demonstrating a strong shift in research focus.
Advanced calculation of the deflection of worm shafts with FEM

Worm gears are gear units with a shaft angle of mostly 90°. They are used in a variety of industrial applications due to large gear ratios and high transmittable torques. The high sliding ratio in the tooth contact requires a hard/soft material combination that is insensitive to scuffing. Worm shafts usually are made of case-hardened steel and worm wheels of a copper-tin-bronze alloy. Since damage predominantly occurs at the wheel (pitting, wear, tooth fracture, etc.), a wide range of calculation methods for load capacity and service life were developed. Regarding the worm shaft, only the deflection is examined, because high displacements of the shaft shifts the contact pattern, which leads to transmission errors and may lead to increased wear. Common calculation methods, e.g. according to DIN 3996 and ISO/TS 14521 provide a good approximation of the deflection. However, simplifications are made in favour of the manageability of the calculation method. Geometric modifications on the worm such as reduced tooth thickness or different lead angles cannot be taken into account with common methods. In order to close this gap, a new approach was presented by Norgauer in 2021, however, significantly overestimating the stiffening effect of the gearing in some cases by neglecting the helical winding of the worm teeth. For efficiency-optimal design of minimally thin worm shafts, approaches that go beyond the deflection are also missing. Investigations by the authors on worm gears using the finite element method (FEM) show a deviation of the bending line of worm shafts from the common calculation methods. The FEM calculations simulate the tooth meshing under load by a driving torque on the shaft, whereas the standard calculations simplify the load distribution on the worm shaft as a radial force introduced at a point. In addition to the magnitude of the radial forces, the axial tooth force component was identified as an influence factor on the bending line since it causes a displacement of the maximum bending location. Especially for increasing diameter factors the displacement of the bearings under load must be taken into account since they may be in the same range as the deflection. Furthermore, the helical winding of the teeth around the shaft leads to an average stiffening effect of the gearing. The cross-section depends on the gear meshing position and causes a periodic fluctuation of the area moment of inertia and a wobbling motion of the shaft. The changing load distribution on several teeth causes a periodic change of the lever arms and an additional dependence of the deflection from the mesh position. An analytical calculation method for the deflection of worm shafts is proposed. It aims at the precise calculation of the spatial expression of the bending line, extends the commonly used simplified model and takes the newly identified factors into account. The bending lines in both radial directions are considered separately. The values of the new introduced factors are derived from the FEM results. Finally, the calculated bending lines are compared with the FEM results and verified.
Covid-19 Detection from Chest X-Ray Images Using Deep CNN Architectures with Transfer Learning

The novel coronavirus COVID-19 first appeared in China at the end of 2019 and was subsequently classified as a world pandemic. At the time of writing, the number of affected persons is 52,331,462 persons and the number of deaths is 1,287,966 deaths. The most used screening methods of COVID-19 is Reverse-Transcription Polymerase Chain Reaction (RT-PCR) test. The number of RT-PCR test kits available is limited because of the increasing number of cases. Some people with COVID-19 have difficulty breathing and their lungs are damaged. Consequently, radiologists utilized Chest X-Ray images to detect the damage caused the COVID-19 into the lungs. However, manual detection takes an important time and depends on the radiologist's expertise. Therefore, it is important to implement automatic detection methods to solve this problem. Due to the limitation of data sets containing COVID-19 images and the small number of training data, transfer learning based on Convolutional Neural Networks (CNN) can be a good combination to solve this problem. In this work, we propose two pre-trained CNNs architectures AlexNet and Residual Network (ResNet-50) to detect COVID-19. The two presented architectures are trained to detect COVID-19, normal and pneumonia from Chest X-Ray images using a 10-Fold cross validation method. Our proposed model outperforms the existing methods and yielded a mean classification accuracy of 96,74% with AlexNet and 99,2% with ResNet-50. In the future work, we will increase the number of COVID-19, Normal and Pneumonia images in the datasets to outperform the performance metrics.
Sistem Informasi Perawatan Dan Inventaris Laboratorium Pada Smk Negeri 1 Rembang Berbasis Web

Abstrak – Thesis report entitled "Information Systems Laboratory Maintenance and Inventory At SMK Negeri 1 Rembang Web Based" has been implemented in order to produce a web-based information system to facilitate the activities of laboratories. The system was designed using UML modeling. While the programming language used is PHP and the MySQL database. The result of this design is the website "Information Systems Maintenance and Inventory" which the website is put emphasis on managing information on the management of goods and some aspects regarding the menu - menu that is compatible profiles menu, menu programs, lending menu, menu improvements information and menu news helpsand support Maintenance and Inventory. With the implementation and maintenance of information systems laboratory inventory at SMK Negeri 1 Rembang, the Lab activities can be well controlled. Key words: Maintenance and Inventory Lab, UML, SMK N egeri 1 Rembang Abstraksi – Penelitian dengan judul “ Sistem Informasi Maintenance Dan Inventaris Laboratorium Pada SMK Negeri 1 Rembang Berbasis Web ” telah dilaksanakan dengan tujuan menghasilkan suatu sistem informasi berbasis web untuk mempermudah kegiatan perawatan dan inventarisasi labolatorium. Sistem ini dirancang dengan menggunakan pemodelan UML. Sedangkan bahasa pemrograman yang digunakan adalah PHP dan database MySQL. Hasil dari rancang bangun ini adalah website “ Sistem Informasi Maintenance Dan Inventaris ” yang mana website ini dititik beratkan pada pengelolaan informasi tentang pengelolaan barang dan beberapa aspek yang berkenaan dengan menu – menu yang kompatible yakni menu profil, menu program, menu peminjaman, menu info perbaikan dan menu berita yang membantu penunjangan Maintenance Dan Inventaris . dengan diimplementasikannya sistem informasi maintenance dan inventaris laboratorium di SMK Negeri 1 Rembang, maka aktifitas Lab bisa terkontrol dengan baik. Kata Kunci : Perawatan dan Inventaris Lab , UML, SMK N egeri 1 Rembang
High Security Finger Vein Recognition Based on Robust Keypoint Correspondence Clustering

Finger vein recognition has been proven to be an effective pattern for personal versification in terms of its convenience and security. However, the existing works of finger vein recognition have neglected the application scenarios of finger vein recognition and treated the false acceptance rate (FAR) and the false rejection rate (FRR) equally, i.e., utilized the equal error rate (EER) as the main evaluation criterion. As structures hidden beneath the skin, the finger vein pattern is usually applied in access controls rather than forensics. Hence, the security requirement of finger vein recognition should be high, i.e., the FRR is assumed to be reduced under the premise of extremely low FAR. In our opinion, the important points and difficulties related to achieving high security recognition are enlarging the differences between genuine and imposter matchings. In this paper, a finger vein recognition framework based on robust keypoint correspondence clustering is proposed to achieve high security recognition. A scale-invariant feature transform (SIFT) descriptor-based method is utilized as the base recognizer. Then, a multi-input multi-output (MIMO) matching structure is designed according to different physical characteristics of the finger vein images to enhance the matching possibilities. After that, integrations of the matching pairs of each correspondence (i.e., matching of two images) are clustered according to the deformation information of each matching pair by a novel simulated clustering technique. Finally, the matching score is defined as the number of matching pairs after clustering. Extensive experiments on HKPU and FV-SDUMLA-HMT open databases demonstrate the superior performance of the proposed method, with the FRRs-at-0-FAR of 0.0139 and 0.2377, respectively, which imply the applicability of the proposed method in high security scenarios. The corresponding EERs are 0.0015 and 0.0139, and the rank-one recognition rates are 99.91% and 97.54%, respectively, which are comparable to the state-of-the-art methods and further indicate the effectiveness of the proposed method.
PHA-680626 Is an Effective Inhibitor of the Interaction between Aurora-A and N-Myc

Neuroblastoma is a severe childhood disease, accounting for ~10% of all infant cancers. The amplification of the MYCN gene, coding for the N-Myc transcription factor, is an essential marker correlated with tumor progression and poor prognosis. In neuroblastoma cells, the mitotic kinase Aurora-A (AURKA), also frequently overexpressed in cancer, prevents N-Myc degradation by directly binding to a highly conserved N-Myc region. As a result, elevated levels of N-Myc are observed. During recent years, it has been demonstrated that some ATP competitive inhibitors of AURKA also cause essential conformational changes in the structure of the activation loop of the kinase that prevents N-Myc binding, thus impairing the formation of the AURKA/N-Myc complex. In this study, starting from a screening of crystal structures of AURKA in complexes with known inhibitors, we identified additional compounds affecting the conformation of the kinase activation loop. We assessed the ability of such compounds to disrupt the interaction between AURKA and N-Myc in vitro, using Surface Plasmon Resonance competition assays, and in tumor cell lines overexpressing MYCN, by performing Proximity Ligation Assays. Finally, their effects on N-Myc cellular levels and cell viability were investigated. Our results identify PHA-680626 as an amphosteric inhibitor both in vitro and in MYCN overexpressing cell lines, thus expanding the repertoire of known conformational disrupting inhibitors of the AURKA/N-Myc complex and confirming that altering the conformation of the activation loop of AURKA with a small molecule is an effective strategy to destabilize the AURKA/N-Myc interaction in neuroblastoma cancer cells.
Exploiting Concurrency in Sharded Parallel State Machine Replication

State machine replication (SMR) is a well-known approach to implementing fault-tolerant services, providing high availability and strong consistency. In classic SMR, commands are executed sequentially, in the same order by all replicas. To improve performance, two classes of protocols have been proposed to parallelize the execution of commands. Early scheduling protocols reduce scheduling overhead but introduce costly synchronization of worker threads; late scheduling protocols, instead, reduce the cost of thread synchronization but suffer from scheduling overhead. Depending on the characteristics of the workload, one class can outperform the other. We introduce a hybrid scheduling technique that builds on the existing protocols. An experimental evaluation has revealed that the hybrid approach not only inherits the advantages of each technique but also scales better than either one of them, improving the system performance by up to <inline-formula><tex-math notation="LaTeX"> $3\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="alchieri-ieq1-3135761.gif"/></alternatives></inline-formula> in a workload with conflicting commands.