Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary Material for Tracking Every Thing in the Wild Anonymous

A BDD100K Per-class Evaluation Results We provide per-class evaluation results using CLEARMOT [1] and TETA metrics on the BDD100K [11] validation set in Table 1. Data distribution in BDD100k is long-tailed. The Car category consists of most of the tracks in the dataset. The rest of the categories are rare compared to the dominant ones. Thus, we characterized them as rare classes. TETer can achieve significant improvements across all rare classes on both established MOTA, IDF1, and our TETA metrics compared to the previous state-of-the-art QDtrack. In particular, TETer boosts MOTA of buses by over 7 points on the validation set and TETA by over 6 points. We also compare our TETer results with CEM with its class agnostic counterpart, where the model only uses the AET strategy without CEM. The result shows that our model gains significant improvements over rare classes where the class agnostic instance association cannot be well trained due to lacking annotations. For instance, we gain +3.8 MOTA on buses and +4.7 MOTA on riders. Further, we can observe improvements on the TETA score, where we gain +2 on train and +1.5 on motorcycle. This demonstrates that TETer can better handle tracking rare classes. With CEM, we exploit the semantic annotations offered by large-scale object detection datasets. It can integrate fine-grained cues required for classification (e.g . the difference between a big red bus and a red truck), which are difficult to learn effectively with class-agnostic appearance training on the long-tailed datasets.
Emotional Analysis Using Hybrid Deep Learning Models

: A person’s expressions of feeling are readable from their face, which is widely regarded as the most significant feature of the human body. Detecting and recognizing a person’s face is more accurate and less expensive than other types of biometrics. It is possible to infer a person’s intention and state based on their emotional state, thanks to a modality known as emotion. Within the realm of computer vision research, expression analysis and recognition have emerged as one of the more exciting research topics. New HCI research considers the user’s emotional state to deliver a smooth interface. This study suggests a hybrid deep learning technique for emotion analysis based on face images. The suggested system is an amalgamation of VGG16 and Bidirectional LSTM techniques to classify various emotions on the face. Binary cross-entropy was used as a loss function to optimize the model. The model was taught and tested on the KDEF dataset. Another hybrid model comprising of Conv2D, Maxpooling2D, and Bidirectional LSTM models was tested on the CK+48 dataset. Both models showed efficient performance accuracy in training and testing the face emotion classifications.
The Missing Piece of the Puzzle: How Brand Authenticity Drives Brand Engagement

Amidst a rapidly changing marketplace, sharp behavioral changes, and increased social media usage, brand authenticity, already an important attribute, has become an essential asset for brands. Even though marketing researchers have been studying the topic for more than 25 years, our extensive literature review shows that a widely accepted definition and scale is still lacking. Many extant definitions and scales only address specific aspects of brand authenticity, thus only providing partial coverage. This paper proposes a new definition and amalgamated scale that addresses authenticity holistically in a germane/relatable manner. The disjointed, and often dated, extant definitions and scales require a re-conceptualization of brand authenticity to meet the needs of today’s consumer in relevant, meaningful manner.
New theory of Lorentz violation from a general principle

We report that a general principle of physical independence of mathematical background manifolds brings a replacement of common derivative operators by co-derivative ones. Then we obtain a new Lagrangian for the ordinary minimal standard model with supplementary terms containing the Lorentz invariance violation information measured by a new matrix, denoted as the Lorentz invariance violation matrix. We thus provide a new fundamental theory to study Lorentz invariance violation effects consistently and systematically.
Function-Hiding Decentralized Multi-Client Functional Encryption for Inner Products

Decentralized Multi-Client Functional Encryption (DMCFE) extends the basic functional encryption to multiple clients that do not trust each other. They can independently encrypt the multiple inputs to be given for evaluation to the function embedded in the functional decryption key. And they keep control on these functions as they all have to contribute to the generation of the functional decryption keys. As any encryption scheme, all the FE schemes provide privacy of the plaintexts. But the functions associated to the functional decryption keys might be sensitive too (e.g. a model in machine learning). The function-hiding property has thus been introduced to additionally protect the function evaluated during the decryption process. But it was not properly defined for previous definitions of DMCFE. In this paper, we provide a formal definition of DMCFE with complete function-hiding security game. We thereafter propose a concrete construction of function-hiding DMCFE for inner products, with strong security guarantees: the adversary is allowed to adaptively query multiple challenge ciphertexts and multiple challenge keys. Previous constructions were proven secure for a single challenge ciphertext only, in the selective setting (i.e. provided before the setup).
Online Portfolio Hedging with the Weak Aggregating Algorithm

In this paper we apply the Weak Aggregating Algorithm to find optimal risk management strategies for financial Market Makers (MMs). Here risk is caused by the market exposure. It is effectively represented by the MM’s overall net position , which is the aggregation of all the buy and sell trades carried out by the MM’s clients at a given point in time. So-called hedging strategies are used by MMs to manage their risk and reduce market exposure. In essence, the MM actively places trades in order to reduce its overall net position, keeping it within some predefined bounds and as neutral (or flat) as possible. A flatter net position allows the MM to counter any unfavourable price movements which could otherwise incur a significant loss. We apply the Weak Aggregating Algorithm (WAA) to hedging strategies, which are treated as the experts. We combine their hedging decisions with the goal of reducing portfolio risk and maximising profitability, whilst also attempting to smooth out significant drawdowns. We develop a variation of the WAA using discounting and evaluate the WAA on a subset of real life client risk data in three commonly traded Foreign Exchange (FX) currency symbols: EUR/USD, EUR/GBP and GBP/USD. The results show how varying loss parameters and application of discount factors can enable the WAA to give combinations of hedging strategies that can significantly improve profitability and reduce drawdowns as compared to the benchmark of not hedging
Decoherence-free quantum information in the presence of dynamical evolution

We analyze decoherence-free (DF) quantum information in the presence of an arbitrary non-nearest-neighbor bath-induced system Hamiltonian using a Markovian master equation. We show that the most appropriate encoding for $N$ qubits is probably contained within the $\ensuremath{\sim}\frac{2}{9}N$ excitation subspace. We give a time scale over which one would expect to apply other methods to correct for the system Hamiltonian. In order to remain applicable to experiment, we then focus on small systems, and present examples of DF quantum information for three and four qubits. We give an encoding for four qubits that, while quantum information remains in the two-excitation subspace, protects against an arbitrary bath-induced system Hamiltonian. Although our results are general to any system of qubits that satisfies our assumptions, throughout the paper we use dipole-coupled qubits as an example physical system.
The Inﬂuencing Mechanism of Perceived UGC Quality on Gen Z Consumers’ Loyalty – An Empirical Analysis

Generation Z consumers are low loyalty characterized. They often collect detailed information on UGC’s APP or website. The study demonstrates that UGC content can inﬂuence Gen Z purchasing decisions and can lead to consumer loyalty to the app. This study uses community interaction and community identiﬁcation to explore the impact on customer loyalty and uses variables such as the community climate that Gen Z cares about, perceived UGC quality, and online word of mouth as the antecedent variables that affect community interaction. The research data was collected from 364 Gen Z respondents from the Guangzhou Higher Education Super Center and analyzed using structural equation modeling. It turns out that supportive climate ( β = 0.25, p < 0.001), controlling climate ( β = 0.18, p < 0.001), and perceived UGC quality ( β = 0.44, p < 0.001) had positive effects on community interaction. Community interaction ( β = 0.78, p < 0.001) affects community identiﬁcation, and community identiﬁcation ( β = 0.82, p < 0.001) affects customer loyalty. However, community interaction is not affected by online word of mouth ( β = 0.17, p > 0.05).
Two trapped particles interacting by a finite-range two-body potential in two spatial dimensions

We examine the problem of two particles confined in an isotropic harmonic trap, which interact via a finite-ranged Gaussian-shaped potential in two spatial dimensions. We derive an approximative transcendental equation for the energy and study the resulting spectrum as a function of the interparticle interaction strength. Both the attractive and repulsive systems are analyzed. We study the impact of the potential's range on the ground-state energy. Complementary, we also explicitly verify by a variational treatment that in the zero-range limit the positive delta potential in two dimensions only reproduces the non-interacting results, if the Hilbert space in not truncated. Finally, we establish and discuss the connection between our finite-range treatment and regularized zero-range results from the literature.
Unifying framework for scalar-tensor theories of gravity

A general framework for effective theories propagating two tensor and one scalar degrees of freedom is investigated. Geometrically, it describes dynamical foliation of spacelike hypersurfaces coupled to a general background, in which the scalar mode encodes the fluctuation of the hypersurfaces. Within this framework, various models in the literature---including $k$-essence, Horndeski theory, the effective field theory of inflation, ghost condensate as well as the Ho\v{r}ava gravity---get unified. Our framework generalizes the Horndeski theory in the sense that, it propagates the correct number of degrees of freedom, although the equations of motion are generally higher order. We also identify new operators beyond the Horndeski theory, which yield second order equations of motion for linear perturbations around an a Friedmann-Robertson-Walker background.
A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy

Volumetric laser endomicroscopy (VLE) is an advanced imaging system offering a promising solution for the detection of early Barrett’s esophagus (BE) neoplasia. BE is a known precursor lesion for esophageal adenocarcinoma and is often missed during regular endoscopic surveillance of BE patients. VLE provides a circumferential scan of near-microscopic resolution of the esophageal wall up to 3-mm depth, yielding a large amount of data that is hard to interpret in real time. In a preliminary study on an automated analysis system for ex vivo VLE scans, novel quantitative image features were developed for two previously identified clinical VLE features predictive for BE neoplasia, showing promising results. This paper proposes a novel quantitative image feature for a missing third clinical VLE feature. The novel gland-based image feature called “gland statistics” (GS), is compared to several generic image analysis features and the most promising clinically-inspired feature “layer histogram” (LH). All features are evaluated on a clinical, validated data set consisting of 88 non-dysplastic BE and 34 neoplastic in vivo VLE images for eight different widely-used machine learning methods. The new clinically-inspired feature has on average superior classification accuracy (0.84 AUC) compared to the generic image analysis features (0.61 AUC), as well as comparable performance to the LH feature (0.86 AUC). Also, the LH feature achieves superior classification accuracy compared to the generic image analysis features in vivo, confirming previous ex vivo results. Combining the LH and the novel GS features provides even further improvement of the performance (0.88 AUC), showing great promise for the clinical utility of this algorithm to detect early BE neoplasia.
Excitonic and Valley-Polarization Signatures of Fractional Correlated Electronic Phases in a math

: We have measured the reflectance contrast, photoluminescence, and valley polarization of a WSe 2 /WS 2 moiré superlattice at gate-tunable charge density. We observe absorption modulation of three intralayer moiré excitons at filling factors ν = 1/3 and 2/3. We also observe luminescence modulation of interlayer trions at around a dozen fractional filling factors, including ν = -3/2, 1/4, 1/3, 2/5, 2/3, 6/7, 5/3. Remarkably, the valley polarization of interlayer trions is suppressed at some fractional fillings. These results demonstrate that electron crystallization can modulate the absorption, emission, and valley dynamics of the excitonic states in a moiré superlattice.
Modeling the dust Spectral Energy Distribution of NGC 4214

Abstract We have carried out a detailed modeling of the dust Spectral Energy Distribution (SED) of the nearby, starbursting dwarf galaxy NGC 4214. A key point of our modeling is that we distinguish the emission from (i) HII regions and their associated photodissociation regions (PDRs) and (ii) diffuse dust. For both components we apply templates from the literature calculated with a realistic geometry and including radiation transfer. The large amount of existing data from the ultraviolet (UV) to the radio allows the direct measurement of most of the input parameters of the models. We achieve a good fit for the total dust SED of NGC 4214. In the present contribution we describe the available data, the data reduction and the determination of the model parameters, whereas a description of the general outline of our work is presented in the contribution of Lisenfeld et al. in this volume.
BINARY BLACK HOLE ACCRETION FROM A CIRCUMBINARY DISK: GAS DYNAMICS INSIDE THE CENTRAL CAVITY

We present the results of two-dimensional (2D) hydrodynamical simulations of circumbinary disk accretion using the finite-volume code DISCO. This code solves the 2D viscous Navier–Stokes equations on a high-resolution moving mesh which shears with the fluid flow, greatly reducing advection errors in comparison with a fixed grid. We perform a series of simulations for binary mass ratios in the range 0.026 ⩽ q ⩽ 1.0, each lasting longer than a viscous time so that we reach a quasi-steady accretion state. In each case, we find that gas is efficiently stripped from the inner edge of the circumbinary disk and enters the cavity along accretion streams, which feed persistent “mini disks” surrounding each black hole. We find that for q ≳ 0.1, the binary excites eccentricity in the inner region of the circumbinary disk, creating an overdense lump which gives rise to enhanced periodicity in the accretion rate. The dependence of the periodicity on mass ratio may provide a method for observationally inferring mass ratios from measurements of the accretion rate. We also find that for all mass ratios studied, the magnitude of the accretion onto the secondary is sufficient to drive the binary toward larger mass ratio. This suggests a mechanism for biasing mass-ratio distributions toward equal mass.
RQ4: What was the perceived usefulness of the Lenses of Psychological Safety throughout the

There is growing evidence on the importance of psychological safety, or how comfortable participants feel in sharing their opinions and ideas in a team, in engineering team performance. However, how to support it in engineering student teams has yet to be explored. The goal of this study was to investigate whether a video intervention with assigned roles could foster psychological safety in student engineering teams. In addition, we sought to explore the impact of the frequency of the videos and the utility of the roles on the self-efficacy of students and the perceived psychological safety of the team. Specifically, this study introduces video interventions and the four lenses of psychological safety (Turn-Taking Equalizer, Point of View Shifter, Affirmation Advocate, and Creativity Promoter), and seeks to determine their effectiveness at increasing psychological safety self-efficacy and individual levels of psychological safety. A pilot study was completed with 54 participants (36 males, 17 females, 1 non-binary/third gender) enrolled in a cornerstone engineering design course. Over 10 weeks, data was collected at 5 time points. The results present four key findings. Most notably, 1) a video educating all students about psychological safety in general was effective in improving psychological safety self-efficacy and students retained this information to the end of the project; 2) intervention groups taught to use the four lenses did not have a statistically significant higher level of psychological safety than non- intervention groups; and 3) intervention groups reported using the lenses frequently throughout the design process. These results provide a baseline understanding that is needed to support psychological safety including: when to intervene, how to intervene, and how frequently to intervene.
Optimizing learner assessment in a respiratory therapy clinical simulation course

The purpose of this participatory action research project was to evaluate the effectiveness and objectiveness of learner evaluation methods used in a clinical simulation course offered at a community college as part of the 3-year Respiratory Therapy advanced diploma program. A mixed-method approach to data collection was used. A paper-based questionnaire was completed by 47 participants and was utilized to identify learner satisfaction with the simulation experience. An online questionnaire was completed by 16 participants and utilized to identify learner satisfaction with the evaluation methods used in the course, as well as to gather suggestions for improvement of those methods. Two focus groups further explored 7 participants’ impressions of how evaluation practice in the course affected their learning process and competence in preparation for formal assessment. Data analysis found that the majority of participants were generally satisfied with the current evaluation process considering the practice objective and found that it was effective in helping learners achieve their learning goals. Areas identified for improvement included practice improvement, such as team and communication skills assessment; student self-reflection assessment; and changing the grading system from a numerical grade system to a pass/fail system. The project offers suggestions for future research, including the development of a standard evaluation rubric in high-fidelity simulation in respiratory therapy in Canada based on the national competency profile for entry to practice.
On the Kink Instability of Twisted Coronal Loops: Nonneutralized Electric Current

Based on linear magnetohydrodynamic equations and an energy principle we analyze the ideal kink instability of a twisted force-free coronal loop (flux rope) surrounded by an outer potential magnetic field by using the sharp-boundary approximation at the surface. Unlike Tsap et al., a magnetic flux rope with nonneutralized (uncompensated) electric current is considered. We have shown that the twist angle of magnetic field lines is closely related to coronal mass ejections and solar flares. The kink instability condition does not depend on the radial profile of the magnetic field inside a flux rope in the long-wavelength limit but depends strongly on the reverse electric currents at the surface. The total critical twist angle of magnetic field lines, which determines the kink instability threshold, can be much greater than π radians due to reverse azimuthal surface current. This agrees with observations and illustrates the importance of the role of reverse currents for stabilization of a flux rope. Additional arguments in favor of the energy release models based on the uncompensated electric currents are presented.
Investigating the laser metal deposition of Inconel 718 superalloy using the numerical and experimental methods

In this research, a volume of fluid (VOF) model was developed to investigate the effect of laser deposition parameters on the geometry of molten pool and deposited layer in the Inconel 718 superalloy. For this purpose, the process parameters of laser power, laser beam diameter and scanning speed were considered to estimate the geometric characteristics of the molten pool and deposited layer. In the following, the laser deposition process of Inconel 718 superalloy was carried out experimentally to validate the results of simulation. It was observed that an increase in the laser power from 100 to 400 W resulted in an improvement in the length, width and depth of molten pool, while an increase in the values of laser beam diameter (from 1 to 2 mm) and scanning speed (from 2 to 10 mm s−1) was associated with a reduction in the length, width and depth of molten pool. From the results of both simulation and experiments, a reduction was observed in the wetting angle of deposited layer when the values of laser power and scanning speed increased up to 400 W and 10 mm s−1 respectively, while the increase of feeding rate from 48 to 62 mgr s−1 indicated an improvement in the wetting angle. It was also observed that the maximum penetration depth was obtained in the rear part of molten pool due to Marangoni convection currents that pushed the melt toward the end of molten pool. The comparison of the experimental results and those predicted by the VOF model indicated that the model is capable of predicting the shape of deposition layer with sensible error.
THERMAL DIAGNOSTICS WITH THE ATMOSPHERIC IMAGING ASSEMBLY ON BOARD THE SOLAR DYNAMICS OBSERVATORY: A VALIDATED METHOD FOR DIFFERENTIAL EMISSION MEASURE INVERSIONS

We present a new method for performing differential emission measure (DEM) inversions on narrow-band EUV images from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory. The method yields positive definite DEM solutions by solving a linear program. This method has been validated against a diverse set of thermal models of varying complexity and realism. These include (1) idealized Gaussian DEM distributions, (2) 3D models of NOAA Active Region 11158 comprising quasi-steady loop atmospheres in a nonlinear force-free field, and (3) thermodynamic models from a fully compressible, 3D MHD simulation of active region (AR) corona formation following magnetic flux emergence. We then present results from the application of the method to AIA observations of Active Region 11158, comparing the region's thermal structure on two successive solar rotations. Additionally, we show how the DEM inversion method can be adapted to simultaneously invert AIA and Hinode X-ray Telescope data, and how supplementing AIA data with the latter improves the inversion result. The speed of the method allows for routine production of DEM maps, thus facilitating science studies that require tracking of the thermal structure of the solar corona in time and space.