OpenRaiser/Intern-Atlas huggingface dataset:
Global One-Counter Tree Automata
Venue: arXiv cs.FL 2024
Year: 2024

We introduce global one-counter tree automata (GOCTA) which deviate from usual counter tree automata by working on only one counter which is passed through the tree in lexicographical order, rather than duplicating the counter at every branching position. We compare the capabilities of GOCTA to those of counter tree automata and obtain that their classes of recognizable tree languages are incomparable. Moreover, we show that the emptiness problem of GOCTA is undecidable while, in stark contrast, their membership problem is in P.
ECLIPSE: Expunging Clean-label Indiscriminate Poisons via Sparse Diffusion Purification
Venue: arXiv cs.CR 2024
Year: 2024

Clean-label indiscriminate poisoning attacks add invisible perturbations to correctly labeled training images, thus dramatically reducing the generalization capability of the victim models. Recently, some defense mechanisms have been proposed such as adversarial training, image transformation techniques, and image purification. However, these schemes are either susceptible to adaptive attacks, built on unrealistic assumptions, or only effective against specific poison types, limiting their universal applicability. In this research, we propose a more universally effective, practical, and robust defense scheme called ECLIPSE. We first investigate the impact of Gaussian noise on the poisons and theoretically prove that any kind of poison will be largely assimilated when imposing sufficient random noise. In light of this, we assume the victim has access to an extremely limited number of clean images (a more practical scene) and subsequently enlarge this sparse set for training a denoising probabilistic model (a universal denoising tool). We then begin by introducing Gaussian noise to absorb the poisons and then apply the model for denoising, resulting in a roughly purified dataset. Finally, to address the trade-off of the inconsistency in the assimilation sensitivity of different poisons by Gaussian noise, we propose a lightweight corruption compensation module to effectively eliminate residual poisons, providing a more universal defense approach. Extensive experiments demonstrate that our defense approach outperforms 10 state-of-the-art defenses. We also propose an adaptive attack against ECLIPSE and verify the robustness of our defense scheme. Our code is available at https://github.com/CGCL-codes/ECLIPSE.
Basilisk: An End-to-End Open-Source Linux-Capable RISC-V SoC in 130nm CMOS
Venue: arXiv cs.AR 2024
Year: 2024

Open-source hardware (OSHW) is rapidly gaining traction in academia and industry. The availability of open RTL descriptions, EDA tools, and even PDKs enables a fully auditable supply chain for end-to-end (RTL to layout) open-source silicon, significantly strengthening security and transparency. Despite promising developments, existing OSHW efforts have so far fallen short of producing end-to-end open-source SoCs at the complexity and performance level needed to run a general-purpose OS. We present Basilisk, the first end-to-end open-source, Linux-capable RISC-V SoC taped out in IHP's open 130 nm technology. Basilisk features a 64-bit RISC-V core, a fully digital HyperRAM DRAM controller, and a rich set of IO peripherals including USB 1.1 and VGA. To tape out Basilisk, we create a reusable tool pipeline to convert its industry-grade SystemVerilog description to Verilog. We optimized logic synthesis in the open source Yosys synthesis tool, obtaining an increase in Basilisk's peak clock speed by 2.3x to 77 MHz and reducing its cell area by 1.6x to 1.1 MGE while also reducing synthesis runtime and RAM usage. We further optimized place and route in OpenROAD, enabling convergence to zero DRC violations while increasing core area utilization by 10% and reducing die area by 12%.
Brain-Like Language Processing via a Shallow Untrained Multihead Attention Network
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) have been shown to be effective models of the human language system, with some models predicting most explainable variance of brain activity in current datasets. Even in untrained models, the representations induced by architectural priors can exhibit reasonable alignment to brain data. In this work, we investigate the key architectural components driving the surprising alignment of untrained models. To estimate LLM-to-brain similarity, we first select language-selective units within an LLM, similar to how neuroscientists identify the language network in the human brain. We then benchmark the brain alignment of these LLM units across five different brain recording datasets. By isolating critical components of the Transformer architecture, we identify tokenization strategy and multihead attention as the two major components driving brain alignment. A simple form of recurrence further improves alignment. We further demonstrate this quantitative brain alignment of our model by reproducing landmark studies in the language neuroscience field, showing that localized model units -- just like language voxels measured empirically in the human brain -- discriminate more reliably between lexical than syntactic differences, and exhibit similar response profiles under the same experimental conditions. Finally, we demonstrate the utility of our model's representations for language modeling, achieving improved sample and parameter efficiency over comparable architectures. Our model's estimates of surprisal sets a new state-of-the-art in the behavioral alignment to human reading times. Taken together, we propose a highly brain- and behaviorally-aligned model that conceptualizes the human language system as an untrained shallow feature encoder, with structural priors, combined with a trained decoder to achieve efficient and performant language processing.
Surface Normal Reconstruction Using Polarization-Unet
Venue: arXiv cs.CV 2024
Year: 2024

Today, three-dimensional reconstruction of objects has many applications in various fields, and therefore, choosing a suitable method for high resolution three-dimensional reconstruction is an important issue and displaying high-level details in three-dimensional models is a serious challenge in this field. Until now, active methods have been used for high-resolution three-dimensional reconstruction. But the problem of active three-dimensional reconstruction methods is that they require a light source close to the object. Shape from polarization (SfP) is one of the best solutions for high-resolution three-dimensional reconstruction of objects, which is a passive method and does not have the drawbacks of active methods. The changes in polarization of the reflected light from an object can be analyzed by using a polarization camera or locating polarizing filter in front of the digital camera and rotating the filter. Using this information, the surface normal can be reconstructed with high accuracy, which will lead to local reconstruction of the surface details. In this paper, an end-to-end deep learning approach has been presented to produce the surface normal of objects. In this method a benchmark dataset has been used to train the neural network and evaluate the results. The results have been evaluated quantitatively and qualitatively by other methods and under different lighting conditions. The MAE value (Mean-Angular-Error) has been used for results evaluation. The evaluations showed that the proposed method could accurately reconstruct the surface normal of objects with the lowest MAE value which is equal to 18.06 degree on the whole dataset, in comparison to previous physics-based methods which are between 41.44 and 49.03 degree.
Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss
Venue: arXiv cs.CL 2024
Year: 2024

Accurately modeling idiomatic or non-compositional language has been a longstanding challenge in Natural Language Processing (NLP). This is partly because these expressions do not derive their meanings solely from their constituent words, but also due to the scarcity of relevant data resources, and their impact on the performance of downstream tasks such as machine translation and simplification. In this paper we propose an approach to model idiomaticity effectively using a triplet loss that incorporates the asymmetric contribution of components words to an idiomatic meaning for training language models by using adaptive contrastive learning and resampling miners to build an idiomatic-aware learning objective. Our proposed method is evaluated on a SemEval challenge and outperforms previous alternatives significantly in many metrics.
Supermodular Approximation of Norms and Applications
Venue: arXiv cs.DS 2024
Year: 2024

Many classical problems in theoretical computer science involve norm, even if implicitly; for example, both XOS functions and downward-closed sets are equivalent to some norms. The last decade has seen a lot of interest in designing algorithms beyond the standard $\ell_p$ norms $\|\cdot \|_p$. Despite notable advancements, many existing methods remain tailored to specific problems, leaving a broader applicability to general norms less understood. This paper investigates the intrinsic properties of $\ell_p$ norms that facilitate their widespread use and seeks to abstract these qualities to a more general setting. We identify supermodularity -- often reserved for combinatorial set functions and characterized by monotone gradients -- as a defining feature beneficial for $ \|\cdot\|_p^p$. We introduce the notion of $p$-supermodularity for norms, asserting that a norm is $p$-supermodular if its $p^{th}$ power function exhibits supermodularity. The association of supermodularity with norms offers a new lens through which to view and construct algorithms. Our work demonstrates that for a large class of problems $p$-supermodularity is a sufficient criterion for developing good algorithms. This is either by reframing existing algorithms for problems like Online Load-Balancing and Bandits with Knapsacks through a supermodular lens, or by introducing novel analyses for problems such as Online Covering, Online Packing, and Stochastic Probing. Moreover, we prove that every symmetric norm can be approximated by a $p$-supermodular norm. Together, these recover and extend several results from the literature, and support $p$-supermodularity as a unified theoretical framework for optimization challenges centered around norm-related problems.
Causal Learning in Biomedical Applications: A Benchmark
Venue: arXiv cs.LG 2024
Year: 2024

Learning causal relationships between a set of variables is a challenging problem in computer science. Many existing artificial benchmark datasets are based on sampling from causal models and thus contain residual information that the ${R} ^2$-sortability can identify. Here, we present a benchmark for methods in causal learning using time series. The presented dataset is not ${R}^2$-sortable and is based on a real-world scenario of the Krebs cycle that is used in cells to release energy. We provide four scenarios of learning, including short and long time series, and provide guidance so that testing is unified between possible users.
Landscape More Secure Than Portrait? Zooming Into the Directionality of Digital Images With Security Implications
Venue: arXiv cs.CR 2024
Year: 2024

The orientation in which a source image is captured can affect the resulting security in downstream applications. One reason for this is that many state-of-the-art methods in media security assume that image statistics are similar in the horizontal and vertical directions, allowing them to reduce the number of features (or trainable weights) by merging coefficients. We show that this artificial symmetrization tends to suppress important properties of natural images and common processing operations, causing a loss of performance. We also observe the opposite problem, where unaddressed directionality causes learning-based methods to overfit to a single orientation. These are vulnerable to manipulation if an adversary chooses inputs with the less common orientation. This paper takes a comprehensive approach, identifies and systematizes causes of directionality at several stages of a typical acquisition pipeline, measures their effect, and demonstrates for three selected security applications (steganalysis, forensic source identification, and the detection of synthetic images) how the performance of state-of-the-art methods can be improved by properly accounting for directionality.
Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting
Venue: arXiv cs.AI 2024
Year: 2024

Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging field with numerous applications. Operator-controlled UAV flights and manual static flight configurations are major limitations for the wide adoption of scalability of UAV services. Several services depend on excellent UAV connectivity with a cellular network and maintaining it is challenging in predetermined flight paths. This paper addresses these limitations by proposing a Deep Reinforcement Learning (DRL) framework for UAV path planning with assured connectivity (DUPAC). During UAV flight, DUPAC determines the best route from a defined source to the destination in terms of distance and signal quality. The viability and performance of DUPAC are evaluated under simulated real-world urban scenarios using the Unity framework. The results confirm that DUPAC achieves an autonomous UAV flight path similar to base method with only 2% increment while maintaining an average 9% better connection quality throughout the flight.
A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation
Venue: arXiv cs.CL 2024
Year: 2024

This paper proposes a novel approach to evaluate Counter Narrative (CN) generation using a Large Language Model (LLM) as an evaluator. We show that traditional automatic metrics correlate poorly with human judgements and fail to capture the nuanced relationship between generated CNs and human perception. To alleviate this, we introduce a model ranking pipeline based on pairwise comparisons of generated CNs from different models, organized in a tournament-style format. The proposed evaluation method achieves a high correlation with human preference, with a $ρ$ score of 0.88. As an additional contribution, we leverage LLMs as zero-shot CN generators and provide a comparative analysis of chat, instruct, and base models, exploring their respective strengths and limitations. Through meticulous evaluation, including fine-tuning experiments, we elucidate the differences in performance and responsiveness to domain-specific data. We conclude that chat-aligned models in zero-shot are the best option for carrying out the task, provided they do not refuse to generate an answer due to security concerns.
Fingerprint Membership and Identity Inference Against Generative Adversarial Networks
Venue: arXiv cs.CV 2024
Year: 2024

Generative models are gaining significant attention as potential catalysts for a novel industrial revolution. Since automated sample generation can be useful to solve privacy and data scarcity issues that usually affect learned biometric models, such technologies became widely spread in this field. In this paper, we assess the vulnerabilities of generative machine learning models concerning identity protection by designing and testing an identity inference attack on fingerprint datasets created by means of a generative adversarial network. Experimental results show that the proposed solution proves to be effective under different configurations and easily extendable to other biometric measurements.
Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metrics
Venue: arXiv cs.IR 2024
Year: 2024

Large language models (LLMs) often produce unsupported or unverifiable content, known as "hallucinations." To mitigate this, retrieval-augmented LLMs incorporate citations, grounding the content in verifiable sources. Despite such developments, manually assessing how well a citation supports the associated statement remains a major challenge. Previous studies use faithfulness metrics to estimate citation support automatically but are limited to binary classification, overlooking fine-grained citation support in practical scenarios. To investigate the effectiveness of faithfulness metrics in fine-grained scenarios, we propose a comparative evaluation framework that assesses the metric effectiveness in distinguishing citations between three-category support levels: full, partial, and no support. Our framework employs correlation analysis, classification evaluation, and retrieval evaluation to measure the alignment between metric scores and human judgments comprehensively. Our results show no single metric consistently excels across all evaluations, revealing the complexity of assessing fine-grained support. Based on the findings, we provide practical recommendations for developing more effective metrics.
Computing Optimal Manipulations in Cryptographic Self-Selection Proof-of-Stake Protocols
Venue: arXiv cs.GT 2024
Year: 2024

Cryptographic Self-Selection is a paradigm employed by modern Proof-of-Stake consensus protocols to select a block-proposing "leader." Algorand [Chen and Micali, 2019] proposes a canonical protocol, and Ferreira et al. [2022] establish bounds $f(α,β)$ on the maximum fraction of rounds a strategic player can lead as a function of their stake $α$ and a network connectivity parameter $β$. While both their lower and upper bounds are non-trivial, there is a substantial gap between them (for example, they establish $f(10\%,1) \in [10.08\%, 21.12\%]$), leaving open the question of how significant of a concern these manipulations are. We develop computational methods to provably nail $f(α,β)$ for any desired $(α,β)$ up to arbitrary precision, and implement our method on a wide range of parameters (for example, we confirm $f(10\%,1) \in [10.08\%, 10.15\%]$). Methodologically, estimating $f(α,β)$ can be phrased as estimating to high precision the value of a Markov Decision Process whose states are countably-long lists of real numbers. Our methodological contributions involve (a) reformulating the question instead as computing to high precision the expected value of a distribution that is a fixed-point of a non-linear sampling operator, and (b) provably bounding the error induced by various truncations and sampling estimations of this distribution (which appears intractable to solve in closed form). One technical challenge, for example, is that natural sampling-based estimates of the mean of our target distribution are \emph{not} unbiased estimators, and therefore our methods necessarily go beyond claiming sufficiently-many samples to be close to the mean.
BliMe Linter
Venue: arXiv cs.CR 2024
Year: 2024

Outsourced computation presents a risk to the confidentiality of clients' sensitive data since they have to trust that the service providers will not mishandle this data. Blinded Memory (BliMe) is a set of hardware extensions that addresses this problem by using hardware-based taint tracking to keep track of sensitive client data and enforce a security policy that prevents software from leaking this data, either directly or through side channels. Since programs can leak sensitive data through timing channels and memory access patterns when this data is used in control-flow or memory access instructions, BliMe prohibits such unsafe operations and only allows constant-time code to operate on sensitive data. The question is how a developer can confirm that their code will run correctly on BliMe. While a program can be manually checked to see if it is constant-time, this process is tedious and error-prone. In this paper, we introduce the BliMe linter, a set of compiler extensions built on top of SVF that analyze LLVM bitcode to identify possible BliMe violations. We evaluate the BliMe linter analytically and empirically and show that it is sound.
Advanced Multimodal Deep Learning Architecture for Image-Text Matching
Venue: arXiv cs.LG 2024
Year: 2024

Image-text matching is a key multimodal task that aims to model the semantic association between images and text as a matching relationship. With the advent of the multimedia information age, image, and text data show explosive growth, and how to accurately realize the efficient and accurate semantic correspondence between them has become the core issue of common concern in academia and industry. In this study, we delve into the limitations of current multimodal deep learning models in processing image-text pairing tasks. Therefore, we innovatively design an advanced multimodal deep learning architecture, which combines the high-level abstract representation ability of deep neural networks for visual information with the advantages of natural language processing models for text semantic understanding. By introducing a novel cross-modal attention mechanism and hierarchical feature fusion strategy, the model achieves deep fusion and two-way interaction between image and text feature space. In addition, we also optimize the training objectives and loss functions to ensure that the model can better map the potential association structure between images and text during the learning process. Experiments show that compared with existing image-text matching models, the optimized new model has significantly improved performance on a series of benchmark data sets. In addition, the new model also shows excellent generalization and robustness on large and diverse open scenario datasets and can maintain high matching performance even in the face of previously unseen complex situations.
The disruption index suffers from citation inflation and is confounded by shifts in scholarly citation practice
Venue: arXiv cs.DL 2024
Year: 2024

Measuring the rate of innovation in academia and industry is fundamental to monitoring the efficiency and competitiveness of the knowledge economy. To this end, a disruption index (CD) was recently developed and applied to publication and patent citation networks (Wu et al., Nature 2019; Park et al., Nature 2023). Here we show that CD systematically decreases over time due to secular growth in research and patent production, following two distinct mechanisms unrelated to innovation -- one behavioral and the other structural. Whereas the behavioral explanation reflects shifts associated with techno-social factors (e.g. self-citation practices), the structural explanation follows from `citation inflation' (CI), an inextricable feature of real citation networks attributable to increasing reference list lengths, which causes CD to systematically decrease. We demonstrate this causal link by way of mathematical deduction, computational simulation, multi-variate regression, and quasi-experimental comparison of the disruptiveness of PNAS versus PNAS Plus articles, which differ only in their lengths. Accordingly, we analyze CD data available in the SciSciNet database and find that disruptiveness incrementally increased from 2005-2015, and that the negative relationship between disruption and team-size is remarkably small in overall magnitude effect size, and shifts from negative to positive for team size $\geq$ 8 coauthors.