Miracle-dz/UniFAR-FTUs huggingface dataset:
Robot Theory of Mind with Reverse Psychology

Theory of mind (ToM) corresponds to the human ability to infer other people's desires, beliefs, and intentions. Acquisition of ToM skills is crucial to obtain a natural interaction between robots and humans. A core component of ToM is the ability to attribute false beliefs. In this paper, a collaborative robot tries to assist a human partner who plays a trust-based card game against another human. The robot infers its partner's trust in the robot's decision system via reinforcement learning. Robot ToM refers to the ability to implicitly anticipate the human collaborator's strategy and inject the prediction into its optimal decision model for a better team performance. In our experiments, the robot learns when its human partner does not trust the robot and consequently gives recommendations in its optimal policy to ensure the effectiveness of team performance. The interesting finding is that the optimal robotic policy attempts to use reverse psychology on its human collaborator when trust is low. This finding will provide guidance for the study of a trustworthy robot decision model with a human partner in the loop.
The Impact of Robot's Body Language on Customer Experience: An Analysis in a Cafe Setting

Nonverbal communication plays a crucial role in human-robot interaction (HRI) and have been widely used for robots in service environments. While few studies have addressed the understanding customer's acceptance of robots under many different interaction conditions, the impact of robots' nonverbal interaction modalities (i.e., a combination of body language, voice, and touch) on customers' experience has not been investigated truly. To this end, in this paper, we introduce an HRI framework that aims to assist customers in their food and beverage choices in a real-world cafe setting. With this framework, the contribution of this paper are two folds. We introduce a time-synchronised multisensory HRI dataset comprising the interactions between a social robot and customers in a real-world environment. We conduct a user study to evaluate the configuration of multimodal HRI framework, particularly nonverbal gestures, and its contribution to customers' interaction experience in this specific marketing setting.
Who to Teach a Robot to Facilitate Multi-party Social Interactions?

One salient function of social robots is to play the role of facilitator to enhance the harmony state of multi-party social interactions so that every human participant is encouraged and motivated to engage actively. However, it is challenging to handcraft the behavior of social robots to achieve this objective. One promising approach is for the robot to learn from human teachers. This paper reports the findings of an empirical test to determine the optimal experiment condition for a robot to learn verbal and nonverbal strategies to facilitate a multi-party interaction. First, the modified L8 Orthogonal Array (OA) is used to design a fractional factorial experiment condition using factors like the type of human facilitator, group size and stimulus type. The response of OA is the harmony state explicitly defined using the speech turn-taking between speakers and represented using metrics extracted from the first order Markov transition matrix. Analyses of Main Effects and ANOVA suggest the type of human facilitator and group size are significant factors affecting the harmony state. Therefore, we propose the optimal experiment condition to train a facilitator robot using high school teachers as human teachers and group size larger than four participants.
Development of a University Guidance and Information Robot

We are developing a social robot that will be deployed in a large, recently-built university building designed for learning and teaching. We outline the design process for this robot, which has included consultations with stakeholders including members of university services, students and other visitors to the building, as well as members of the "Reach Out'' team who normally provide in-person support in the building. These consultations have resulted in a clear specification of the desired robot functionality, which will combine central helpdesk queries with local information about the building and the surrounding university campus. We outline the technical components that will be used to develop the robot system, and also describe how the success of the deployed robot will be evaluated.
What Skin Is Your Robot In?

The use of socially assistive robots is able to alleviate some depression symptoms, according to existing research. However, due to comorbidities that often accompany depression and the unique experiences of each individual, it is necessary to get a better understanding of how SARs should be personalized. Through 10 hourlong workshops with 10 individuals living with depression, we explored the customization of a zoomorphic SAR for adults with depression. By using the SAR Therabot? as a base platform, participants designed their own unique covering for the robot, and discussed desired robot behaviors and privacy concerns around data collection. Though the physical designs of the robots varied greatly, participants expressed common themes regarding their preference for a soft touchable exterior, comfort with sharing data with their therapists, and interest in the robot producing more realistic sounds and movements, among other design features.
Recursive gain controller for strict feedback systems

This paper presents a new solution to the ‘explosion of complexity’ problem arising from the repeated derivations of the virtual controls in Standard Backstepping approach. It is inspired by the principle of separation and is based on the use of ‘fictitious’ signals. This solution avoids the use of filters to approximate the virtual controls first derivatives as it is done both in Command Filtered Backstepping and Dynamic Surface Control and ensures global and exponential convergence of the tracking errors to zero within balls of radii that can be made as small as desired. This proposed solution, referred to as Recursive Gain Controller, is applicable to more wider class of strict feedback systems as it requires less conservative assumptions. Simulation results are given to illustrate the effectiveness of the proposed approach. It is shown that the Recursive Gain Controller has better performance than the Standard Backstepping approach.
Measurement of optical rotation in crystals

Abstract In the last few years, new techniques have been devised to measure optical rotation in cases where the linear birefringence dominates and this has opened up new fields of research into the phenomenon of optical rotation. In this paper, the phenomenon of optical rotation will be discussed with respect to our knowledge of crystal structure and a new measurement technique based on tilting of the crystal will be described.
You can move, but you can’t hide: identification of mobile genetic elements with geNomad

Identifying and characterizing mobile genetic elements (MGEs) in sequencing data is essential for understanding their diversity, ecology, biotechnological applications, and impact on public health. Here, we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a large dataset of marker proteins to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks that included diverse MGE and chromosome sequences, geNomad significantly outperformed other tools in all evaluated clades of plasmids and viruses. Leveraging geNomad’s speed and scalability, we were able to process public metagenomes and metatranscriptomes, leading to the discovery of millions of new viruses and plasmids that are available through the IMG/VR and IMG/PR databases. We anticipate that geNomad will enable further advancements in MGE research, and it is available at https://portal.nersc. gov/genomad.
Glioblastoma-instructed microglia transition to heterogeneous phenotypic states with phagocytic and dendritic cell-like features in patient tumors and patient-derived orthotopic xenografts

Background A major contributing factor to glioblastoma (GBM) development and progression is its ability to evade the immune system by creating an immune-suppressive environment, where GBM-associated myeloid cells, including resident microglia and peripheral monocyte-derived macrophages, play critical pro-tumoral roles. However, it is unclear whether recruited myeloid cells are phenotypically and functionally identical in GBM patients and whether this heterogeneity is recapitulated in patient-derived orthotopic xenografts (PDOXs). A thorough understanding of the GBM ecosystem and its recapitulation in preclinical models is currently missing, leading to inaccurate results and failures of clinical trials. Methods Here, we report systematic characterization of the tumor microenvironment (TME) in GBM PDOXs and patient tumors at the single-cell and spatial levels. We applied single-cell RNA-sequencing, spatial transcriptomics, multicolor flow cytometry, immunohistochemistry and functional studies to examine the heterogeneous TME instructed by GBM cells. GBM PDOXs representing different tumor phenotypes were compared to glioma mouse GL261 syngeneic model and patient tumors. Results We show that GBM tumor cells reciprocally interact with host cells to create a GBM patient-specific TME in PDOXs. We detected the most prominent transcriptomic adaptations in myeloid cells, with brain-resident microglia representing the main population in the cellular tumor, while peripheral-derived myeloid cells infiltrated the brain at sites of blood-brain barrier disruption. More specifically, we show that GBM-educated microglia undergo transition to diverse phenotypic states across distinct GBM landscapes and tumor niches. GBM-educated microglia subsets display phagocytic and dendritic cell-like gene expression programs. Additionally, we found novel microglial states expressing cell cycle programs, astrocytic or endothelial markers. Lastly, we show that temozolomide treatment leads to transcriptomic plasticity and altered crosstalk between GBM tumor cells and adjacent TME components. Conclusion Our data provide novel insights into the phenotypic adaptation of the heterogeneous TME instructed by GBM tumors. We show the key role of microglial phenotypic states in supporting GBM tumor growth and response to treatment. Our data place PDOXs as relevant models to assess the functionality of the TME and changes in the GBM ecosystem upon treatment.
Identification of a carbohydrate recognition motif of purinergic receptors

As a major class of biomolecules, carbohydrates play indispensable roles in various biological processes. However, it remains largely unknown how carbohydrates directly modulate important drug targets, such as G-protein coupled receptors (GPCRs). Here, we employed P2Y purinoceptor 14 (P2Y14), a drug target for inflammation and immune responses, to uncover the sugar nucleotide activation of GPCRs. Integrating molecular dynamics simulation with functional study, we identified the uridine diphosphate (UDP)-sugar-binding site on P2Y14, and revealed that a UDP-glucose might activate the receptor by bridging the transmembrane helices (TM) 2 and 7. Between TM2 and TM7 of P2Y14, a conserved salt bridging chain (K2.60-D2.64-K7.35-E7.36, KDKE) was identified to distinguish different UDP-sugars, including UDP-glucose, UDP-galactose, UDP-glucuronic acid and UDP-N-acetylglucosamine. We identified the KDKE chain as a conserved functional motif of sugar binding for both P2Y14 and P2Y purinoceptor 12 (P2Y12), and then designed three sugar nucleotides as agonists of P2Y12. These results not only expand our understanding for activation of purinergic receptors but also provide insights for the carbohydrate drug development for GPCRs.
KSHV uses viral IL6 to expand infected immunosuppressive macrophages

Kaposi’s sarcoma-associated herpesvirus (KSHV) is an oncogenic double-stranded DNA virus and the etiologic agent of Kaposi’s sarcoma and hyperinflammatory lymphoproliferative disorders. Understanding the mechanism by which KSHV increases the infected cell population is crucial for curing KSHV-associated diseases. Here we demonstrate that KSHV preferentially infects CD14+ monocytes and sustains viral replication through the viral interleukin-6 (vIL6)-mediated activation of STAT1 and 3. Using vIL6-sufficient and vIL6-deficient recombinant KSHV, we demonstrated that vIL6 plays a critical role in promoting the proliferation and differentiation of KSHV-infected monocytes into macrophages. The macrophages derived from vIL6-sufficient KSHV infection showed a distinct transcriptional profile of elevated IFN-pathway activation with immune suppression and were compromised in T-cell stimulation function compared to those from vIL6-deficient KSHV infection or uninfected control. These results highlight a clever strategy, in which KSHV utilizes vIL6 to secure its viral pool by expanding infected dysfunctional macrophages. This mechanism also facilitates KSHV to escape from host immune surveillance and to establish a lifelong infection. 160 Summary KSHV causes multiple inflammatory diseases, however, the underlying mechanism is not clear. Shimoda et al. demonstrate that KSHV preferentially infects monocytes and utilizes virally encoded interleukin-6 to expand and deregulate infected monocytes. This helps the virus escape from host immune surveillance.
Machines learn ecological networks: automated discovery of ecological networks based on empirical data

Constructing ecological networks is known to be important and challenging in community ecology. In particular, to construct the holistic structure of ecological networks, identifying species interaction is essential but often costly and impalpable. Recent studies providing major challenges in assembling ecological networks have highlighted the need of new and more powerful approaches to reconstruct biological networks, including species interaction networks. In literature, there are no promising verifications in using machine leaning (ML) approaches to reconstruct ecological networks. In this work, we develop and employ a variety of ML methods, including penalized regression and graphical tools, to reconstruct ecological networks. For evaluation, we apply the methods to empirical time series data sets of 20 species abundances collected at Lake Constance in central Europe. We use resampled data to identify highly-ranked interactions among species and measure their consistency across 7 ML methods and 5,000 learning processes. We show that the best precision, recall, and F1 score were 0.48, 0.97, and 0.64, respectively, among all penalized regression methods under comparison. In summary, our study shows that machine learning methods offer promising data-driven and automated tools for reconstructing ecological networks and discovering underlying biological interactions among species.
Chlamydomonas Mutants Null for Chloroplast Triose Phosphate Transporter3 are Metabolically Compromised and Light Sensitive

Modulation of export of photoassimilates from the chloroplast is essential for controlling the distribution of fixed carbon in the cell and maintaining optimum photosynthetic rates. In this study we identified chloroplast triose phosphate/phosphate translocators 2 and 3 (CreTPT2 and CreTPT3) in the green alga Chlamydomonas reinhardtii that exhibited similar substrate specificities but were differentially expressed over the diel cycle. We focused mostly on analyzing CreTPT3 because of its high level of expression and the severe phenotype exhibited by tpt3 relative to the tpt2 mutants. Null mutants for CreTPT3 had a pleiotropic phenotype that impacted growth, photosynthetic activities, metabolite profiles, carbon partitioning, and organelle-specific accumulation of H2O2. These analyses demonstrated that CreTPT3 is a dominant conduit on the chloroplast envelope for the transport of photoassimilate. In addition, CreTPT3 can serve as a safety valve that moves excess reductant out of the chloroplast and appears to be essential for preventing the cells from experiencing oxidative stress and accumulating of reactive oxygen species, even under low/moderate light intensities. Finally, our studies indicate subfunctionalization of the CreTPT transporters and suggest that there are differences in managing the export of photoassimilates from the chloroplasts of Chlamydomonas and vascular plants.
Multiple scattering in the high-frequency limit with second-order shadowing function from 2D anisotropic rough dielectric surfaces: I. Theoretical study

Abstract In this paper the first- and second-order Kirchhoff approximation is applied to study the backscattering enhancement phenomenon, which appears when the surface rms slope is greater than 0.5. The formulation is reduced to the geometric optics approximation in which the second-order illumination function is taken into account. This study is developed for a two-dimensional (2D) anisotropic stationary rough dielectric surface and for any surface slope and height distributions assumed to be statistically even. Using the Weyl representation of the Green function (which introduces an absolute value over the surface elevation in the phase term), the incoherent scattering coefficient under the stationary phase assumption is expressed as the sum of three terms. The incoherent scattering coefficient then requires the numerical computation of a ten- dimensional integral. To reduce the number of numerical integrations, the geometric optics approximation is applied, which assumes that the correlation between two adjacent points is very strong. The model is then proportional to two surface slope probabilities, for which the slopes would specularly reflect the beams in the double scattering process. In addition, the slope distributions are related with each other by a propagating function, which accounts for the second-order illumination function. The companion paper is devoted to the simulation of this model and comparisons with an ‘exact’ numerical method.
Depth encoding multicrystal detectors for PET

A detector unit having a depth encoding scheme was designed and an experimental study has been performed to test the new idea for a positron camera detector. The detector unit consists of a multistage rectangular block of scintillation crystals optically coupled at the bottom face to four photomultiplier tubes (PMTs) in a 2/spl times/2 array or to a position sensitive photomultiplier tube. Each stage of the crystal block consists of four elements in a 2/spl times/2 array. Each scintillation event is mapped in a two dimensional (2D) distribution through the relative ratio of the output signals of the PMTs. The three dimensional location in the detector unit projects into the 2D map so that four top crystals correspond to four innermost regions in the map, and the four bottom crystals facing the PMTs correspond to four outermost regions. With appropriate light control in such a crystal block of Gd/sub 2/SiO/sub 5/:Ce coupled to four PMTs, good positioning performance to encode the depth-of-interaction was obtained for Cs-137 gamma-rays incidence.
Adaptive robust dynamic surface control with composite adaptation laws

This paper focuses on an adaptive robust dynamic surface control (ARDSC) with composite adaptation laws (CAL) for a class of uncertain nonlinear systems in semi‐strict feedback form. A simple and effective controller has been obtained by introducing dynamic surface control (DSC) technique and designing novel adaptation laws. First, the ‘explosion of terms’ problem caused by backstepping method in the traditional adaptive robust control (ARC) is avoided. Meanwhile, through a new proof philosophy the asymptotical output tracking that the ARC possesses is theoretically preserved. Second, when persistent excitation (PE) condition satisfies, true parameter estimates could be acquired via designing CALs which integrate the information of estimation errors. Finally, simulation results are presented to illustrate the effectiveness of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.
Development of a Business Logic using Simple Application of Rough Set Theory

In today’s scenario, business establishment usually fails due to improper planning and lack of business knowledge. In this paper, we develop a concept which is precise and reduces the number of data attributes to start any business. For the development of the concept we have used the concept of rough set idea for reduction of number of attributes. Initially, we take a survey of 1000 samples and consider the best 20 samples for our purpose. Using rough set theory, we consider the most essential attributes out of several attributes for a business development.
Quantitative analysis of complex targets by proton‐induced x rays

We have explored the potential of proton‐induced x‐ray spectroscopy as a quantitative analytical technique to determine the composition of a homogeneous complex target. Direct production of x ray by protons in the low MeV range was considered using the binary encounter approximation and published data on the atomic number dependance of the stopping power. Also included is a correction scheme for secondary excitation processes if radiation is generated in the target capable of fluorescence excitation. The formalism of the correction scheme is closely related to the one commonly employed in electron microprobe spectroscopy. Our data indicate that the accuracy achievable in IIXS is of the order of ±5%, if one considers the two important corrections: (1) the difference in the stopping power properties of the composite target compared to the pure element target; (2) secondary excitation processes if required.
On Two Iterative Least-Squares Finite Element Schemes for the Incompressible Navier–Stokes Problem

This paper is mainly devoted to a comparative study of two iterative least-squares finite element schemes for solving the stationary incompressible Navier–Stokes equations with velocity boundary condition. Introducing vorticity as an additional unknown variable, we recast the Navier–Stokes problem into a first-order quasilinear velocity–vorticity–pressure system. Two Picard-type iterative least-squares finite element schemes are proposed to approximate the solution to the nonlinear first-order problem. In each iteration, we adopt the usual L 2 least-squares scheme or a weighted L 2 least-squares scheme to solve the corresponding Oseen problem and provide error estimates. We concentrate on two-dimensional model problems using continuous piecewise polynomial finite elements on uniform meshes for both iterative least-squares schemes. Numerical evidences show that the iterative L 2 least-squares scheme is somewhat suitable for low Reynolds number flow problems, whereas for flows with relatively higher Reynolds numbers the iterative weighted L 2 least-squares scheme seems to be better than the iterative L 2 least-squares scheme. Numerical simulations of the two-dimensional driven cavity flow are presented to demonstrate the effectiveness of the iterative least-squares finite element approach.