Miracle-dz/UniFAR-FTUs huggingface dataset:
BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense

Deep Learning backdoor attacks have a threat model similar to traditional cyber attacks. Attack forensics, a critical counter-measure for traditional cyber attacks, is hence of importance for defending model backdoor attacks. In this paper, we propose a novel model backdoor forensics technique. Given a few attack samples such as inputs with backdoor triggers, which may represent different types of backdoors, our technique automatically decomposes them to clean inputs and the corresponding triggers. It then clusters the triggers based on their properties to allow automatic attack categorization and summarization. Backdoor scanners can then be automatically synthesized to find other instances of the same type of backdoor in other models. Our evaluation on 2,532 pre-trained models, 10 popular attacks, and comparison with 9 baselines show that our technique is highly effective. The decomposed clean inputs and triggers closely resemble the ground truth. The synthesized scanners substantially outperform the vanilla versions of existing scanners that can hardly generalize to different kinds of attacks.
PromptShots at the FinNLP-2022 ERAI Task: Pairwise Comparison and Unsupervised Ranking

This report describes our PromptShots submissions to a shared task on Evaluating the Rationales of Amateur Investors (ERAI). We participated in both pairwise comparison and unsupervised ranking tasks. For pairwise comparison, we employed instruction-based models based on T5-small and OpenAI InstructGPT language models. Surprisingly, we observed OpenAI InstructGPT language model few-shot trained on Chinese data works best in our submissions, ranking 3rd on the maximal loss (ML) pairwise accuracy. This model works better than training on the Google translated English data by a large margin, where the English few-shot trained InstructGPT model even performs worse than an instruction-based T5-small model finetuned on the English data. However, all instruction-based submissions do not perform well on the maximal potential profit (MPP) pairwise accuracy where there are more data and learning signals. The Chinese few-shot trained InstructGPT model still performs best in our setting. For unsupervised ranking, we utilized many language models, including many financial-specific ones, and Bayesian lexicons unsupervised-learned on both Chinese and English words using a method-of-moments estimator. All our submissions rank best in the MPP ranking, from 1st to 3rd. However, they all do not perform well for ML scoring. Therefore, both MPP and ML scores need different treatments since we treated MPP and ML using the same formula. Our only difference is the treatment of market sentiment lexicons.
A Gelfand duality for continuous lattices

We prove that the category of continuous lattices and meet- and directed join-preserving maps is dually equivalent, via the hom functor to $[0,1]$, to the category of complete Archimedean meet-semilattices equipped with a finite meet-preserving action of the monoid of continuous monotone maps of $[0,1]$ fixing $1$. We also prove an analogous duality for completely distributive lattices. Moreover, we prove that these are essentially the only well-behaved"sound classes of joins $\Phi$, dual to a class of meets"for which"$\Phi$-continuous lattice"and"$\Phi$-algebraic lattice"are different notions, thus for which a $2$-valued duality does not suffice.
Non-Hermitian boost deformation

The Hatano-Nelson model is one of the most prototypical non-Hermitian models that exhibit the intrinsic non-Hermitian topological phases and the concomitant skin effect. These phenomena unique to non-Hermitian topological systems originate from the Galilean transformation. Here, we extend such an idea to a broader range of systems based on an imaginary boost deformation and identify the corresponding energy-twisted boundary conditions. This imaginary boost deformation complexifies spectral parameters of integrable models and can be implemented by the coordinate Bethe ansatz. We apply the imaginary boost deformation to several typical integrable models, including free fermions, the Calogero-Sutherland model, and the XXZ model. We find the complex-spectral winding in free fermion models under the periodic boundary conditions and the non-Hermitian skin effect under the open boundary conditions. The interaction effect is also shown in the two-particle spectrum of the XXZ model.
Defect physics of the CuInSe 2 chalcopyrite semiconductor

We studied the defect physics in ${\mathrm{CuInSe}}_{2},$ a prototype chalcopyrite semiconductor. We showed that (i) it takes much less energy to form a Cu vacancy in ${\mathrm{CuInSe}}_{2}$ than to form cation vacancies in II-VI compounds (ii) defect formation energies vary considerably both with the Fermi energy and with the chemical potential of the atomic species, and (iii) the defect pairs such as $({2\mathrm{V}}_{\mathrm{Cu}}^{\mathrm{\ensuremath{-}}}{+\mathrm{I}\mathrm{n}}_{\mathrm{Cu}}^{2+})$ and $({2\mathrm{C}\mathrm{u}}_{\mathrm{In}}^{2\mathrm{\ensuremath{-}}}{+\mathrm{I}\mathrm{n}}_{\mathrm{Cu}}^{2+})$ have particularly low formation energies (under certain conditions, even exothermic). Using (i)--(iii), we (a) explain the existence of unusual ordered compounds ${\mathrm{CuIn}}_{5}{\mathrm{Se}}_{8},$ ${\mathrm{CuIn}}_{3}{\mathrm{Se}}_{5},$ ${\mathrm{Cu}}_{2}{\mathrm{In}}_{4}{\mathrm{Se}}_{7},$ and ${\mathrm{Cu}}_{3}{\mathrm{In}}_{5}{\mathrm{Se}}_{9}$ as a repeat of a single unit of $({2\mathrm{V}}_{\mathrm{Cu}}^{\mathrm{\ensuremath{-}}}{+\mathrm{I}\mathrm{n}}_{\mathrm{Cu}}^{2+})$ pairs for each $n=4,$ 5, 7, and 9 units, respectively, of ${\mathrm{CuInSe}}_{2};$ (b) attribute the very efficient $p$-type self-doping ability of ${\mathrm{CuInSe}}_{2}$ to the exceptionally low formation energy of the shallow defect Cu vacancies; (c) explained in terms of an electronic passivation of the ${\mathrm{In}}_{\mathrm{Cu}}^{2+}$ by ${2\mathrm{V}}_{\mathrm{Cu}}^{\mathrm{\ensuremath{-}}}$ the electrically benign character of the large defect population in ${\mathrm{CuInSe}}_{2}. $ Our calculation leads to a set of new assignment of the observed defect transition energy levels in the band gap. The calculated level positions agree rather well with available experimental data.
Conditional probability and pragmatic conditionals: Dissociating truth and effectiveness

Recent research (e.g., Evans & Over, 2004) has provided support for the hypothesis that people evaluate the probability of conditional statements of the form if p then q as the conditional probability of q given p, P(q/p). The present paper extends this approach to pragmatic conditionals in the form of inducements (i.e., promises and threats) and advice (i.e., tips and warnings). In so doing, we demonstrate a distinction between the truth status of these conditionals and their effectiveness as speech acts. Specifically, while probability judgements of the truth of conditional inducements and advice are highly correlated with estimates of P(q/p), their perceived effectiveness in changing behaviour instead varies as a function of the conditional probability of q given not-p, P(q/∼p). Finally, we show that the conditional probability approach can be extended to predicting inference rates on a conditional reasoning task.
Recent advances in artificial intelligence for retrosynthesis

Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand-new molecules. Conventional rule-based or expert-based computer-aided synthesis has obvious limitations, such as high labor costs and limited search space. In recent years, dramatic breakthroughs driven by artificial intelligence have revolutionized retrosynthesis. Here we aim to present a comprehensive review of recent advances in AI-based retrosynthesis. For single-step and multi-step retrosynthesis both, we first list their goal and provide a thorough taxonomy of existing methods. Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets. In the next part we introduce popular databases and established platforms for retrosynthesis. Finally, this review concludes with a discussion about promising research directions in this field.
Einstein observations of the Hydra A cluster and the efficiency of galaxy formation in groups and clusters

The Einstein imaging proportional counter observations of the poor cluster of galaxies centered on the radio galaxy Hydra A are examined. From the surface brightness profile, it is found that the X-ray-emitting gas in the Hydra A cluster must be condensing out of the intracluster medium at a rate of 600 solar masses/yr. This is one of the largest mass deposition rates observed in a cluster of galaxies. The ratio of gas mass to stellar mass is compared for a variety of systems, showing that this ratio correlates with the gas temperature. 55 refs.
RF Signal Source Search and Localization Using an Autonomous UAV with Predefined Waypoints

Localization of a radio frequency (RF) signal source has various use cases, ranging from search and rescue, identification and deactivation of jammers, and tracking hostile activity near borders or on the battlefield. The use of unmanned aerial vehicles (UAVs) for signal source search and localization (SSSL) can have significant advantages when compared to terrestrial-based approaches, due to the ease of capturing RF signals at higher altitudes and the autonomous 3D navigation capabilities of UAVs. However, the limited flight duration of UAVs due to battery constraints, as well as limited computational resources on board of lightweight UAVs introduce challenges for SSSL. In this paper, we study various SSSL techniques using a UAV with predefined waypoints.A linear least square (LLS) based localization scheme is considered with enhanced reference selection due to its relatively lower computational complexity. Five different LLS localization algorithms are proposed and studied for selecting anchor positions to be used for localization as the UAV navigates through an area. The performance of each algorithm is measured in two ways: 1) real-time positioning accuracy during the ongoing UAV flight, and 2) long-term accuracy measured at the end of the UAV flight. We compare and analyze the performance of the proposed approaches using computer simulations in terms of accuracy, UAV flight distance, and reliability.
Generalised Legendrian racks of Legendrian links

A generalised Legendrian rack is a rack equipped with a Legendrian structure, which is a pair of maps encoding the information of Legendrian Reidemeister moves together with up and down cusps in the front diagram of an oriented Legendrian link. Employing a purely rack theoretic approach, we associate a generalised Legendrian rack (or a GL-rack) to an oriented Legendrian link, and prove that it is an invariant under Legendrian isotopy. As immediate applications, we prove that this invariant distinguishes infinitely many oriented Legendrian unknots and oriented Legendrian trefoils. To comprehend their algebraic structure, we prove that every GL-rack admits a homogeneous representation. Further, using the idea of trunks, we define modules over GL-racks, and prove the equivalence of the category of GL-rack modules and the category of Beck modules over a fixed GL-rack.
Edge Modes in Subwavelength Resonators in One Dimension

We present the mathematical theory of one-dimensional infinitely periodic chains of subwavelength resonators. We analyse both Hermitian and non-Hermitian systems. Subwavelength resonances and associated modes can be accurately predicted by a finite dimensional eigenvalue problem involving a capacitance matrix. We are able to compute the Hermitian and non-Hermitian Zak phases, showing that the former is quantised and the latter is not. Furthermore, we show the existence of localised edge modes arising from defects in the periodicity in both the Hermitian and non-Hermitian cases. In the non-Hermitian case, we provide a complete characterisation of the edge modes.
Phenomenological analysis of simple ion channel block in large populations of uncoupled cardiomyocytes.

Current understanding of arrhythmia mechanisms and design of anti-arrhythmic drug therapies hinges on the assumption that myocytes from the same region of a single heart have similar, if not identical, action potential waveforms and drug responses. On the contrary, recent experiments reveal significant heterogeneity in uncoupled healthy myocytes both from different hearts as well as from identical regions within a single heart. In this work, a methodology is developed for quantifying the individual electrophysiological properties of large numbers of uncoupled cardiomyocytes under ion channel block in terms of the parameters values of a conceptual fast-slow model of electrical excitability. The approach is applied to a population of nearly 500 rabbit ventricular myocytes for which action potential duration (APD) before and after the application of the drug nifedipine was experimentally measured (Lachaud et al., 2022, Cardiovasc. Res.). To this end, drug action is represented by a multiplicative factor to an effective ion conductance, a closed form asymptotic expression for APD is derived and inverted to determine model parameters as functions of APD and ΔAPD (drug-induced change in APD) for each myocyte. Two free protocol-related quantities are calibrated to experiment using an adaptive-domain procedure based on an original assumption of optimal excitability. The explicit APD expression and the resulting set of model parameter values allow (a) direct evaluation of conditions necessary to maintain fixed APD or ΔAPD, (b) predictions of the proportion of cells remaining excitable after drug application, (c) predictions of stimulus period dependency and (d) predictions of dose-response curves, the latter being in agreement with additional experimental data.
Img2Tab: Automatic Class Relevant Concept Discovery from StyleGAN Features for Explainable Image Classification

Traditional tabular classifiers provide explainable decision-making with interpretable features(concepts). However, using their explainability in vision tasks has been limited due to the pixel representation of images. In this paper, we design Img2Tabs that classify images by concepts to harness the explainability of tabular classifiers. Img2Tabs encode image pixels into tabular features by StyleGAN inversion. Since not all of the resulting features are class-relevant or interpretable due to their generative nature, we expect Img2Tab classifiers to discover class-relevant concepts automatically from the StyleGAN features. Thus, we propose a novel method using the Wasserstein-1 metric to quantify class-relevancy and interpretability simultaneously. Using this method, we investigate whether important features extracted by tabular classifiers are class-relevant concepts. Consequently, we determine the most effective classifier for Img2Tabs in terms of discovering class-relevant concepts automatically from StyleGAN features. In evaluations, we demonstrate concept-based explanations through importance and visualization. Img2Tab achieves top-1 accuracy that is on par with CNN classifiers and deep feature learning baselines. Additionally, we show that users can easily debug Img2Tab classifiers at the concept level to ensure unbiased and fair decision-making without sacrificing accuracy.
Transformers as Algorithms: Generalization and Implicit Model Selection in In-context Learning

In-context learning (ICL) is a type of prompting where a transformer model operates on a sequence of (input, output) examples and performs inference on-the-ﬂy. This implicit training is in contrast to explicitly tuning the model weights based on examples. In this work, we formalize in-context learning as an algorithm learning problem, treating the transformer model as a learning algorithm that can be specialized via training to implement—at inference-time—another target algorithm. We ﬁrst explore the statistical aspects of this abstraction through the lens of multitask learning: We obtain generalization bounds for ICL when the input prompt is (1) a sequence of i.i. d. (input, label) pairs or (2) a trajectory arising from a dynamical system. The crux of our analysis is relating the excess risk to the stability of the algorithm implemented by the transformer, which holds under mild assumptions. Secondly, we use our abstraction to show that transformers can act as an adaptive learning algorithm and perform model selection across diﬀerent hypothesis classes. We provide numerical evaluations that (1) demonstrate transformers can indeed implement near-optimal algorithms on classical regression problems with i
First Constraints on Growth Rate from Redshift-space Ellipticity Correlations of SDSS Galaxies at 0.16 < z < 0.70

We report the first constraints on the growth rate of the universe, f(z)σ 8(z), with intrinsic alignments (IAs) of galaxies. We measure the galaxy density-intrinsic ellipticity cross-correlation and intrinsic ellipticity autocorrelation functions over 0.16 < z < 0.7 from luminous red galaxies (LRGs) and LOWZ and CMASS galaxy samples in the Sloan Digital Sky Survey (SDSS) and SDSS-III BOSS survey. We detect clear anisotropic signals of IA due to redshift-space distortions. By combining measured IA statistics with the conventional galaxy clustering statistics, we obtain tighter constraints on the growth rate. The improvement is particularly prominent for the LRG, which is the brightest galaxy sample and known to be strongly aligned with underlying dark matter distribution; using the measurements on scales above 10 h −1 Mpc, we obtain fσ8=0.5196−0.0354+0.0352 (68% confidence level) from the clustering-only analysis and fσ8=0.5322−0.0291+0.0293 with clustering and IA, meaning 19% improvement. The constraint is in good agreement with the prediction of general relativity, f σ 8 = 0.4937 at z = 0.34. For LOWZ and CMASS samples, the improvement of constraints on f σ 8 is found to be 10% and 3.5%, respectively. Our results indicate that the contribution from IA statistics for cosmological constraints can be further enhanced by carefully selecting galaxies for a shape sample.
On the Development of Turbomachine Blade Aerodynamic Design System

A turbomachine blade aerodynamic design process is proposed to design turbomachine blades. The design system, including a global optimization of through flow for whole machine and a local optimization of the airfoil design and airfoil section, stacks up. The airfoil generator code employs Bezier polynomial curves to produce smooth airfoil shapes. A meanline program combined with an optimizer was used to perform the global optimization. In the airfoil section design, for fast calculations of the airfoil pressure distributions a discrete vortex method is developed. A novel Navier-Stokes (N-S) solver is developed for further examination of the airfoil performance for final section design. The N-S code is used to obtain the blade-to-blade quasi-three-dimensional, turbulent, and viscous flow characteristics. The time-dependent N-S equations are discretized and integrated in a coupled manner based on a finite-volume formulation, as well as a flux-difference splitting. The flux-difference splitting method enables us to compute with a rapid convergence. The present N-S code can handle computations of both subsonic and supersonic flows and can be connected to an external optimizer code with the airfoil generator. After optimized airfoil was designed, a three-dimensional code was used for final tuning of the design.
Significant influence of fluid viscoelasticity on flow dynamics past an oscillating cylinder

Abstract This study focuses on numerically investigating the impact of fluid viscoelasticity on the flow dynamics around a transversely forced oscillating cylinder operating in the laminar vortex shedding regime at a fixed Reynolds number of $Re = 100$. Specifically, we explore how fluid viscoelasticity affects the boundary between the lock-in and no lock-in regions and the corresponding wake characteristics compared with a simple Newtonian fluid. Our findings reveal that fluid viscoelasticity enables the synchronization of the vortex street with the cylinder motion at lower oscillation frequencies than those required for a Newtonian fluid. Consequently, the lock-in region boundary for a viscoelastic fluid differs from that of a Newtonian fluid and expands in the non-dimensional cylinder oscillation amplitude and frequency parameter space. In the primary synchronization region, the wake of a Newtonian fluid exhibits ‘2S’ (two single vortices) and ‘P+S’ (a pair of vortices and a single vortex) shedding modes. In contrast, a ‘2P’ (two pairs of vortices) vortex mode is observed for a viscoelastic fluid within the same region. To gain a deeper understanding of the differences in the coherent flow structures and their associated frequencies between the two fluids, we employ the data-driven reduced-order modelling technique, known as the dynamic mode decomposition (DMD) technique. Utilizing this technique, we successfully extract and visualize the two competing fundamental frequencies (cylinder oscillation and natural vortex shedding frequencies) and their associated flow structures in the case of the no lock-in state, whereas only the dominant cylinder oscillation frequency and associated flow structure in the case of the lock-in state. Furthermore, we propose that the presence of excess strain resulting from the stretching of polymer molecules in viscoelastic fluids leads to a distinct difference in the wake structure compared with Newtonian fluids. This observation aligns with the findings obtained from the $Q$-criterion and vorticity transport analysis of the wake.
Modeling Uncertain Feature Representation for Domain Generalization

Though deep neural networks have achieved impressive success on various vision tasks, obvious performance degradation still exists when models are tested in out-of-distribution scenarios. In addressing this limitation, we ponder that the feature statistics (mean and standard deviation), which carry the domain characteristics of the training data, can be properly manipulated to improve the generalization ability of deep learning models. Existing methods commonly consider feature statistics as deterministic values measured from the learned features and do not explicitly model the uncertain statistics discrepancy caused by potential domain shifts during testing. In this paper, we improve the network generalization ability by modeling domain shifts with uncertainty (DSU), i.e., characterizing the feature statistics as uncertain distributions during training. Specifically, we hypothesize that the feature statistic, after considering the potential uncertainties, follows a multivariate Gaussian distribution. During inference, we propose an instance-wise adaptation strategy that can adaptively deal with the unforeseeable shift and further enhance the generalization ability of the trained model with negligible additional cost. We also conduct theoretical analysis on the aspects of generalization error bound and the implicit regularization effect, showing the efficacy of our method. Extensive experiments demonstrate that our method consistently improves the network generalization ability on multiple vision tasks, including image classification, semantic segmentation, instance retrieval, and pose estimation. Our methods are simple yet effective and can be readily integrated into networks without additional trainable parameters or loss constraints. Code will be released in https://github.com/lixiaotong97/DSU.