Miracle-dz/UniFAR-FTUs huggingface dataset:
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation

The goal of sequential recommendation (SR) is to predict a user's potential interested items based on her/his historical interaction sequences. Most existing sequential recommenders are developed based on ID features, which, despite their widespread use, often underperform with sparse IDs and struggle with the cold-start problem. Besides, inconsistent ID mappings hinder the model's transferability, isolating similar recommendation domains that could have been co-optimized. This paper aims to address these issues by exploring the potential of multi-modal information in learning robust and generalizable sequence representations. We propose MISSRec, a multi-modal pre-training and transfer learning framework for SR. On the user side, we design a Transformer-based encoder-decoder model, where the contextual encoder learns to capture the sequence-level multi-modal synergy while a novel interest-aware decoder is developed to grasp item-modality-interest relations for better sequence representation. On the candidate item side, we adopt a dynamic fusion module to produce user-adaptive item representation, providing more precise matching between users and items. We pre-train the model with contrastive learning objectives and fine-tune it in an efficient manner. Extensive experiments demonstrate the effectiveness and flexibility of MISSRec, promising an practical solution for real-world recommendation scenarios.
Opening the Vocabulary of Egocentric Actions

Human actions in egocentric videos are often hand-object interactions composed from a verb (performed by the hand) applied to an object. Despite their extensive scaling up, egocentric datasets still face two limitations - sparsity of action compositions and a closed set of interacting objects. This paper proposes a novel open vocabulary action recognition task. Given a set of verbs and objects observed during training, the goal is to generalize the verbs to an open vocabulary of actions with seen and novel objects. To this end, we decouple the verb and object predictions via an object-agnostic verb encoder and a prompt-based object encoder. The prompting leverages CLIP representations to predict an open vocabulary of interacting objects. We create open vocabulary benchmarks on the EPIC-KITCHENS-100 and Assembly101 datasets; whereas closed-action methods fail to generalize, our proposed method is effective. In addition, our object encoder significantly outperforms existing open-vocabulary visual recognition methods in recognizing novel interacting objects.
SPANet: Frequency-balancing Token Mixer using Spectral Pooling Aggregation Modulation

Recent studies show that self-attentions behave like low-pass filters (as opposed to convolutions) and enhancing their high-pass filtering capability improves model performance. Contrary to this idea, we investigate existing convolution-based models with spectral analysis and observe that improving the low-pass filtering in convolution operations also leads to performance improvement. To account for this observation, we hypothesize that utilizing optimal token mixers that capture balanced representations of both high- and low-frequency components can enhance the performance of models. We verify this by decomposing visual features into the frequency domain and combining them in a balanced manner. To handle this, we replace the balancing problem with a mask filtering problem in the frequency domain. Then, we introduce a novel token-mixer named SPAM and leverage it to derive a MetaFormer model termed as SPANet. Experimental results show that the proposed method provides a way to achieve this balance, and the balanced representations of both high- and low-frequency components can improve the performance of models on multiple computer vision tasks. Our code is available at https://doranlyong.github. io/projects/spanet/.
A note on some polynomial-factorial diophantine equations

In 1876 Brocard, and independently in 1913 Ramanujan, asked to find all integer solutions for the equation $n! =x^2-1$. It is conjectured that this equation has only three solutions, but up to now this is an open problem. Overholt observed that a weak form of Szpiro's-conjecture implies that Brocard's equation has finitely many integer solutions. More generally, assuming the ABC-conjecture, Luca showed that equations of the form $n! =P(x)$ where $P(x)\in\mathbb{Z}[x]$ of degree $d\geq 2$ have only finitely many integer solutions with $n>0$. And if $P(x)$ is irreducible, Berend and Harmse proved unconditionally that $P(x)=n! $ has only finitely many integer solutions. In this note we study diophantine equations of the form $g(x_1,.,x_r)=P(x)$ where $P(x)\in\mathbb{Z}[x]$ of degree $d\geq 2$ and $g(x_1,.,x_r)\in \mathbb{Z}[x_1,. ,x_r]$ where for $x_i$ one may also plug in $A^{n}$ or the Bhargava factorial $n! _S$. We want to understand when there are finitely many or infinitely many integer solutions. Moreover, we study diophantine equations of the form $g(x_1,.,x_r)=f(x,y)$ where $f(x,y)\in\mathbb{Z}[x,y]$ is a homogeneous polynomial of degree $\geq2$.
Anonymity at Risk? Assessing Re-Identification Capabilities of Large Language Models

Anonymity of both natural and legal persons in court rulings is a critical aspect of privacy protection in the European Union and Switzerland. With the advent of LLMs, concerns about large-scale re-identification of anonymized persons are growing. In accordance with the Federal Supreme Court of Switzerland, we explore the potential of LLMs to re-identify individuals in court rulings by constructing a proof-of-concept using actual legal data from the Swiss federal supreme court. Following the initial experiment, we constructed an anonymized Wikipedia dataset as a more rigorous testing ground to further investigate the findings. With the introduction and application of the new task of re-identifying people in texts, we also introduce new metrics to measure performance. We systematically analyze the factors that influence successful re-identifications, identifying model size, input length, and instruction tuning among the most critical determinants. Despite high re-identification rates on Wikipedia, even the best LLMs struggled with court decisions. The complexity is attributed to the lack of test datasets, the necessity for substantial training resources, and data sparsity in the information used for re-identification. In conclusion, this study demonstrates that re-identification using LLMs may not be feasible for now, but as the proof-of-concept on Wikipedia showed, it might become possible in the future. We hope that our system can help enhance the confidence in the security of anonymized decisions, thus leading to the courts being more confident to publish decisions.
Poincaré-sphere symmetries in four-wave mixing with orbital angular momentum

We explore a degenerate four-wave mixing process induced by transversely structured light beams in a rubidium vapor cell. In particular, we consider the nonlinear interaction driven by optical modes contained in the orbital angular momentum Poincar\'e sphere, which can be parametrized in terms of a polar and an azimuthal angle. In this context we investigate the transfer of spatial structure to two distinct four-wave mixing signals, possessing different propagation directions in space. We show that under usual assumptions, the output fields can also be described by modes belonging to Poincar\'e spheres, and that the angles describing the input and output modes are related according to well-defined rules. Our experimental results show good agreement with the calculations, which predict intricate field structures and a transition of the FWM transverse profile between the near- and far-field regions.
MatFuse: Controllable Material Generation with Diffusion Models

Creating high-quality materials in computer graphics is a challenging and time-consuming task, which requires great expertise. To simplify this process, we introduce MatFuse, a unified approach that harnesses the generative power of diffusion models for creation and editing of 3D materials. Our method integrates multiple sources of conditioning, including color palettes, sketches, text, and pictures, enhancing creative possibilities and granting fine-grained control over material synthesis. Additionally, MatFuse enables map-level material editing capabilities through latent manipulation by means of a multi-encoder compression model which learns a disentangled latent representation for each map. We demonstrate the effectiveness of MatFuse under multiple conditioning settings and explore the potential of material editing. Finally, we assess the quality of the generated materials both quantitatively in terms of CLIP-IQA and FID scores and qualitatively by conducting a user study. Source code for training MatFuse and supplemental materials are publicly available at https://gvecchio.com/matfuse.
Graph Neural Network-Enhanced Expectation Propagation Algorithm for MIMO Turbo Receivers

Deep neural networks (NNs) are considered a powerful tool for balancing the performance and complexity of multiple-input multiple-output (MIMO) receivers due to their accurate feature extraction, high parallelism, and excellent inference ability. Graph NNs (GNNs) have recently demonstrated outstanding capability in learning enhanced message passing rules and have shown success in overcoming the drawback of inaccurate Gaussian approximation of expectation propagation (EP)-based MIMO detectors. However, the application of the GNN-enhanced EP detector to MIMO turbo receivers is underexplored and non-trivial due to the requirement of extrinsic information for iterative processing. This paper proposes a GNN-enhanced EP algorithm for MIMO turbo receivers, which realizes the turbo principle of generating extrinsic information from the MIMO detector through a specially designed training procedure. Additionally, an edge pruning strategy is designed to eliminate redundant connections in the original fully connected model of the GNN utilizing the correlation information inherently from the EP algorithm. Edge pruning reduces the computational cost dramatically and enables the network to focus more attention on the weights that are vital for performance. Simulation results and complexity analysis indicate that the proposed MIMO turbo receiver outperforms the EP turbo approaches by over 1 dB at the bit error rate of 10${}^{\boldsymbol{-}\mathbf{5}}$, exhibits performance equivalent to state-of-the-art receivers with 2.5 times shorter running time, and adapts to various scenarios.
GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised Learning

Large-scale foundation models, such as CLIP, have demonstrated remarkable success in visual recognition tasks by embedding images in a semantically rich space. Self-supervised learning (SSL) has also shown promise in improving visual recognition by learning invariant features. However, the combination of CLIP with SSL is found to face challenges due to the multi-task framework that blends CLIP's contrastive loss and SSL's loss, including difficulties with loss weighting and inconsistency among different views of images in CLIP's output space. To overcome these challenges, we propose a prompt learning-based model called GOPro, which is a unified framework that ensures similarity between various augmented views of input images in a shared image-text embedding space, using a pair of learnable image and text projectors atop CLIP, to promote invariance and generalizability. To automatically learn such prompts, we leverage the visual content and style primitives extracted from pre-trained CLIP and adapt them to the target task. In addition to CLIP's cross-domain contrastive loss, we introduce a visual contrastive loss and a novel prompt consistency loss, considering the different views of the images. GOPro is trained end-to-end on all three loss objectives, combining the strengths of CLIP and SSL in a principled manner. Empirical evaluations demonstrate that GOPro outperforms the state-of-the-art prompting techniques on three challenging domain generalization tasks across multiple benchmarks by a significant margin. Our code is available at https://github.com/mainaksingha01/GOPro.
Simple and Faster Algorithms for Knapsack

In this paper, we obtain a number of new simple pseudo-polynomial time algorithms on the well-known knapsack problem, focusing on the running time dependency on the number of items $n$, the maximum item weight $w_\mathrm{max}$, and the maximum item profit $p_\mathrm{max}$. Our results include: - An $\widetilde{O}(n^{3/2}\cdot \min\{w_\mathrm{max},p_\mathrm{max}\})$-time randomized algorithm for 0-1 knapsack, improving the previous $\widetilde{O}(\min\{n w_\mathrm{max} p_\mathrm{max}^{2/3},n p_\mathrm{max} w_\mathrm{max}^{2/3}\})$ [Bringmann and Cassis, ESA'23] for the small $n$ case. - An $\widetilde{O}(n+\min\{w_\mathrm{max},p_\mathrm{max}\}^{5/2})$-time randomized algorithm for bounded knapsack, improving the previous $O(n+\min\{w_\mathrm{max}^3,p_\mathrm{max}^3\})$ [Polak, Rohwedder and Wegrzyck, ICALP'21].
Adversarial Attacks on Code Models with Discriminative Graph Patterns

Pre-trained language models of code are now widely used in various software engineering tasks such as code generation, code completion, vulnerability detection, etc. This, in turn, poses security and reliability risks to these models. One of the important threats is \textit{adversarial attacks}, which can lead to erroneous predictions and largely affect model performance on downstream tasks. Current adversarial attacks on code models usually adopt fixed sets of program transformations, such as variable renaming and dead code insertion, leading to limited attack effectiveness. To address the aforementioned challenges, we propose a novel adversarial attack framework, GraphCodeAttack, to better evaluate the robustness of code models. Given a target code model, GraphCodeAttack automatically mines important code patterns, which can influence the model's decisions, to perturb the structure of input code to the model. To do so, GraphCodeAttack uses a set of input source codes to probe the model's outputs and identifies the \textit{discriminative} ASTs patterns that can influence the model decisions. GraphCodeAttack then selects appropriate AST patterns, concretizes the selected patterns as attacks, and inserts them as dead code into the model's input program. To effectively synthesize attacks from AST patterns, GraphCodeAttack uses a separate pre-trained code model to fill in the ASTs with concrete code snippets. We evaluate the robustness of two popular code models (e.g., CodeBERT and GraphCodeBERT) against our proposed approach on three tasks: Authorship Attribution, Vulnerability Prediction, and Clone Detection. The experimental results suggest that our proposed approach significantly outperforms state-of-the-art approaches in attacking code models such as CARROT and ALERT.
ReFit: Recurrent Fitting Network for 3D Human Recovery

We present Recurrent Fitting (ReFit), a neural network architecture for single-image, parametric 3D human reconstruction. ReFit learns a feedback-update loop that mirrors the strategy of solving an inverse problem through optimization. At each iterative step, it reprojects keypoints from the human model to feature maps to query feedback, and uses a recurrent-based updater to adjust the model to fit the image better. Because ReFit encodes strong knowledge of the inverse problem, it is faster to train than previous regression models. At the same time, ReFit improves state-of-the-art performance on standard benchmarks. Moreover, ReFit applies to other optimization settings, such as multi-view fitting and single-view shape fitting. Project website: https://yufu-wang.github. io/refit_humans/
Learned Lock-free Search Data Structures

Non-blocking search data structures offer scalability with a progress guarantee on high-performance multi-core architectures. In the recent past,"learned queries"have gained remarkable attention. It refers to predicting the rank of a key computed by machine learning models trained to infer the cumulative distribution function of an ordered dataset. A line of works exhibits the superiority of learned queries over classical query algorithms. Yet, to our knowledge, no existing non-blocking search data structure employs them. In this paper, we introduce \textbf{Kanva}, a framework for learned non-blocking search. Kanva has an intuitive yet non-trivial design: traverse down a shallow hierarchy of lightweight linear models to reach the"non-blocking bins,"which are dynamic ordered search structures. The proposed approach significantly outperforms the current state-of-the-art -- non-blocking interpolation search trees and elimination (a,b) trees -- in many workload and data distributions. Kanva is provably linearizable.
LEAP: Efficient and Automated Test Method for NLP Software

The widespread adoption of DNNs in NLP software has highlighted the need for robustness. Researchers proposed various automatic testing techniques for adversarial test cases. However, existing methods suffer from two limitations: weak error-discovering capabilities, with success rates ranging from 0% to 24.6% for BERT-based NLP software, and time inefficiency, taking 177.8s to 205.28s per test case, making them challenging for time-constrained scenarios. To address these issues, this paper proposes LEAP, an automated test method that uses LEvy flight-based Adaptive Particle swarm optimization integrated with textual features to generate adversarial test cases. Specifically, we adopt Levy flight for population initialization to increase the diversity of generated test cases. We also design an inertial weight adaptive update operator to improve the efficiency of LEAP's global optimization of high-dimensional text examples and a mutation operator based on the greedy strategy to reduce the search time. We conducted a series of experiments to validate LEAP's ability to test NLP software and found that the average success rate of LEAP in generating adversarial test cases is 79.1%, which is 6.1% higher than the next best approach (PSOattack). While ensuring high success rates, LEAP significantly reduces time overhead by up to 147.6s compared to other heuristic-based methods. Additionally, the experimental results demonstrate that LEAP can generate more transferable test cases and significantly enhance the robustness of DNN-based systems.
Admissible pairs and $p$-adic Hodge structures I: Transcendence of the de Rham lattice

For an algebraically closed non-archimedean extension $C/\mathbb{Q}_p$, we define a Tannakian category of $p$-adic Hodge structures over $C$ that is a local, $p$-adic analog of the global, archimedean category of $\mathbb{Q}$-Hodge structures in complex geometry. In this setting the filtrations of classical Hodge theory must be enriched to lattices over a complete discrete valuation ring, Fontaine's integral de Rham period ring $B^+_\mathrm{dR}$, and a pure $p$-adic Hodge structure is then a $\mathbb{Q}_p$-vector space equipped with a $B^+_\mathrm{dR}$-lattice satisfying a natural condition analogous to the transversality of the complex Hodge filtration with its conjugate. We show $p$-adic Hodge structures are equivalent to a full subcategory of basic objects in the category of admissible pairs, a toy category of cohomological motives over $C$ that is equivalent to the isogeny category of rigidified Breuil-Kisin-Fargues modules and closely related to Fontaine's $p$-adic Hodge theory over $p$-adic subfields. As an application, we characterize basic admissible pairs with complex multiplication in terms of the transcendence of $p$-adic periods. This generalizes an earlier result for one-dimensional formal groups and is an unconditional, local, $p$-adic analog of a global, archimedean characterization of CM motives over $\mathbb{C}$ conditional on the standard conjectures, the Hodge conjecture, and the Grothendieck period conjecture (known unconditionally for abelian varieties by work Cohen and Shiga and Wolfart).
Decision-Making for Land Conservation: A Derivative-Free Optimization Framework with Nonlinear Inputs

Protected areas (PAs) are designated spaces where human activities are restricted to preserve critical habitats. Decision-makers are challenged with balancing a trade-off of financial feasibility with ecological benefit when establishing PAs. Given the long-term ramifications of these decisions and the constantly shifting environment, it is crucial that PAs are carefully selected with long-term viability in mind. Using AI tools like simulation and optimization is common for designating PAs, but current decision models are primarily linear. In this paper, we propose a derivative-free optimization framework paired with a nonlinear component, population viability analysis (PVA). Formulated as a mixed integer nonlinear programming (MINLP) problem, our model allows for linear and nonlinear inputs. Connectivity, competition, crowding, and other similar concerns are handled by the PVA software, rather than expressed as constraints of the optimization model. In addition, we present numerical results that serve as a proof of concept, showing our models yield PAs with similar expected risk to that of preserving every parcel in a habitat, but at a significantly lower cost. The overall goal is to promote interdisciplinary work by providing a new mathematical programming tool for conservationists that allows for nonlinear inputs and can be paired with existing ecological software. The code and data are available at https://github.com/cassiebuhler/conservation-dfo.
Strain-Induced Polarization Enhancement in BaTiO$_3$ Core-Shell Nanoparticles

Despite fascinating experimental results, the influence of defects and elastic strains on the physical state of nanosized ferroelectrics is still poorly explored theoretically. One of unresolved theoretical problems is the analytical description of the strongly enhanced spontaneous polarization, piezoelectric response, and dielectric properties of ferroelectric oxide thin films and core-shell nanoparticles induced by elastic strains and stresses. In particular, the 10-nm quasi-spherical BaTiO3 core-shell nanoparticles reveal a giant spontaneous polarization up to 130 mu_C/cm2, where the physical origin is a large Ti off-centering. The available theoretical description cannot explain the giant spontaneous polarization observed in these spherical nanoparticles. This work analyzes polar properties of BaTiO3 core-shell spherical nanoparticles using the Landau-Ginzburg-Devonshire approach, which considers the nonlinear electrostriction coupling and large Vegard strains in the shell. We reveal that a spontaneous polarization greater than 50 mu_C/cm2 can be stable in a (10-100) nm BaTiO3 core at room temperature, where a 5 nm paraelectric shell is stretched by (3-6)% due to Vegard strains, which contribute to the elastic mismatch at the core-shell interface. The polarization value 50 mu_C/cm2 corresponds to high tetragonality ratios (1.02 - 1.04), which is further increased up to 100 mu_C/cm2 by higher Vegard strains and/or intrinsic surface stresses leading to unphysically high tetragonality ratios (1.08 - 1.16). The nonlinear electrostriction coupling and the elastic mismatch at the core-shell interface are key physical factors of the spontaneous polarization enhancement in the core. Doping with the highly-polarized core-shell nanoparticles can be useful in optoelectronics and nonlinear optics, electric field enhancement, reduced switching voltages, catalysis, and electrocaloric nanocoolers.