Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Scalable and Robust Model Versioning

As the deployment of deep learning models continues to expand across industries, the threat of malicious incursions aimed at gaining access to these deployed models is on the rise. Should an attacker gain access to a deployed model, whether through server breaches, insider attacks, or model inversion techniques, they can then construct white-box adversarial attacks to manipulate the model's classification outcomes, thereby posing significant risks to organizations that rely on these models for critical tasks. Model owners need mechanisms to protect themselves against such losses without the necessity of acquiring fresh training data - a process that typically demands substantial investments in time and capital. In this paper, we explore the feasibility of generating multiple versions of a model that possess different attack properties, without acquiring new training data or changing model architecture. The model owner can deploy one version at a time and replace a leaked version immediately with a new version. The newly deployed model version can resist adversarial attacks generated leveraging white-box access to one or all previously leaked versions. We show theoretically that this can be accomplished by incorporating parameterized hidden distributions into the model training data, forcing the model to learn task-irrelevant features uniquely defined by the chosen data. Additionally, optimal choices of hidden distributions can produce a sequence of model versions capable of resisting compound transferability attacks over time. Leveraging our analytical insights, we design and implement a practical model versioning method for DNN classifiers, which leads to significant robustness improvements over existing methods. We believe our work presents a promising direction for safeguarding DNN services beyond their initial deployment.
From inflation to dark matter halo profiles: the impact of primordial non-Gaussianities on the central density cusp

It has recently been shown that local primordial non-Gaussianities (PNG) with significant amplitude ($|f_{\rm NL}| \sim 1000$), at small (Mpc) scales, can help in forming simulated galaxies with more disky baryonic kinematics than in the Gaussian case, while generating matter power spectra that can differ by up to 20% from the Gaussian case at non-linear scales. Here, we explore in detail the consequences of such small-scale PNG on the dark matter halo profiles. We show in particular that, for negative $f_{\rm NL}$, dark matter halos formed in collisionless simulations are not always well described by the traditional Navarro-Frenk-White (NFW) profiles, as supported by their sparsity distribution. We conclude that NFW profiles are not as clear attractors for the density profiles of dark matter halos in the presence of PNG than in the case of a Gaussian contrast density field. We show how a minimal extension of the NFW profile can describe halos both in the Gaussian and non-Gaussian cases. From the combination of our sparsity analysis and the quality of the adjustments of the density profiles with a minimal extension to NFW, we conclude that $z=1$ halos carry the most interesting information about PNG.
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization

Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining models to a fixed structure or uniform parameterization to encapsulate invariant features can inadvertently blend specific aspects. Such an approach struggles with nuanced differentiation of inter-domain variations and may exhibit bias towards certain domains, hindering the precise learning of domain-invariant features. Recognizing this, we introduce a novel method designed to supplement the model with domain-level and task-specific characteristics. This approach aims to guide the model in more effectively separating invariant features from specific characteristics, thereby boosting the generalization. Building on the emerging trend of visual prompts in the DG paradigm, our work introduces the novel \textbf{H}ierarchical \textbf{C}ontrastive \textbf{V}isual \textbf{P}rompt (HCVP) methodology. This represents a significant advancement in the field, setting itself apart with a unique generative approach to prompts, alongside an explicit model structure and specialized loss functions. Differing from traditional visual prompts that are often shared across entire datasets, HCVP utilizes a hierarchical prompt generation network enhanced by prompt contrastive learning. These generative prompts are instance-dependent, catering to the unique characteristics inherent to different domains and tasks. Additionally, we devise a prompt modulation network that serves as a bridge, effectively incorporating the generated visual prompts into the vision transformer backbone. Experiments conducted on five DG datasets demonstrate the effectiveness of HCVP, outperforming both established DG algorithms and adaptation protocols.
Efficient generative adversarial networks using linear additive-attention Transformers

Although the capacity of deep generative models for image generation, such as Diffusion Models (DMs) and Generative Adversarial Networks (GANs), has dramatically improved in recent years, much of their success can be attributed to computationally expensive architectures. This has limited their adoption and use to research laboratories and companies with large resources, while significantly raising the carbon footprint for training, fine-tuning, and inference. In this work, we present LadaGAN, an efficient generative adversarial network that is built upon a novel Transformer block named Ladaformer. The main component of this block is a linear additive-attention mechanism that computes a single attention vector per head instead of the quadratic dot-product attention. We employ Ladaformer in both the generator and discriminator, which reduces the computational complexity and overcomes the training instabilities often associated with Transformer GANs. LadaGAN consistently outperforms existing convolutional and Transformer GANs on benchmark datasets at different resolutions while being significantly more efficient. Moreover, LadaGAN shows competitive performance compared to state-of-the-art multi-step generative models (e.g. DMs) using orders of magnitude less computational resources.
LoMA: Lossless Compressed Memory Attention

Large Language Models (LLMs) face limitations due to the high demand on GPU memory and computational resources when handling long contexts. While sparsify the Key-Value (KV) cache of transformer model is a typical strategy to alleviate resource usage, it unavoidably results in the loss of information. We introduce Lossless Compressed Memory Attention (LoMA), a novel approach that enables lossless compression of the KV cache, thereby reducing the memory and computational demands during autoregressive generation. LoMA incorporates a specialized training or fine-tuning precedure alongside an autoregressive generation algorithm optimized for the compressed context. Our method compresses the KV cache after every $tc$ generated tokens with a compression ratio of $c$ and a target compressed length $t$, and this process occurs within a single inference pass without dependency on auxiliary models. We engineered an efficient training scheme involving specific inputs, attention masks, and position identifiers to instill this compression capability. Experimental validation has demonstrated that LoMA significantly reducing computational consumption and memory usage through achieving lossless KV cache compression.
Strong decay of correlations for Gibbs states in any dimension

Quantum systems in thermal equilibrium are described using Gibbs states. The correlations in such states determine how difficult it is to describe or simulate them. In this article, we show that systems with short-range interactions that are above a critical temperature satisfy a mixing condition, that is that for any regions $A$, $C$ the distance of the reduced state $\rho_{AC}$ on these regions to the product of its marginals, $$\| \rho_{AC} \rho_A^{-1} \otimes \rho_C^{-1} - \mathbf{1}_{AC}\| \, ,$$ decays exponentially with the distance between regions $A$ and $C$. This mixing condition is stronger than other commonly studied measures of correlation. In particular, it implies the exponential decay of the mutual information between distant regions. The mixing condition has been used, for example, to prove positive log-Sobolev constants. On the way, we investigate the relations to other notions of decay of correlations in quantum many-body systems and show that many of them are equivalent under the assumption that there exists a local effective Hamiltonian. The proof employs a variety of tools such as Araki's expansionals and quantum belief propagation.
Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks

Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner. In this paper, we consider HFL with highly mobile devices, mainly targeting at vehicular networks. Through convergence analysis, we show that mobility influences the convergence speed by both fusing the edge data and shuffling the edge models. While mobility is usually considered as a challenge from the perspective of communication, we prove that it increases the convergence speed of HFL with edge-level heterogeneous data, since more diverse data can be incorporated. Furthermore, we demonstrate that a higher speed leads to faster convergence, since it accelerates the fusion of data. Simulation results show that mobility increases the model accuracy of HFL by up to 15.1% when training a convolutional neural network on the CIFAR-10 dataset.
Learning Hybrid Policies for MPC with Application to Drone Flight in Unknown Dynamic Environments

In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC's sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100\% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics.
VMamba: Visual State Space Model

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) stand as the two most popular foundation models for visual representation learning. While CNNs exhibit remarkable scalability with linear complexity w.r. t. image resolution, ViTs surpass them in fitting capabilities despite contending with quadratic complexity. A closer inspection reveals that ViTs achieve superior visual modeling performance through the incorporation of global receptive fields and dynamic weights. This observation motivates us to propose a novel architecture that inherits these components while enhancing computational efficiency. To this end, we draw inspiration from the recently introduced state space model and propose the Visual State Space Model (VMamba), which achieves linear complexity without sacrificing global receptive fields. To address the encountered direction-sensitive issue, we introduce the Cross-Scan Module (CSM) to traverse the spatial domain and convert any non-causal visual image into order patch sequences. Extensive experimental results substantiate that VMamba not only demonstrates promising capabilities across various visual perception tasks, but also exhibits more pronounced advantages over established benchmarks as the image resolution increases. Source code has been available at https://github.com/MzeroMiko/VMamba.
Enabling On-device Continual Learning with Binary Neural Networks

On-device learning remains a formidable challenge, especially when dealing with resource-constrained devices that have limited computational capabilities. This challenge is primarily rooted in two key issues: first, the memory available on embedded devices is typically insufficient to accommodate the memory-intensive back-propagation algorithm, which often relies on floating-point precision. Second, the development of learning algorithms on models with extreme quantization levels, such as Binary Neural Networks (BNNs), is critical due to the drastic reduction in bit representation. In this study, we propose a solution that combines recent advancements in the field of Continual Learning (CL) and Binary Neural Networks to enable on-device training while maintaining competitive performance. Specifically, our approach leverages binary latent replay (LR) activations and a novel quantization scheme that significantly reduces the number of bits required for gradient computation. The experimental validation demonstrates a significant accuracy improvement in combination with a noticeable reduction in memory requirement, confirming the suitability of our approach in expanding the practical applications of deep learning in real-world scenarios.
SHINOBI: Shape and Illumination using Neural Object Decomposition via BRDF Optimization In-the-wild

We present SHINOBI, an end-to-end framework for the reconstruction of shape, material, and illumination from object images captured with varying lighting, pose, and background. Inverse rendering of an object based on unconstrained image collections is a long-standing challenge in computer vision and graphics and requires a joint optimization over shape, radiance, and pose. We show that an implicit shape representation based on a multi-resolution hash encoding enables faster and robust shape reconstruction with joint camera alignment optimization that outperforms prior work. Further, to enable the editing of illumination and object reflectance (i.e. material) we jointly optimize BRDF and illumination together with the object's shape. Our method is class-agnostic and works on in-the-wild image collections of objects to produce relightable 3D assets for several use cases such as AR/VR, movies, games, etc. Project page: https://shinobi.aengelhardt. com Video: https://www.youtube. com/watch? v=iFENQ6AcYd8&feature=youtu.be
LDPC-cat codes for low-overhead quantum computing in 2D

Quantum low-density parity-check (qLDPC) codes are a promising construction for drastically reducing the overhead of fault-tolerant quantum computing (FTQC) architectures. However, all of the known hardware implementations of these codes require advanced technologies, such as long-range qubit connectivity, high-weight stabilizers, or multi-layered chip layouts. An alternative approach to reduce the hardware overhead of fault-tolerance is to use bosonic cat qubits where bit-flip errors are exponentially suppressed by design. In this work, we combine both approaches and propose an architecture based on cat qubits concatenated in classical LDPC codes correcting for phase-flips. We find that employing such phase-flip LDPC codes provides two major advantages. First, the hardware implementation of the code can be realised using short-range qubit interactions in 2D and low-weight stabilizers, which makes it readily compatible with current superconducting circuit technologies. Second, we demonstrate how to implement a fault-tolerant universal set of logical gates with a second layer of cat qubits while maintaining the local connectivity. We conduct a numerical brute force optimisation of these classical codes to find the ones with the best encoding rate for algorithmically relevant code distances. We discover that some of the best codes benefit from a cellular automaton structure. This allows us to define families of codes with high encoding rates and distances. Finally, we numerically assess the performance of our codes under circuit-level noise. Assuming a physical phase-flip error probability $\epsilon \approx 0.1\%$, our $[165+8\ell, 34+2\ell, 22]$ code family allows to encode $100$ logical qubits with a total logical error probability (including both logical phase-flip and bit-flip) per cycle and per logical qubit $\epsilon_L \leq 10^{-8}$ on a $758$ cat qubit chip.
20 Minuten: A Multi-task News Summarisation Dataset for German

We present 20 Minuten , a dataset for abstractive text summarisation of German news articles. This dataset fills a gap in summarisation resources for the German language and includes multiple professionally-written and stylistically distinct summaries, along with captioned images and document-level reading times. In this paper, we conduct baseline experiments with mT5 and compare the performance of fine-tuning in both single-and multi-task settings on six different downstream tasks supported by our dataset. Our results reveal that dedicated models are preferable, especially for the more distinct tasks. We make our dataset available for research purposes and provide code to facilitate its handling at https://github.com/ ZurichNLP/20Minuten .
Are Polish firms risk-averting or risk-loving?

This paper investigates the effect of demand uncertainty on the capital-labour ratio of non-financial firms in Poland in order to infer the firms’ risk behaviour. A generic model is used to characterise a utility maximising firm in a transition economy with demand uncertainty and imperfect competition. It is assumed that labour is completely variable and capital is quasi-fixed. The demand for capital, and hence the capital-labour ratio, derives from the optimisation of expected costs and the firm’s pricing and output decisions, and crucially depends on the sign of the covariance term i.e. the firm’s risk behaviour. The main proposition of the model is that if firms are risk-loving, an increase in demand uncertainty increases the capital-labour ratio, whereas the capital-labour ratio would decrease when a firm is risk-averting. The model is estimated using data from a cross-section of 148 non-financial firms in Poland. The results unambiguously show that there exists a significant positive relationship between demand uncertainty and the capital-labour ratio. This finding suggests that Polish firms are risk-lovers. The evidence may have important implications for the needed set of regulations and corporate governance in Poland as part of the necessary economic reform.
Enhanced legumain activity links progranulin deficiency to TDP-43 pathology in frontotemporal lobar degeneration

Loss-of-function mutations in GRN are a major cause of frontotemporal lobar degeneration (FTLD) with TDP-43-positive inclusions. Progranulin (PGRN) loss leads to lysosomal dysfunction, microglial hyperactivation, and TDP-43 deposition, yet the underlying pathomechanism remains unknown. We demonstrate that PGRN slows the maturation and limits the proteolytic activity of the lysosomal protease legumain (LGMN). Accordingly, LGMN activity is strongly elevated in Grn knockout (ko) mice, in human induced pluripotent stem cell-derived GRN ko microglia, and in FTLD-GRN patients’ brain. Secreted microglial LGMN is internalized by neurons, where it mediates pathological processing of TDP-43, which is prevented by selective LGMN inhibition. In contrast, AAV-mediated overexpression of LGMN in mouse brains promotes TDP-43 processing, the aggregation of phosphorylated TDP-43 and increases plasma neurofilament light chain (NfL), a marker for neuronal loss. Our findings identify LGMN as a link between PGRN haploinsufficiency and TDP-43 pathology in FTLD-GRN and suggest LGMN as a therapeutic target.
Domain-general cognitive control processes in bilingual switching: evidence from midfrontal theta oscillations

Language control in bilingual speakers is thought to be implicated in effectively switching between languages, inhibiting the non-intended language, and continuously monitoring what to say and what has been said. It has been a matter of controversy concerning whether language control operates in a comparable manner to cognitive control processes in non-linguistic domains (domain-general) or if it is exclusive to language processing (domain-specific). As midfrontal theta oscillations have been considered as an index of cognitive control, examining whether a midfrontal theta effect is evident in tasks requiring bilingual control could bring new insights to the ongoing debate. To this end, we reanalysed the EEG data from two previous bilingual production studies where Dutch-English bilinguals named pictures based on colour cues. Specifically, we focused on three fundamental control processes in bilingual production: switching between languages, inhibition of the nontarget language, and monitoring of speech errors. Theta power increase was observed in switch trials compared to repeat trials, with a midfrontal scalp distribution. However, this midfrontal theta effect was absent in switch trials following a short sequence of same-language trials compared to a long sequence, suggesting a missing modulation of inhibitory control. Similarly, increased midfrontal theta power was observed when participants failed to switch to the intended language compared to correct responses. Altogether, these findings tentatively support the involvement of domain-general cognitive control mechanisms in bilingual switching.
Synaptic communication within the microcircuits of pyramidal neurons and basket cells in the mouse prefrontal cortex

Basket cells are inhibitory interneurons in cortical structures with the potential to efficiently control the activity of their postsynaptic partners. Although their contribution to higher order cognitive functions associated with the medial prefrontal cortex (mPFC) relies on the characteristics of their synaptic connections, the way they are embedded into local circuits is still not fully uncovered. Here, we determined the synaptic properties of excitatory and inhibitory connections between pyramidal neurons (PNs), cholecystokinin-containing basket cells (CCKBCs) and parvalbumin-containing basket cells (PVBCs) in the mouse mPFC. By performing paired recordings, we revealed that PVBCs receive larger unitary excitatory postsynaptic currents from PNs with shorter latency and faster kinetic properties compared to events evoked in CCKBCs. Also, unitary inhibitory postsynaptic currents in PNs were more reliably evoked by PVBCs than by CCKBCs yet the former connections showed profound short-term depression. Moreover, we demonstrated that CCKBCs and PVBCs in the mPFC are mutually interconnected with each other. As alterations in PVBC function have been linked to neurological and psychiatric conditions like Alzheimer’s disease and schizophrenia and CCKBC vulnerability might play a role in mood disorders, a deeper understanding of the general features of basket cell synapses could serve as a reference point for future investigations with therapeutic objectives.