Miracle-dz/UniFAR-FTUs huggingface dataset:
InfoPrint: Embedding Information into 3D Printed Objects

We present a technique to embed information invisible to the eye inside 3D printed objects. The information is integrated in the object model, and then fabricated using off-the-shelf dual-head FDM (Fused Deposition Modeling) 3D printers. Our process does not require human intervention during or after printing with the integrated model. The information can be arbitrary symbols, such as icons, text,binary, or handwriting. To retrieve the information, we evaluate two different infrared-based imaging devices that are readily available-thermal cameras and near-infrared scanners. Based on our results, we propose design guidelines for a range of use cases to embed and extract hidden information. We demonstrate how our method can be used for different applications, such as interactive thermal displays, hidden board game tokens, tagging functional printed objects, and autographing non-fungible fabrication work.
Towards Full-Fledged Argument Search: A Framework for Extracting and Clustering Arguments from Unstructured Text

Argument search aims at identifying arguments in natural language texts. In the past, this task has been addressed by a combination of keyword search and argument identification on the sentence- or document-level. However, existing frameworks often address only specific components of argument search and do not address the following aspects: (1) argument-query matching: identifying arguments that frame the topic slightly differently than the actual search query; (2) argument identification: identifying arguments that consist of multiple sentences; (3) argument clustering: selecting retrieved arguments by topical aspects. In this paper, we propose a framework for addressing these shortcomings. We suggest (1) to combine the keyword search with precomputed topic clusters for argument-query matching, (2) to apply a novel approach based on sentence-level sequence-labeling for argument identification, and (3) to present aggregated arguments to users based on topic-aware argument clustering. Our experiments on several real-world debate data sets demonstrate that density-based clustering algorithms, such as HDBSCAN, are particularly suitable for argument-query matching. With our sentence-level, BiLSTM-based sequence-labeling approach we achieve a macro F1 score of 0.71. Finally, evaluating our argument clustering method indicates that a fine-grained clustering of arguments by subtopics remains challenging but is worthwhile to be explored.
On the Emergence of a New Instability during Core Collapse of Very Massive Stars

The process of uniform supernovae (SNe) explosions is well investigated for all their types. However, observational data suggests that the SNe may be not spherically symmetric. Modern multidimensional simulations of SNe demonstrate development of hydrodynamical instabilities during the explosion phase. But the configuration of a star and inhomogeneities prior to explosion could strongly affect how the SN develops. A number of papers on numerical modeling of pair-instability SNe explosion considered the case when thermonuclear energy in the central region of a massive star is introduced by a series of several hot spots. It leads to the appearance of many fragments of hot matter behind the divergence shock wave. An observable manifestation of this may be the presence of peaks on light curves of gamma-ray bursts associated with explosions of massive stars. The physical nature of such inhomogeneities is not evident and the number and size of spots is conjecture. In this work, we study the possibility of formation of these inhomogeneities at the stage of core collapse (CC) in a massive star. To check this assumption, we chose an analytic self-similar model of CC and investigated the stability of solutions obtained from it with respect to small multidimensional perturbations. It shows there are no conditions where the collapse of a very massive star may remain stable, although, for a less massive star, it is possible. Using the relations obtained, we found characteristic features of developing instability, thereby making it possible to estimate the amount and characteristic size of the inhomogeneities.
Software Variants for Hardware Trojan Detection and Resilience in COTS Processors

The commercial off-the-shelf (COTS) component based ecosystem provides an attractive system design paradigm due to the drastic reduction in development time and cost compared to custom solutions. However, it brings in a growing concern of trustworthiness arising from the possibility of embedded malicious logic, or hardware Trojans in COTS components. Existing trust-verification approaches are typically not applicable to COTS hardware due to the absence of golden models and the lack of observability of internal signals. In this work, we propose a novel approach for runtime Trojan detection and resilience in untrusted COTS processors through judicious modifications in software. The proposed approach does not rely on any hardware redundancy or architectural modification and hence seamlessly integrates with the COTS-based system design process. Trojan resilience is achieved through the execution of multiple functionally equivalent software variants. We have developed and implemented a solution for compiler-based automatic generation of program variants, metric-guided selection of variants, and their integration in a single executable. To evaluate the proposed approach, we first analyzed the effectiveness of program variants in avoiding the activation of a random pool of Trojans. By implementing several Trojans in an OpenRISC 1000 processor, we analyzed the detectability and resilience during Trojan activation in both single and multiple variants. We also present delay and code size overhead for the automatically generated variants for several programs and discuss future research directions to reduce the overhead.
On the Practical Consistency of Meta-Reinforcement Learning Algorithms

Consistency is the theoretical property of a meta learning algorithm that ensures that, under certain assumptions, it can adapt to any task at test time. An open question is whether and how theoretical consistency translates into practice, in comparison to inconsistent algorithms. In this paper, we empirically investigate this question on a set of representative meta-RL algorithms. We find that theoretically consistent algorithms can indeed usually adapt to out-of-distribution (OOD) tasks, while inconsistent ones cannot, although they can still fail in practice for reasons like poor exploration. We further find that theoretically inconsistent algorithms can be made consistent by continuing to update all agent components on the OOD tasks, and adapt as well or better than originally consistent ones. We conclude that theoretical consistency is indeed a desirable property, and inconsistent meta-RL algorithms can easily be made consistent to enjoy the same benefits.
Do reverberation-measured Hβ quasars provide a useful test of cosmology?

We use 118 Hβ quasar (QSO) observations in the redshift range 0.0023 ≤ z ≤ 0.89 to simultaneously constrain cosmological model parameters and QSO 2-parameter radius-luminosity (R − L) relation parameters in six different cosmological models. We find that the R − L relation parameters for these QSOs are independent of the assumed cosmology so these QSOs seem to be standardizable through the R − L relation (although there is a complication that might render this untrue). Cosmological constraints obtained using these QSOs are weak, more favour currently decelerated cosmological expansion, and typically are in ∼2σ tension with those obtained from a joint analysis of baryon acoustic oscillation and Hubble parameter measurements. Extending the R − L relation to a 3-parameter one to try to correct for the accretion rate effect does not result in a reduction of the cosmological constraints discrepancy nor does it result in the hoped-for significant reduction of the intrinsic scatter of the R − L relation.
Benchmarking Deep Deblurring Algorithms: A Large-Scale Multi-Cause Dataset and A New Baseline Model

Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for speciﬁc scenarios. However, in most real-world images, blur is caused by different factors, e.g., motion and defocus. In this paper, we address how different deblurring methods perform on general types of blur. For in-depth performance evaluation, we construct a new large-scale multi-cause image deblurring dataset called (MC-Blur) including real-world and synthesized blurry images with mixed factors of blurs. The images in the proposed MC-Blur dataset are collected using different techniques: convolving Ultra-High-Deﬁnition (UHD) sharp images with large kernels, averaging sharp images captured by a 1000 fps high-speed camera, adding defocus to images, and real-world blurred images captured by various camera models. These results provide a comprehensive overview of the advantages and limitations of current deblurring methods. Further, we pro-pose a new baseline model, level-attention deblurring network, to adapt to multiple causes of blurs. By including different weights of attention to the different levels of features, the proposed network derives more powerful features with larger weights assigned to more important levels, thereby enhancing the feature representation. Extensive experimental results on the new dataset demonstrate the effectiveness of the proposed model for the multi-cause blur scenarios.
Beyond Flatland: Pre-training with a Strong 3D Inductive Bias

Pre-training on large-scale databases consisting of natural images and then fine-tuning them to fit the application at hand, or transfer-learning, is a popular strategy in computer vision. However, Kataoka et al., 2020 introduced a technique to eliminate the need for natural images in supervised deep learning by proposing a novel synthetic, formula-based method to generate 2D fractals as training corpus. Using one synthetically generated fractal for each class, they achieved transfer learning results comparable to models pre-trained on natural images. In this project, we take inspiration from their work and build on this idea -- using 3D procedural object renders. Since the image formation process in the natural world is based on its 3D structure, we expect pre-training with 3D mesh renders to provide an implicit bias leading to better generalization capabilities in a transfer learning setting and that invariances to 3D rotation and illumination are easier to be learned based on 3D data. Similar to the previous work, our training corpus will be fully synthetic and derived from simple procedural strategies; we will go beyond classic data augmentation and also vary illumination and pose which are controllable in our setting and study their effect on transfer learning capabilities in context to prior work. In addition, we will compare the 2D fractal and 3D procedural object networks to human and non-human primate brain data to learn more about the 2D vs. 3D nature of biological vision.
Aspects of Noncommutative Geometry of Bunce-Deddens Algebras

The purpose of this paper is to study noncommutative geometry of Bunce-Deddens (BD) algebras [3], [4] and their natural smooth subalgebras. Our previous, related investigations of BD algebras are contained in [13] and [14] and are mostly concerned with classifications of unbounded derivations. The main objects of study in this paper are smooth subalgebras of Bunce-Deddens algebras. The smooth subalgebras are dense *-subalgebras closed under holomorphic functional calculus and complete in their own stronger locally convex topology. As the name suggests, smooth subalgebras capture differentiable structure in noncommutative geometry, in analogy with ordinary differential geometry and thus are important objects to study. The most common way to construct smooth subalgebras in C-algebras is as smooth elements of an action of a Lie group, see [2]. There is a natural circle action on BD algebras, however the resulting smooth subalgebras seem to be too big and in particular don’t have a nice classification of derivations on them. The smooth-subalgebras studied in this paper are more closely related to the work in [16] regarding smooth subalgebras of crossed products A⋊ Z of which Bunce-Deddens algebras are examples. A key difference, however, is that the construction in [16] starts with a dense subalgebra of A with its own Frechet topology, while in our case a choice of such a subalgebra is not obvious. Bunce-Deddens algebras are crossed-product C-algebras obtained from odometers. Due to the topology of odometers, which are Cantor sets with a minimal action of a homeomorphism, the smooth subalgebras are naturally equipped with inductive limit Frechet (LF) topology. Derivations naturally arise in differential geometry as vector fields and play an analogous role in noncommutative geometry. We prove that the space of continuous derivations on the smooth subalgebra of a BD algebra modulo inner derivations is just one-dimensional and we exhibit its natural generator. K-Theory and K-Homology are key invariants in the noncommutative topology of quantum spaces. In addition to reviewing the known K-Theory of BD algebras, we include two detailed calculations of K-Homology. One calculation uses Rosenberg and Schochet’s
Mobility and Routing in a Delay-tolerant Network of Unmanned Aerial Vehicles

Technology has reached a point where it has become feasible to develop unmanned aerial vehicles (UAVs), that is aircraft without a human pilot on board. Given that future UAVs can be autonomous and cheap, applications of swarming UAVs are possible. In this thesis we have studied a reconnaissance application using swarming UAVs and how these UAVs can communicate the reconnaissance data. To guide the UAVs in their reconnaissance mission we have proposed a pheromone based mobility model that in a distributed manner guides the UAVs to areas not recently visited. Each UAV has a local pheromone map that it updates based on its reconnaissance scans. The information in the local map is regularly shared with a UAV’s neighbors. Evaluations have shown that the pheromone logic is very good at guiding the UAVs in their cooperative reconnaissance mission in a distributed manner. Analyzing the connectivity of the UAVs we found that they were heavily partitioned which meant that contemporaneous communication paths generally were not possible to establish. This means that traditional mobile ad hoc network (MANET) routing protocols like AODV, DSR and GPSR will generally fail. By using node mobility and the store-carry-forward principle of delay-tolerant routing the transfer of messages between nodes is still possible. In this thesis we propose location aware routing for delay-tolerant networks (LAROD). LAROD is a beacon-less geographical routing protocol for intermittently connected mobile ad hoc networks. Using static destinations we have shown by a comparative study that LAROD has almost as good delivery rate as an epidemic routing scheme, but at a substantially lower overhead.
Diffusion Bridges for Stochastic Hamiltonian Systems and Shape Evolutions

Stochastically evolving geometric systems are studied in shape analysis and compu- tational anatomy for modelling random evolutions of human organ shapes. The notion of geodesic paths between shapes is central to shape analysis and has a natural generalisation as diﬀusion bridges in a stochastic setting. Simulation of such bridges is key to solve inference and registration problems in shape analysis. We demonstrate how to apply state-of-the-art diﬀusion bridge simulation methods to recently introduced stochastic shape deformation models thereby substantially expanding the ap-plicability of such models. We exemplify these methods by estimating template shapes from observed shape conﬁgurations while simultaneously learning model parameters.
Structure and function of axo-axonic inhibition

Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.
Modulating the Graphitic Domains of Hard Carbons Derived from Mixed Pitch and Resin to Achieve High Rate and Stable Sodium Storage.

Resin derived hard carbons (HCs) generally demonstrate remarkable electrochemical performance for both sodium ion batteries (SIBs) and potassium-ion batteries (KIBs), but their practical applications are hindered by their high price and high temperature pyrolysis (≈1500 °C). Herein, low-cost pitch is coated on the resin surface to compromise the cost, and meanwhile manipulate the microstructure at a relatively low pyrolysis temperature (1000 °C). HC-0.2P-1000 has a large number of short graphitic layer structures and a relatively large interlayer spacing of 0.3743 nm, as well as ≈1 nm sized nanopores suitable for sodium storage. Consequently, the as produced material demonstrates a superior reversible capacity (349.9 mAh g-1 for SIBs and 321.9 mAh g-1 for KIBs) and excellent rate performance (145.1 mAh g-1 at 20 A g-1 for SIBs, 48.5 mAh g-1 at 20 A g-1 for KIBs). Furthermore, when coupled with Na3 V2 (PO4 )3 as cathode, the full cell exhibits a high energy density of 251.1 Wh kg-1 and excellent stability with a capacity retention of 73.3% after 450 cycles at 1 A g-1 .
TORQUE VECTOR CONTROL (TVC) OF AXIALLY-LAMINATED ANISOTROPIC (ALA) ROTOR RELUCTANCE SYNCHRONOUS MOTORS

Abstract Axially-laminated anisotropic (ALA) rotor reluctance synchronous motors (RSMs) have been proved recently capable of higher torque density, higher power factor and efficiency compared to their induction motor counterpart with the same stator. Vector current control systems have been proposed for such drives, based on maximum available torque constant current angle or minimum current for a given torque constant current angle or combined current-voltage control. In an effort to do away with the current controller limitations, especially in the high speed region, the paper proposes a torque vector control (TVC) system which is a direct stator flux and torque control through a table of optimum switchings in the voltage-source PWM inverter. A sliding mode speed controller is added. Extensive digital simulations are presented. They demonstrate the TVC fast response, wide speed range and load rejection performance.
Surfactin induces ER stress‐mediated apoptosis via IRE1‐ASK1‐JNK signaling in human osteosarcoma

Osteosarcoma, one of primary bone tumor in children and young adults, has poor prognosis and drug resistances to chemotherapy. In order to reinforce the conventional therapies and antagonize the osteosarcoma in patients, a novel strategy is required for developing a new treatment. In this study, surfactin, a natural product from Bacillus subtilis, showed the efficiency of cell death in osteosarcoma, but not in normal cells. Surfactin triggers ER stress mechanism by promoting the aberrant Ca2+ release from ER lumen and ER‐signaling to mitochondrial dysfunction following caspases activation mediating cell apoptosis. Surfactin‐induced ER stress not only upregulated of glucose‐regulated protein 78/94 and IRE1‐ASK1‐JNK pathway but also leading to calpains and Bcl‐2 proteins family involving the release of cytochrome c. The releases into cytosol trigger the cleavage of caspase‐9 and caspase‐3 to induce cell apoptosis. In this study, surfactin demonstrated the potential functions to trigger the ER stress, ER stress‐associated IRE1‐ASK1‐JNK signaling pathway, mitochondrial dysfunction, and caspase activations leading to programmed cell apoptosis. Importantly, implicating the signaling pathway that regulates the connection between ER stress and mitochondrial dysfunction causing apoptosis associated with surfactin. These results indicated a potential application of surfactin strengthen current conventional therapies.
Employer branding and its impact on employees

In this competitive era competition for talented employees has become as important as for customers. Due to this competition it has become very important for an organization to differentiate itself from its competitors and to make better image to attract talented employees and retain existing ones. For this we need a strong HR strategy which is helpful in differentiating an organization from its competitors. Employer branding has emerged as one such HR strategy in the employment context which is helpful in making differences between organizations. The present study aims to evaluate the various aspects and thoughts related to employer branding. The purpose of this paper is to show the impact of employer branding on employees working in corporate sector with the help of literature available. The major finding of this study is that employer branding has a great impact on commitment, retention, satisfaction and loyalty.
Three-Dimensional Tracking of Multiple Small Insects by a Single Camera

Abstract Many systems to monitor insect behavior have been developed recently. Yet most of these can only detect two-dimensional behavior for convenient analysis and exclude other activities, such as jumping or flying. Therefore, the development of a three-dimensional (3D) monitoring system is necessary to investigate the 3D behavior of insects. In such a system, multiple-camera setups are often used to accomplish this purpose. Here, a system with a single camera for tracking small insects in a 3D space is proposed, eliminating the synchronization problems that typically occur when multiple cameras are instead used. With this setup, two other images are obtained via mirrors fixed at other viewing angles. Using the proposed algorithms, the tracking accuracy of five individual drain flies, Clogmia albipunctata (Williston) (Diptera: Psychodidae), flitting about in a spherical arena (78 mm in diameter) is as high as 98.7%, whereas the accuracy of 10 individuals is 96.3%. With this proposed method, the 3D trajectory monitoring experiments of insects can be performed more efficiently.