Miracle-dz/UniFAR-FTUs huggingface dataset:
Respecting Transfer Gap in Knowledge Distillation

Knowledge distillation (KD) is essentially a process of transferring a teacher model's behavior, e.g., network response, to a student model. The network response serves as additional supervision to formulate the machine domain, which uses the data collected from the human domain as a transfer set. Traditional KD methods hold an underlying assumption that the data collected in both human domain and machine domain are both independent and identically distributed (IID). We point out that this naive assumption is unrealistic and there is indeed a transfer gap between the two domains. Although the gap offers the student model external knowledge from the machine domain, the imbalanced teacher knowledge would make us incorrectly estimate how much to transfer from teacher to student per sample on the non-IID transfer set. To tackle this challenge, we propose Inverse Probability Weighting Distillation (IPWD) that estimates the propensity score of a training sample belonging to the machine domain, and assigns its inverse amount to compensate for under-represented samples. Experiments on CIFAR-100 and ImageNet demonstrate the effectiveness of IPWD for both two-stage distillation and one-stage self-distillation.
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation

Adversarial Examples Detection (AED) is a crucial defense technique against adversarial attacks and has drawn increasing attention from the Natural Language Processing (NLP) community. Despite the surge of new AED methods, our studies show that existing methods heavily rely on a shortcut to achieve good performance. In other words, current search-based adversarial attacks in NLP stop once model predictions change, and thus most adversarial examples generated by those attacks are located near model decision boundaries. To surpass this shortcut and fairly evaluate AED methods, we propose to test AED methods with Far Boundary (FB) adversarial examples. Existing methods show worse than random guess performance under this scenario. To overcome this limitation, we propose a new technique, ADDMU, adversary detection with data and model uncertainty, which combines two types of uncertainty estimation for both regular and FB adversarial example detection. Our new method outperforms previous methods by 3.6 and 6.0 AUC points under each scenario. Finally, our analysis shows that the two types of uncertainty provided by ADDMU can be leveraged to characterize adversarialexamples and identify the ones that contribute most to model’s robustness in adversarial training.
Flow-Level Packet Loss Detection via Sketch Decomposition and Matrix Optimization

For cloud service providers, ﬁne-grained packet loss detection across data centers is crucial in improving their service level and increasing business income. However, the inability to obtain suﬃcient measurements makes it diﬃcult owing to the fundamental limit that the wide-area network links responsible for communication are not under their management. Moreover, millisecond-level delay jitter and clock synchronization errors in the WAN disable many tools that perform well in data center networks on this issue. Therefore, there is an urgent need to develop a new tool or method. In this work, we propose SketchDecomp, a novel loss detection method, from a mathematical perspective that has never been considered before. Its key is to decompose sketches upstream and downstream into several sub-sketches and builds a low-rank matrix optimization model to solve them. Extensive experiments on the test bed demonstrate its superiority.
Hierarchical Dirichlet process and relative entropy

The Hierarchical Dirichlet process is a discrete random measure serving as an im-portant prior in Bayesian non-parametrics. It is motivated with the study of groups of clustered data. Each group is modelled through a level two Dirichlet process and all groups share the same base distribution which itself is a drawn from a level one Dirichlet process. It has two concentration parameters with one at each level. The main results of the paper are the law of large numbers and large deviations for the hierarchical Dirichlet process and its mass when both concentration parameters converge to inﬁnity. The large deviation rate functions are identiﬁed explicitly. The rate function for the hierarchical Dirichlet process consists of two terms corresponding to the relative entropies at each level. It is less than the rate function for the Dirichlet process, which reﬂects the fact that the number of clusters under the hierarchical Dirichlet process has a slower growth rate than under the Dirichlet process.
A Task-aware Dual Similarity Network for Fine-grained Few-shot Learning

The goal of fine-grained few-shot learning is to recognize sub-categories under the same super-category by learning few labeled samples. Most of the recent approaches adopt a single similarity measure, that is, global or local measure alone. However, for fine-grained images with high intra-class variance and low inter-class variance, exploring global invariant features and discriminative local details is quite essential. In this paper, we propose a Task-aware Dual Similarity Network(TDSNet), which applies global features and local patches to achieve better performance. Specifically, a local feature enhancement module is adopted to activate the features with strong discriminability. Besides, task-aware attention exploits the important patches among the entire task. Finally, both the class prototypes obtained by global features and discriminative local patches are employed for prediction. Extensive experiments on three fine-grained datasets demonstrate that the proposed TDSNet achieves competitive performance by comparing with other state-of-the-art algorithms.
Augmenting Task-Oriented Dialogue Systems with Relation Extraction

The standard task-oriented dialogue pipeline uses intent clas- siﬁcation and slot-ﬁlling to interpret user utterances. While this approach can handle a wide range of queries, it does not extract the information needed to handle more complex queries that contain relationships between slots. We propose integration of relation extraction into this pipeline as an effective way to expand the capabilities of dialogue systems. We evaluate our approach by using an internal dataset with slot and relation annotations spanning three domains. Finally, we show how slot-ﬁlling annotation schemes can be simpliﬁed once the expressive power of relation annotations is available, reducing the number of slots while still capturing the user’s intended meaning.
How long does it take to form the Andreev quasiparticles

We study transient effects in a setup, where the quantum dot (QD) is abruptly sandwiched between the metallic and superconducting leads. Focusing on the proximity-induced electron pairing, manifested by the in-gap bound states, we determine characteristic time-scale needed for these quasiparticles to develop. In particular, we derive analytic expressions for (i) charge occupancy of the QD, (ii) amplitude of the induced electron pairing, and (iii) the transient currents under equilibrium and nonequilibrium conditions. We also investigate the correlation effects within the Hartree-Fock-Bogolubov approximation, revealing a competition between the Coulomb interactions and electron pairing.
Gravitational-wave memory: Waveforms and phenomenology

The non-linear gravitational-wave memory effect is a prediction of general relativity in which test masses are permanently displaced by gravitational radiation. We implement a method for calculating the expected memory waveform from an oscillatory gravitational-wave time series. We use this method to explore the phenomenology of gravitational-wave memory using a numerical relativity surrogate model. Previous methods of calculating the memory have considered only the dominant oscillatory ($\ell=2$, $m=|2|$) mode in the spherical harmonic decomposition or the post-Newtonian expansion. We explore the contribution of higher-order modes and reveal a richer phenomenology than is apparent with $\ell=|m|=2$ modes alone. We also consider the `memory of the memory' in which the memory is, itself, a source of memory, which leads to a small, $O\left(10^{-4}\right)$, correction to the memory waveform. The method is implemented in the python package {\tt\sc GWMemory}, which is made publicly available.
A hybrid finite element/neural network solver and its application to the Poisson problem

We analyze a hybrid method that enriches coarse grid finite element solutions with fine scale fluctuations obtained from a neural network. The idea stems from the Deep Neural Network Multigrid Solver (DNN‐MG) which embeds a neural network into a multigrid hierarchy by solving coarse grid levels directly and predicting the corrections on fine grid levels locally (e.g., on small patches that consist of several cells) by a neural network. Such local designs are quite appealing, as they allow a very good generalizability. In this work, we formalize the method and describe main components of the a‐priori error analysis. Moreover, we numerically investigate how the size of training set affects the solution quality.
Conditioning and Hessians in analytical and numerical optimization - Some illustrations

This note illustrates the connections between the Hessians of numerical optimization problems, variance-covariance matrices for parameter vectors, and the influence that data mismeasurement may have on parameter estimates. Condition numbers provide a central guide to the sensitivity of common numerical problems to data mismeasurement. Examples are provided that clarify their importance. Two simple prescriptions arise from this analysis. First, data must be of an appropriate scale. In some cases this means that the data need similar means and similar variances. Second, in numerical algorithms it is desirable to ascertain the condition number of the Hessian implied by the initial parameter values used for numerical optimisation algorithms. Condition numbers are easy to compute and indicate whether the updates from an initial starting value are likely to be poor.
Magnetoresistance oscillations in two-dimensional electron systems under monochromatic and bichromatic radiations

The magnetoresistance oscillations in high-mobility two-dimensional electron systems induced by two radiation fields of frequencies 31 and 47GHz are analyzed in a wide magnetic-field range down to 100G, using the balance-equation approach to magnetotransport for high-carrier-density systems. The frequency mixing processes are shown to be important. The predicted peak positions, relative heights, radiation-intensity dependence and their relation with monochromatic resistivities are in good agreement with recent experimental finding [M. A. Zudov et al., Phys. Rev. Lett. 96, 236804 (2006)].
Clinical Significance of Applause Sign in Patients with Progressive Supranuclear Palsy

ABSTRACT: Applause sign (AS) is a clinical phenomenon observed in several neurological disorders including progressive supranuclear palsy (PSP). We investigated the factors associated with AS in patients with PSP. PSP patients with AS showed greater motor impairment compared to those with negative AS. Global cognition including attention and memory were affected more in patients with positive AS. We also observed that gait variability, a known marker for unstable gait is pronounced in those who presented with positive AS. Hence, the clinician might expect a more severe disease and an unstable gait in a PSP patient presenting with AS.
The K2-ESPRINT project. VI. K2-105 b, a hot Neptune around a metal-rich G-dwarf

We report on the confirmation that the candidate transits observed for the star EPIC 211525389 are due to a short-period Neptune-sized planet. The host star, located in K2 campaign field 5, is a metal-rich ([Fe/H] = 0.26$\pm$0.05) G-dwarf (T_eff = 5430$\pm$70 K and log g = 4.48$\pm$0.09), based on observations with the High Dispersion Spectrograph (HDS) on the Subaru 8.2m telescope. High-spatial resolution AO imaging with HiCIAO on the Subaru telescope excludes faint companions near the host star, and the false positive probability of this target is found to be <$10^{-6}$ using the open source vespa code. A joint analysis of transit light curves from K2 and additional ground-based multi-color transit photometry with MuSCAT on the Okayama 1.88m telescope gives the orbital period of P = 8.266902$\pm$0.000070 days and consistent transit depths of $R_p/R_\star \sim 0.035$ or $(R_p/R_\star)^2 \sim 0.0012$. The transit depth corresponds to a planetary radius of $R_p = 3. 59_{-0.39}^{+0.44} R_{\oplus}$, indicating that EPIC 211525389 b is a short-period Neptune-sized planet. Radial velocities of the host star, obtained with the Subaru HDS, lead to a 3\sigma\ upper limit of 90 $M_{\oplus} (0.00027 M_{\odot})$ on the mass of EPIC 211525389 b, confirming its planetary nature. We expect this planet, newly named K2-105 b, to be the subject of future studies to characterize its mass, atmosphere, spin-orbit (mis)alignment, as well as investigate the possibility of additional planets in the system.
A Vision Towards Generated Assistive Systems for Supporting Human Interactions in Production

: Human workers need to cope with complex production settings when handling and monitoring cyber-physical production systems. Assistive systems can provide situational step-by-step support for human behavior, e.g., when interacting with a machine or for manual assembly. These systems need to take personal knowledge, workers skills or personal restrictions into account and are therefore subject to privacy concerns. However, the engineering of such interactive assistive systems within the production domain is a complex task as they might support critical functionality in dangerous environments and have a high need for safety and privacy considerations due to processing personal data. We want to investigate how the software engineering process of assistive systems in production can be improved to achieve higher reusability. Current research focuses on specific use cases and implements systems specifically for those needs without reusability in mind. We suggest using behavior and context models in a generative approach, to create a reusable method to engineer assistive systems for production environments, either as own applications or as services integrated within digital twins. We have already applied model-driven methods for assistive systems in the smart home domain and discuss the opportunities and challenges of an application of these methods for the production domain. These methods can facilitate the engineering of assistive functionalities within applications in production while meeting privacy, adaptability, and context-sensitivity requirements.
Spin relaxation and antisymmetric exchange in n -doped III-V semiconductors

Recently, Kavokin [Phys. Rev. B 64, 075305 (2001)] suggested that the Dzyaloshinskii-Moriya interaction between localized electrons governs slow spin relaxation in n-doped GaAs in the regime close to the metal-insulator transition. We derive the correct spin Hamiltonian and apply it to the determination of spin dephasing time using the method of moments expansion. Our estimates would give longer than the observed values of the spin-relaxation time.
Digital Marketing: Buying Behaviour Practices of Medical Professionals

The modernization has emphasized the pattern of living on a fast pace and additionally this velocity has produced an alteration on our day to day activities conversely Digital marketing at once created a phenomenon in transforming the lifestyle for everyone in adopting the current circumstances. Haryana is one of the medically advanced states of India and the Medical Professionals contribute a lot towards its realization. The main objective of this study is to realize the acquaintance of Digital Marketing by Medical Professionals with Gender as a Dependent Variable besides Buying Behaviour Practices, User Friendly Behaviour, Previous Buying Behaviour, Success Factors and Hindrances Factors respectively as its predictors. The study uses a survey analysis for 150 medical practitioners across Haryana. The results depict there is a encouraging correlation between Buying Behaviour Practices and Previous Buying Behaviour at 0.51 and Durbin-Watson index is 1.31, which is suitable for Digital Marketing Model.
Development and Psychometric Properties of the COVID-19 New Coronavirus Awareness Scale for University Students

Background: Many university students gained knowledge about COVID-19 pandemic through social media and distance education. Their knowledge, attitude, and behavior related pandemic can be evaluated to take effective measures. Objectives: This study developed the COVID-19 New Coronavirus Awareness Scale (CV19S-CAS) and determined the psychometric properties in university students. Methodology: The sample comprised 577 Turkish university students via an online survey. The items of the CV19S-CAS constructed based on the review of the literature, and expert evaluations. The psychometric tests conducted for the reliability and validity properties. Results: After the expert review and pilot study, the reliability and validity analysis conducted. According to the exploratory factor analysis, the CV19S-CAS consisted of four sub-scales (knowledge, protection, attitude, behavior), and the sub-scales explained 46.1% of the total variance. Six items excluded from the scale because the factor loads were below 0.30. Cronbach's alpha coefficient of the CV19S-CAS-26 items was 0.87;four sub-scales were 0.62, 0.57, 0.71, and 0.82, respectively. Conclusion: The Five-point Likert-CV19S-CAS, a 26-item scale, with four factors and sub-scales, is a valid and reliable scale for university students. It can also use for the level of COVID-19 awareness among the general population.
Supplementary Material of Detecting and Recovering Sequential DeepFake Manipulation

Implementation is in PyTorch. To be comparable in the number of parameters, we adopt a transformer model with 2 encoder and 2 decoder layers with 4 attention heads. For the training schedule, we employ a 20-epochs warm-up strategy. The initial learning rate is set as 1e − 3 for transformer part and 1e − 4 for CNN part, with a decay factor of 10 at 70 and 120 epochs, totally 170 epochs. We use the SGD momentum optimizer with weight decay set as 1e− 4. We use a mini-batch size of 32 per GPU and 4 GPUs in total. We set λ = 4. Model selection for evaluation is conducted by considering the trained model that has produced the best accuracy on the validation set.
How do rural communities sustain latrine coverage and use? Qualitative comparative analyses in Cambodia and Ghana

: Community-led Total Sanitation (CLTS) is a popular intervention for eliminating open defecation in rural communities. Previous research has explored the contextual and programmatic factors that influence CLTS performance. Less is known about the community-level conditions that sustain latrine coverage and use. We hypothesized three categories of community conditions un-13 derlying CLTS sustainability: (i) engagement of community leaders, (ii) follow-up intensity, and (iii) support to poor households. We evaluated these among communities in Cambodia and Ghana, and applied fuzzy-set Qualitative Comparative Analysis (fsQCA) to identify combinations of conditions that influenced current latrine coverage and consistent latrine use. In Cambodia, latrine coverage was highest in communities with active commune rather than traditional leaders, and with leaders who used casual approaches for promoting latrine construction. Latrine use in Cambodia was less consistent among communities with intense commune engagement, higher pressure from tradi-20 tional leaders, high follow-up and high financial support. In Ghana, by contrast, active leaders, high follow-up, high pro-poor support, and continued activities post-implementation promoted latrine coverage and consistent use. The different responses to CLTS programming emphasize that rural communities do not have homogenous reactions to CLTS. Accounting for community perceptions and context when designing interventions can foster long-term sustainability beyond short-term achievement.
Multifunctional GO Hybrid Hydrogel Scaffolds for Wound Healing

Hydrogel dressings have received extensive attention for the skin wound repair, while it is still a challenge to develop a smart hydrogel for adapting the dynamic wound healing process. Herein, we develop a novel graphene oxide (GO) hybrid hydrogel scaffold with adjustable mechanical properties, controllable drug release, and antibacterial behavior for promoting wound healing. The scaffold was prepared by injecting benzaldehyde and cyanoacetate group-functionalized dextran solution containing GO into a collection pool of histidine. As the GO possesses obvious photothermal behavior, the hybrid hydrogel scaffold exhibited an obvious stiffness decrease and effectively promoted cargo release owing to the breaking of the thermosensitive C=C double bond at a high temperature under NIR light. In addition, NIR-assisted photothermal antibacterial performance of the scaffold could be also achieved with the local temperature rising after irradiation. Therefore, it is demonstrated that the GO hybrid hydrogel scaffold with vascular endothelial growth factor (VEGF) encapsulation can achieve the adjustable mechanical properties, photothermal antibacterial, and angiogenesis during the wound healing process. These features indicated that the proposed GO hybrid hydrogel scaffold is potentially valuable for promoting wound healing and other biomedical application.
Model atmospheres and radiation of magnetic neutron stars: Anisotropic thermal emission

We investigate the anisotropy of the thermal radiation emitted by a surface element of a neutron star atmosphere (e.g., by a polar cap of a radio pulsar). Angular dependences of the partial fluxes at various photon energies, and spectra at various angles are obtained for different values of the effective temperature T(sub eff) and magnetic field strength B, and for different directions of the magnetic field. It is shown that the local radiation of the magnetized neutron star atmospheres is highly anisotropic, with the maximum flux emitted in the magnetic field direction. At high B the angular dependences in the soft X-ray range have two maxima, a high narrow peak along B and a lower and broader maximum at intermediate angles. The radiation is strongly polarized, the modulation of the degree of polarization due to the rotation of the neurtron star may be much higher than that for the radiative flux. The results obtained are compared with recent ROSAT observations of the thermal-like radiation from the radio pulsars PSR 1929+10 and PSR J0437-4715.
Determined position dependent Mass of the Rosen-Morse potential and its Bound state

In this paper, Schrodinger equation has been solved analytically with position dependent mass by the Rosen-Morse potential. The position dependent mass defined as the function 1/(1 — tanh(ηx)). Then corresponding position dependent mass substituted into Schrodinger equation. After that the obtained equation compared with associated Jacobi differential equation. Therefore, the eigenvalue and eigenfunctionhave been calculated, and from there the bound state has been found in terms of quantum numbers and the coefficients of potential. Also, the wave function has been obtained in terms of the associated Jacobi equation by μ = —3 and v = 2.