Miracle-dz/UniFAR-FTUs huggingface dataset:
IIsy: Practical In-Network Classification

The rat race between user-generated data and data-processing systems is currently won by data. The increased use of machine learning leads to further increase in processing requirements, while data volume keeps growing. To win the race, machine learning needs to be applied to the data as it goes through the network. In-network classification of data can reduce the load on servers, reduce response time and increase scalability. In this paper, we introduce IIsy, implementing machine learning classification models in a hybrid fashion using off-the-shelf network devices. IIsy targets three main challenges of in-network classification: (i) mapping classification models to network devices (ii) extracting the required features and (iii) addressing resource and functionality constraints. IIsy supports a range of traditional and ensemble machine learning models, scaling independently of the number of stages in a switch pipeline. Moreover, we demonstrate the use of IIsy for hybrid classification, where a small model is implemented on a switch and a large model at the backend, achieving near optimal classification results, while significantly reducing latency and load on the servers.
AvatarCLIP

3D avatar creation plays a crucial role in the digital age. However, the whole production process is prohibitively time-consuming and labor-intensive. To democratize this technology to a larger audience, we propose AvatarCLIP, a zero-shot text-driven framework for 3D avatar generation and animation. Unlike professional software that requires expert knowledge, AvatarCLIP empowers layman users to customize a 3D avatar with the desired shape and texture, and drive the avatar with the described motions using solely natural languages. Our key insight is to take advantage of the powerful vision-language model CLIP for supervising neural human generation, in terms of 3D geometry, texture and animation. Specifically, driven by natural language descriptions, we initialize 3D human geometry generation with a shape VAE network. Based on the generated 3D human shapes, a volume rendering model is utilized to further facilitate geometry sculpting and texture generation. Moreover, by leveraging the priors learned in the motion VAE, a CLIP-guided reference-based motion synthesis method is proposed for the animation of the generated 3D avatar. Extensive qualitative and quantitative experiments validate the effectiveness and generalizability of AvatarCLIP on a wide range of avatars. Remarkably, AvatarCLIP can generate unseen 3D avatars with novel animations, achieving superior zero-shot capability. Codes are available at https://github.com/hongfz16/AvatarCLIP.
Strong Revenue (Non-)Monotonicity of Single-parameter Auctions

Consider Myerson's optimal auction with respect to an inaccurate prior, e.g., estimated from data, which is an underestimation of the true value distribution. Can the auctioneer expect getting at least the optimal revenue w.r. t. the inaccurate prior since the true value distribution is larger? This so-called strong revenue monotonicity is known to be true for single-parameter auctions when the feasible allocations form a matroid. We find that strong revenue monotonicity fails to generalize beyond the matroid setting, and further show that auctions in the matroid setting are the only downward-closed auctions that satisfy strong revenue monotonicity. On the flip side, we recover an approximate version of strong revenue monotonicity that holds for all single-parameter auctions, even without downward-closedness. As applications, we get sample complexity upper bounds for single-parameter auctions under matroid constraints, downward-closed constraints, and general constraints. They improve the state-of-the-art upper bounds and are tight up to logarithmic factors.
Cyber Risk Assessment for Capital Management

Cyber risk is an omnipresent risk in the increasingly digitized world that is known to be difficult to manage. This paper proposes a two-pillar cyber risk management framework to address such difficulty. The first pillar, cyber risk assessment, blends the frequency-severity model in insurance with the cascade model in cybersecurity, to capture the unique feature of cyber risk. The second pillar, cyber capital management, provides informative decision-making on a balanced cyber risk management strategy, which includes cybersecurity investments, insurance coverage, and reserves. This framework is demonstrated by a case study based on a historical cyber incident dataset, which shows that a comprehensive cost-benefit analysis is necessary for a budget-constrained company with competing objectives for cyber risk management. Sensitivity analysis also illustrates that the best strategy depends on various factors, such as the amount of cybersecurity investments and the effectiveness of cybersecurity controls.
On the solidity parameter in canopy flows

Abstract We have performed high-fidelity simulations of turbulent open-channel flows over submerged rigid canopies made of cylindrical filaments of fixed length $l=0.25H$ ($H$ being the domain depth) mounted on the wall with angle of inclination $\theta$. The inclination is the free parameter that sets the density of the canopy by varying its frontal area. The density of the canopy, based on the solidity parameter $\lambda$, is a widely accepted criterion defining the ongoing canopy flow regime, with low values ($\lambda \ll 0.1$) indicating the sparse regime, and higher values ($\lambda > 0.1$) the dense regime. All the numerical predictions have been obtained considering the same nominal bulk Reynolds number (i.e. $Re_b=U_b H/\nu = 6000$). We consider nine configurations of canopies, with $\theta$ varying symmetrically around $0^{\circ }$ in the range $\theta \in [\pm 78.5^{\circ }$], where positive angles define canopies inclined in the flow direction (with the grain) and $\theta =0^{\circ }$ corresponds to the wall-normally mounted canopy. The study compares canopies with identical solidity obtained inclining the filaments in opposite angles, and assesses the efficacy of the solidity as a representative parameter. It is found that when the canopy is inclined, the actual flow regime differs substantially from the one of a straight canopy that shares the same solidity, indicating that criteria based solely on this parameter are not robust. Finally, a new phenomenological model describing the interaction between the coherent structures populating the canopy region and the outer flow is given.
Composing General Audio Representation by Fusing Multilayer Features of a Pre-trained Model

Many application studies rely on audio DNN models pre-trained on a large-scale dataset as essential feature extractors, and they extract features from the last layers. In this study, we focus on our finding that the middle layer features of existing supervised pre-trained models are more effective than the late layer features for some tasks. We propose a simple approach to compose features effective for general-purpose applications, consisting of two steps: (1) calculating feature vectors along the time frame from middle/late layer outputs, and (2) fusing them. This approach improves the utility of frequency and channel information in downstream processes, and combines the effectiveness of middle and late layer features for different tasks. As a result, the feature vectors become effective for general purposes. In the experiments using VGGish, PANNs' CNN14, and AST on nine downstream tasks, we first show that each layer output of these models serves different tasks. Then, we demonstrate that the proposed approach significantly improves their performance and brings it to a level comparable to that of the state-of-the-art. In particular, the performance of the non-semantic speech (NOSS) tasks greatly improves, especially on Speech commands V2 with VGGish of +77.1 (14.3% to 91.4%).
Ultrastrong waveguide QED with giant atoms

Quantum optics with giant emitters has shown a new route for the observation and manipulation of non-Markovian properties in waveguide-QED. In this paper we extend the theory of giant atoms, hitherto restricted to the perturbative light-matter regime, to deal with the ultrastrong coupling regime. Using static and dynamical polaron methods we address the low energy subspace of a giant atom coupled to an Ohmic waveguide beyond the standard rotating wave approximation. We analyze the equilibrium properties of the system by computing the atomic frequency renormalization as a function of the coupling characterizing the localization-delocalization quantum phase transition for a giant atom. We show that virtual photons dressing the ground state are non-exponentially localized around the contact points but decay as a power-law. Dynamics of an initially excited giant atom are studied, pointing out the effects of ultrastrong coupling on the Lamb shift and the spontaneous emission decay rate. Finally we comment on the existence of the so-called oscillating bound states beyond the rotating wave approximation.
Pseudostreamer influence on flux rope evolution

A critical aspect of solar activity is the coupling between eruptions and the surrounding coronal magnetic field, which determines the trajectory and morphology of the eruptive event. Pseudostreamers (PSs) are coronal magnetic structures formed by arcs of twin loops capped by magnetic field lines from coronal holes of the same polarity that meet at a central spine. They contain a single magnetic null point in the spine, potentially influencing the evolution of nearby flux ropes (FRs). To understand the net effect of the PS on FR eruptions is first necessary to study diverse and isolated FR-PS scenarios, which are not influenced by other magnetic structures. We performed numerical simulations in which a FR structure is in the vicinity of a PS magnetic configuration. The combined magnetic field of the PS and the FR results in the formation of two magnetic null points. We evolve this scenario by numerically solving the magnetohydrodynamic equations in 2.5D. The simulations consider a fully ionised compressible ideal plasma in the presence of a gravitational field and a stratified atmosphere. We find that the dynamic behaviour of the FR can be categorised into three different classes based on the FR trajectories and whether it is eruptive or confined. Our analysis indicates that the magnetic null points are decisive in the direction and intensity of the FR deflection and their hierarchy depends on the topological arrangement of the scenario. Moreover, the PS lobe acts as a magnetic cage enclosing the FR. We report that the total unsigned magnetic flux of the cage is a key parameter defining whether the FR is ejected or not.
Btech thesis report on adversarial attack detection and purification of adverserially attacked images

This is Btech thesis report on detection and purification of adverserially attacked images. A deep learning model is trained on certain training examples for various tasks such as classification, regression etc. By training, weights are adjusted such that the model performs the task well not only on training examples judged by a certain metric but has an excellent ability to generalize on other unseen examples as well which are typically called the test data. Despite the huge success of machine learning models on a wide range of tasks, security has received a lot less attention along the years. Robustness along various potential cyber attacks also should be a metric for the accuracy of the machine learning models. These cyber attacks can potentially lead to a variety of negative impacts in the real world sensitive applications for which machine learning is used such as medical and transportation systems. Hence, it is a necessity to secure the system from such attacks. Int this report, I focus on a class of these cyber attacks called the adversarial attacks in which the original input sample is modified by small perturbations such that they still look visually the same to human beings but the machine learning models are fooled by such inputs. In this report I discuss 2 novel ways to counter the adversarial attack using AutoEncoders, 1) by detecting the presence of adversaries and 2) purifying these adversaries to make target classification models robust against such attacks.
Optical characterization of dyed liquid crystal cells

The guest-host liquid crystal display, first proposed in 1968, relies on controlling the orientation of dichroic dyes dissolved in a nematic liquid crystal host. Controlling the orientation of the liquid crystal and of the dissolved dye with an electric field allows control of the transmittance of the cell. Knowing the dielectric properties at optical frequencies of the dye and liquid crystal mixtures is crucial for the optimal design of guest-host liquid crystal devices. In this work, the dielectric functions of various layers in liquid crystal cells are described by models obeying the Kramers-Kronig relations: the Sellmeier equation for transparent layers and causal Gaussian oscillator model for absorbing layers. We propose a systematic way to accurately model the dielectric response of each layer by minimizing the sum of squared differences between the measured transmittance spectrum of a guest-host cell in the near-UV/vis range and the prediction of the transmittance of the modeled multilayer structure. By measuring the transmittance for incident light polarized parallel and perpendicular to the nematic director allows us to separately characterize the two principal dielectric functions of the uniaxial sample. Our results show that the causal Gaussian oscillator model can accurately characterize the dielectric functions of dyes in liquid crystals.
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space

General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments. To address this issue, goal-conditioned reinforcement learning aims to acquire policies that can reach configurable goals for a wide range of tasks on command. However, such goal-conditioned policies are notoriously difficult and time-consuming to train from scratch. In this paper, we propose Planning to Practice (PTP), a method that makes it practical to train goal-conditioned policies for long-horizon tasks that require multiple distinct types of interactions to solve. Our approach is based on two key ideas. First, we decompose the goal-reaching problem hierarchically, with a high-level planner that sets intermediate subgoals using conditional subgoal generators in the latent space for a low-level model-free policy. Second, we propose a hybrid approach which first pre-trains both the conditional subgoal generator and the policy on previously collected data through offline reinforcement learning, and then fine-tunes the policy via online exploration. This fine-tuning process is itself facilitated by the planned subgoals, which breaks down the original target task into short-horizon goal-reaching tasks that are significantly easier to learn. We conduct experiments in both the simulation and real world, in which the policy is pre-trained on demonstrations of short primitive behaviors and fine-tuned for temporally extended tasks that are unseen in the offline data. Our experimental results show that PTP can generate feasible sequences of subgoals that enable the policy to efficiently solve the target tasks. 11Supplementary video: sites.google. com/view/planning-to-practice
Chemical Case Studies in KeYmaera X

Safety-critical chemical processes are well-studied in the formal methods literature, including hybrid systems models which combine discrete and continuous dynamics. This paper is the first to use a theorem-prover to verify hybrid chemical models: the KeYmaera X prover for differential dynamic logic. KeYmaera X provides parametric results that hold for a whole range of parameter values, non-linear physical dynamics, and a small trusted computing base. We tell a general story about KeYmaera X: recent advances in auto-mated reasoning about safety and liveness for differential equations have enabled elegant proofs about reaction dynamics.
The Ages of Galactic Bulge Stars with Realistic Uncertainties

Using modern isochrones with customized physics and carefully considered statistical techniques, we recompute the age distribution for a sample of 91 microlensed dwarfs in the Galactic bulge presented by Bensby et al. and do not produce an age distribution consistent with their results. In particular, our analysis finds that only 15 of 91 stars have ages younger than 7 Gyr, compared to their finding of 42 young stars in the same sample. While we do not find a constituency of very young stars, our results do suggest the presence of an ∼8 Gyr population at the highest metallicities, thus contributing to the long-standing debate about the age–metallicity distribution of the Galactic bulge. We supplement this with attempts at independent age determinations from two sources of photometry, BDBS and Gaia, but find that the imprecision of photometric measurements prevents reliable age and age uncertainty determinations. Lastly, we present age uncertainties derived using a first-order consideration of global modeling uncertainties in addition to standard observational uncertainties. The theoretical uncertainties are based on the known variance of free parameters in the 1D stellar evolution models used to generate isochrones, and when included, result in age uncertainties of 2–5 Gyr for this spectroscopically well-constrained sample. These error bars, which are roughly twice as large as typical literature values, constitute realistic lower limits on the true age uncertainties.
MuIT: An End-to-End Multitask Learning Transformer

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection. Based on the Swin transformer model, our framework encodes the input image into a shared representation and makes predictions for each vision task using task-specific transformer-based decoder heads. At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks. We evaluate our model on several multitask benchmarks, showing that our MulT framework outperforms both the state-of-the art multitask convolutional neural network models and all the respective single task transformer models. Our experiments further highlight the benefits of sharing attention across all the tasks, and demonstrate that our MulT model is robust and generalizes well to new domains. Our project website is at https://ivrl.github. io/MulT/.
On the inﬂuence of diﬀerent partial temperatures on transport properties in polydisperse granular mixtures. A review

It is well-recognized that granular media under rapid ﬂow conditions can be modeled as a gas of hard spheres with inelastic collisions. At moderate densities, a fundamental basis for the determination of the granular hydrodynamics is provided by the Enskog kinetic equation conveniently adapted to account for inelastic collisions. A surprising result (compared to its molecular gas counterpart) for granular mixtures is the failure of the energy equipartition, even in homogeneous states. This means that the partial temperatures T i (measuring the mean kinetic energy of each species) are diﬀerent to the (total) granular temperature T . The goal of this paper is to provide an overview on the eﬀect of diﬀerent partial temperatures on the transport properties of the mixture. Our analysis addresses ﬁrst the impact of energy nonequipartition on transport which is only due to the inelastic character of collisions. This eﬀect (which is absent for elastic collisions) is shown to be signiﬁcant in important problems in granular mixtures such as thermal diﬀusion segregation. Then, an independent source of energy nonequipartition due to the existence of a divergence of the ﬂow velocity is studied. This eﬀect (which was already analyzed in several pioneering works on dense hard-sphere molecular mixtures) aﬀects to the bulk viscosity coeﬃcient. Analytical (approximate) results are compared against Monte Carlo and molecular dynamics simulations, showing the reliability of kinetic theory for describing granular ﬂows.
Enhanced EDMs from small instantons

We show that models in which the strong $CP$ problem is solved by introducing an axion field with a mass enhanced by non-QCD UV dynamics at a scale $\Lambda_{\rm SI}$ exhibit enhanced sensitivity to external sources of $CP$ violation. In the presence of higher-dimensional $CP$-odd sources at a scale $\Lambda_{\rm CP}$, the same mechanisms that enhance the axion mass also modify the axion potential, shifting the potential minimum by a factor $\propto\Lambda^2_{\rm SI}/\Lambda^2_{\rm CP}$. This phenomenon of $CP$-violation enhancement, which puts stringent constraints on the scale of new physics, is explicitly demonstrated within a broad class of"small instanton"models with $CP$-odd sources arising from the dimension-six Weinberg gluonic and four-fermion operators. We find that for heavy axion masses $\gtrsim 100$MeV, arising from new dynamics at $\Lambda_{\rm SI}\lesssim 10^{10}$GeV, $CP$ violation generated up to the Planck scale can be probed by future electric dipole moment experiments.
Unbiased Math Word Problems Benchmark for Mitigating Solving Bias

In this paper, we revisit the solving bias when evaluating models on current Math Word Problem (MWP) benchmarks. However, current solvers exist solving bias which consists of data bias and learning bias due to biased dataset and improper training strategy. Our experiments verify MWP solvers are easy to be biased by the biased training datasets which do not cover diverse questions for each problem narrative of all MWPs, thus a solver can only learn shallow heuristics rather than deep semantics for understanding problems. Besides, an MWP can be naturally solved by multiple equivalent equations while current datasets take only one of the equivalent equations as ground truth, forcing the model to match the labeled ground truth and ignoring other equivalent equations. Here, we ﬁrst introduce a novel MWP dataset named UnbiasedMWP which is constructed by varying the grounded expressions in our collected data and annotating them with corresponding multiple new questions manually. Then, to further mitigate learning bias, we propose a Dynamic Target Selection (DTS) Strategy to dynamically select more suitable target expressions according to the longest preﬁx match between the current model output and candidate equivalent equations which are obtained by applying commutative law during training. The results show that our UnbiasedMWP has signiﬁcantly fewer biases than its original data and other datasets, posing a promising benchmark for fairly evaluating the solvers’ reasoning skills rather than matching nearest neigh-bors. And the solvers trained with our DTS achieve higher accuracies on multiple MWP benchmarks. The source code is available at