OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Concept Embedding Model (ACEM): No train-time concepts, No issue!
Venue: arXiv cs.LG 2023
Year: 2023

Interpretability and explainability of neural networks is continuously increasing in importance, especially within safety-critical domains and to provide the social right to explanation. Concept based explanations align well with how humans reason, proving to be a good way to explain models. Concept Embedding Models (CEMs) are one such concept based explanation architectures. These have shown to overcome the trade-off between explainability and performance. However, they have a key limitation -- they require concept annotations for all their training data. For large datasets, this can be expensive and infeasible. Motivated by this, we propose Automatic Concept Embedding Models (ACEMs), which learn the concept annotations automatically.
Analog Computing for the 21st Century
Venue: arXiv cs.ET 2023
Year: 2023

Many people think of analog computing as a historic dead-end in computing. In fact, nothing could be further from the truth as analog computing - together with quantum computing - has the potential to bring computing to new levels with respect to raw computational power and energy efficiency. The following paper explains the limits of digital computers, gives a quick introduction to analog computing in general, and shows a number of recent developments that will change the way we think about computers in the next few years.
Multiple Representation Transfer from Large Language Models to End-to-End ASR Systems
Venue: arXiv cs.CL 2023
Year: 2023

Transferring the knowledge of large language models (LLMs) is a promising technique to incorporate linguistic knowledge into end-to-end automatic speech recognition (ASR) systems. However, existing works only transfer a single representation of LLM (e.g. the last layer of pretrained BERT), while the representation of a text is inherently non-unique and can be obtained variously from different layers, contexts and models. In this work, we explore a wide range of techniques to obtain and transfer multiple representations of LLMs into a transducer-based ASR system. While being conceptually simple, we show that transferring multiple representations of LLMs can be an effective alternative to transferring only a single representation.
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usability of scientific data, error-bound lossy compression is proposed and developed as an essential technique for the size reduction of scientific data with constrained data distortion. Among the diverse datasets generated by various scientific simulations, certain datasets cannot be effectively compressed by existing error-bounded lossy compressors with traditional techniques. The recent success of Artificial Intelligence has inspired several researchers to integrate neural networks into error-bounded lossy compressors. However, those works still suffer from limited compression ratios and/or extremely low efficiencies. To address those issues and improve the compression on the hard-to-compress datasets, in this paper, we propose SRN-SZ, which is a deep learning-based scientific error-bounded lossy compressor leveraging the hierarchical data grid expansion paradigm implemented by super-resolution neural networks. SRN-SZ applies the most advanced super-resolution network HAT for its compression, which is free of time-costing per-data training. In experiments compared with various state-of-the-art compressors, SRN-SZ achieves up to 75% compression ratio improvements under the same error bound and up to 80% compression ratio improvements under the same PSNR than the second-best compressor.
UER: A Heuristic Bias Addressing Approach for Online Continual Learning
Venue: arXiv cs.LG 2023
Year: 2023

Online continual learning aims to continuously train neural networks from a continuous data stream with a single pass-through data. As the most effective approach, the rehearsal-based methods replay part of previous data. Commonly used predictors in existing methods tend to generate biased dot-product logits that prefer to the classes of current data, which is known as a bias issue and a phenomenon of forgetting. Many approaches have been proposed to overcome the forgetting problem by correcting the bias; however, they still need to be improved in online fashion. In this paper, we try to address the bias issue by a more straightforward and more efficient method. By decomposing the dot-product logits into an angle factor and a norm factor, we empirically find that the bias problem mainly occurs in the angle factor, which can be used to learn novel knowledge as cosine logits. On the contrary, the norm factor abandoned by existing methods helps remember historical knowledge. Based on this observation, we intuitively propose to leverage the norm factor to balance the new and old knowledge for addressing the bias. To this end, we develop a heuristic approach called unbias experience replay (UER). UER learns current samples only by the angle factor and further replays previous samples by both the norm and angle factors. Extensive experiments on three datasets show that UER achieves superior performance over various state-of-the-art methods. The code is in https://github.com/FelixHuiweiLin/UER.
Hardness of Approximating Bounded-Degree Max 2-CSP and Independent Set on k-Claw-Free Graphs
Venue: arXiv cs.DS 2023
Year: 2023

We consider the question of approximating Max 2-CSP where each variable appears in at most $d$ constraints (but with possibly arbitrarily large alphabet). There is a simple $(\frac{d+1}{2})$-approximation algorithm for the problem. We prove the following results for any sufficiently large $d$: - Assuming the Unique Games Conjecture (UGC), it is NP-hard (under randomized reduction) to approximate this problem to within a factor of $\left(\frac{d}{2} - o(d)\right)$. - It is NP-hard (under randomized reduction) to approximate the problem to within a factor of $\left(\frac{d}{3} - o(d)\right)$. Thanks to a known connection [Dvorak et al., Algorithmica 2023], we establish the following hardness results for approximating Maximum Independent Set on $k$-claw-free graphs: - Assuming the Unique Games Conjecture (UGC), it is NP-hard (under randomized reduction) to approximate this problem to within a factor of $\left(\frac{k}{4} - o(k)\right)$. - It is NP-hard (under randomized reduction) to approximate the problem to within a factor of $\left(\frac{k}{3 + 2\sqrt{2}} - o(k)\right) \geq \left(\frac{k}{5.829} - o(k)\right)$. In comparison, known approximation algorithms achieve $\left(\frac{k}{2} - o(k)\right)$-approximation in polynomial time [Neuwohner, STACS 2021; Thiery and Ward, SODA 2023] and $(\frac{k}{3} + o(k))$-approximation in quasi-polynomial time [Cygan et al., SODA 2013].
A Grounded Conceptual Model for Ownership Types in Rust
Venue: arXiv cs.PL 2023
Year: 2023

Programmers learning Rust struggle to understand ownership types, Rust's core mechanism for ensuring memory safety without garbage collection. This paper describes our attempt to systematically design a pedagogy for ownership types. First, we studied Rust developers' misconceptions of ownership to create the Ownership Inventory, a new instrument for measuring a person's knowledge of ownership. We found that Rust learners could not connect Rust's static and dynamic semantics, such as determining why an ill-typed program would (or would not) exhibit undefined behavior. Second, we created a conceptual model of Rust's semantics that explains borrow checking in terms of flow-sensitive permissions on paths into memory. Third, we implemented a Rust compiler plugin that visualizes programs under the model. Fourth, we integrated the permissions model and visualizations into a broader pedagogy of ownership by writing a new ownership chapter for The Rust Programming Language, a popular Rust textbook. Fifth, we evaluated an initial deployment of our pedagogy against the original version, using reader responses to the Ownership Inventory as a point of comparison. Thus far, the new pedagogy has improved learner scores on the Ownership Inventory by an average of 9% ($N = 342, d = 0.56$).
Sparse-DFT and WHT Precoding with Iterative Detection for Highly Frequency-Selective Channels
Venue: arXiv cs.IT 2023
Year: 2023

Various precoders have been recently studied by the wireless community to combat the channel fading effects. Two prominent precoders are implemented with the discrete Fourier transform (DFT) and Walsh-Hadamard transform (WHT). The WHT precoder is implemented with less complexity since it does not need complex multiplications. Also, spreading can be applied sparsely to decrease the transceiver complexity, leading to sparse DFT (SDFT) and sparse Walsh-Hadamard (SWH). Another relevant topic is the design of iterative receivers that deal with inter-symbol-interference (ISI). In particular, many detectors based on expectation propagation (EP) have been proposed recently for channels with high levels of ISI. An alternative is the maximum a-posterior (MAP) detector, although it leads to unfeasible high complexity in many cases. In this paper, we provide a relatively low-complexity \textcolor{black}{computation} of the MAP detector for the SWH. We also propose two \textcolor{black}{feasible methods} based on the Log-MAP and Max-Log-MAP. Additionally, the DFT, SDFT and SWH precoders are compared using an EP-based receiver with one-tap FD equalization. Lastly, SWH-Max-Log-MAP is compared to the (S)DFT with EP-based receiver in terms of performance and complexity. The results show that the proposed SWH-Max-Log-MAP has a better performance and complexity trade-off for QPSK and 16-QAM under highly selective channels, but has unfeasible complexity for higher QAM orders.
Grouping Boundary Proposals for Fast Interactive Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Geodesic models are known as an efficient tool for solving various image segmentation problems. Most of existing approaches only exploit local pointwise image features to track geodesic paths for delineating the objective boundaries. However, such a segmentation strategy cannot take into account the connectivity of the image edge features, increasing the risk of shortcut problem, especially in the case of complicated scenario. In this work, we introduce a new image segmentation model based on the minimal geodesic framework in conjunction with an adaptive cut-based circular optimal path computation scheme and a graph-based boundary proposals grouping scheme. Specifically, the adaptive cut can disconnect the image domain such that the target contours are imposed to pass through this cut only once. The boundary proposals are comprised of precomputed image edge segments, providing the connectivity information for our segmentation model. These boundary proposals are then incorporated into the proposed image segmentation model, such that the target segmentation contours are made up of a set of selected boundary proposals and the corresponding geodesic paths linking them. Experimental results show that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.
Double RIS-Assisted MIMO Systems Over Spatially Correlated Rician Fading Channels and Finite Scatterers
Venue: arXiv cs.IT 2023
Year: 2023

This paper investigates double RIS-assisted MIMO communication systems over Rician fading channels with finite scatterers, spatial correlation, and the existence of a double-scattering link between the transceiver. First, the statistical information is driven in closed form for the aggregated channels, unveiling various influences of the system and environment on the average channel power gains. Next, we study two active and passive beamforming designs corresponding to two objectives. The first problem maximizes channel capacity by jointly optimizing the active precoding and combining matrices at the transceivers and passive beamforming at the double RISs subject to the transmitting power constraint. In order to tackle the inherently non-convex issue, we propose an efficient alternating optimization algorithm (AO) based on the alternating direction method of multipliers (ADMM). The second problem enhances communication reliability by jointly training the encoder and decoder at the transceivers and the phase shifters at the RISs. Each neural network representing a system entity in an end-to-end learning framework is proposed to minimize the symbol error rate of the detected symbols by controlling the transceiver and the RISs phase shifts. Numerical results verify our analysis and demonstrate the superior improvements of phase shift designs to boost system performance.
Cyclic Operator Precedence Grammars for Parallel Parsing
Venue: arXiv cs.FL 2023
Year: 2023

Operator precedence languages (OPL) enjoy the local parsability property, which essentially means that a code fragment enclosed within a pair of markers -- playing the role of parentheses -- can be compiled with no knowledge of its external context. Such a property has been exploited to build parallel compilers for languages formalized as OPLs. It has been observed, however, that when the syntax trees of the sentences have a linear substructure, its parsing must necessarily proceed sequentially making it impossible to split such a subtree into chunks to be processed in parallel. Such an inconvenience is due to the fact that so far much literature on OPLs has assumed the hypothesis that equality precedence relation cannot be cyclic. This hypothesis was motivated by the need to keep the mathematical notation as simple as possible. We present an enriched version of operator precedence grammars, called cyclic, that allows to use a simplified version of regular expressions in the right hand sides of grammar's rules; for this class of operator precedence grammars the acyclicity hypothesis of the equality precedence relation is no more needed to guarantee the algebraic properties of the generated languages. The expressive power of the cyclic grammars is now fully equivalent to that of other formalisms defining OPLs such as operator precedence automata, monadic second order logic and operator precedence expressions. As a result cyclic operator precedence grammars now produce also unranked syntax trees and sentences with flat unbounded substructures that can be naturally partitioned into chunks suitable for parallel parsing.
Towards Practical Capture of High-Fidelity Relightable Avatars
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we propose a novel framework, Tracking-free Relightable Avatar (TRAvatar), for capturing and reconstructing high-fidelity 3D avatars. Compared to previous methods, TRAvatar works in a more practical and efficient setting. Specifically, TRAvatar is trained with dynamic image sequences captured in a Light Stage under varying lighting conditions, enabling realistic relighting and real-time animation for avatars in diverse scenes. Additionally, TRAvatar allows for tracking-free avatar capture and obviates the need for accurate surface tracking under varying illumination conditions. Our contributions are two-fold: First, we propose a novel network architecture that explicitly builds on and ensures the satisfaction of the linear nature of lighting. Trained on simple group light captures, TRAvatar can predict the appearance in real-time with a single forward pass, achieving high-quality relighting effects under illuminations of arbitrary environment maps. Second, we jointly optimize the facial geometry and relightable appearance from scratch based on image sequences, where the tracking is implicitly learned. This tracking-free approach brings robustness for establishing temporal correspondences between frames under different lighting conditions. Extensive qualitative and quantitative experiments demonstrate that our framework achieves superior performance for photorealistic avatar animation and relighting.
From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
Venue: arXiv cs.CL 2023
Year: 2023

Selecting the ``right'' amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and hard to follow. To better understand this tradeoff, we solicit increasingly dense GPT-4 summaries with what we refer to as a ``Chain of Density'' (CoD) prompt. Specifically, GPT-4 generates an initial entity-sparse summary before iteratively incorporating missing salient entities without increasing the length. Summaries generated by CoD are more abstractive, exhibit more fusion, and have less of a lead bias than GPT-4 summaries generated by a vanilla prompt. We conduct a human preference study on 100 CNN DailyMail articles and find that that humans prefer GPT-4 summaries that are more dense than those generated by a vanilla prompt and almost as dense as human written summaries. Qualitative analysis supports the notion that there exists a tradeoff between informativeness and readability. 500 annotated CoD summaries, as well as an extra 5,000 unannotated summaries, are freely available on HuggingFace (https://huggingface.co/datasets/griffin/chain_of_density).
Data-Flow-Based Normalization Generation Algorithm of R1CS for Zero-Knowledge Proof
Venue: arXiv cs.FL 2023
Year: 2023

The communities of blockchains and distributed ledgers have been stirred up by the introduction of zero-knowledge proofs (ZKPs). Originally designed to solve privacy issues, ZKPs have now evolved into an effective remedy for scalability concerns and are applied in Zcash (internet money like Bitcoin). To enable ZKPs, Rank-1 Constraint Systems (R1CS) offer a verifier for bi-linear equations. To accurately and efficiently represent R1CS, several language tools like Circom, Noir, and Snarky have been proposed to automate the compilation of advanced programs into R1CS. However, due to the flexible nature of R1CS representation, there can be significant differences in the compiled R1CS forms generated from circuit language programs with the same underlying semantics. To address this issue, this paper uses a data-flow-based R1CS paradigm algorithm, which produces a standardized format for different R1CS instances with identical semantics. By using the normalized R1CS format circuits, the complexity of circuits' verification can be reduced. In addition, this paper presents an R1CS normalization algorithm benchmark, and our experimental evaluation demonstrates the effectiveness and correctness of our methods.
How can feature usage be tracked across product variants? Implicit Feedback in Software Product Lines
Venue: arXiv cs.SE 2023
Year: 2023

Implicit feedback is collecting information about software usage to understand how and when the software is used. This research tackles implicit feedback in Software Product Lines (SPLs). The need for platform-centric feedback makes SPL feedback depart from one-off-application feedback in both the artefact to be tracked (the platform vs the variant) as well as the tracking approach (indirect coding vs direct coding). Traditionally, product feedback is achieved by embedding `usage trackers' into the software's code. Yet, products are now members of the SPL portfolio, and hence, this approach conflicts with one of the main SPL tenants: reducing, if not eliminating, coding directly into the variant's code. Thus, we advocate for Product Derivation to be subject to a second transformation that precedes the construction of the variant based on the configuration model. This approach is tested through FEACKER, an extension to pure::variants. We resorted to a TAM evaluation on pure-systems GmbH employees(n=8). Observed divergences were next tackled through a focus group (n=3). The results reveal agreement in the interest in conducting feedback analysis at the platform level (perceived usefulness) while regarding FEACKER as a seamless
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations
Venue: arXiv cs.CL 2023
Year: 2023

Fuzzy Fingerprints have been successfully used as an interpretable text classification technique, but, like most other techniques, have been largely surpassed in performance by Large Pre-trained Language Models, such as BERT or RoBERTa. These models deliver state-of-the-art results in several Natural Language Processing tasks, namely Emotion Recognition in Conversations (ERC), but suffer from the lack of interpretability and explainability. In this paper, we propose to combine the two approaches to perform ERC, as a means to obtain simpler and more interpretable Large Language Models-based classifiers. We propose to feed the utterances and their previous conversational turns to a pre-trained RoBERTa, obtaining contextual embedding utterance representations, that are then supplied to an adapted Fuzzy Fingerprint classification module. We validate our approach on the widely used DailyDialog ERC benchmark dataset, in which we obtain state-of-the-art level results using a much lighter model.
Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes
Venue: arXiv cs.CV 2023
Year: 2023

In the life cycle of highly automated systems operating in an open and dynamic environment, the ability to adjust to emerging challenges is crucial. For systems integrating data-driven AI-based components, rapid responses to deployment issues require fast access to related data for testing and reconfiguration. In the context of automated driving, this especially applies to road obstacles that were not included in the training data, commonly referred to as out-of-distribution (OoD) road obstacles. Given the availability of large uncurated recordings of driving scenes, a pragmatic approach is to query a database to retrieve similar scenarios featuring the same safety concerns due to OoD road obstacles. In this work, we extend beyond identifying OoD road obstacles in video streams and offer a comprehensive approach to extract sequences of OoD road obstacles using text queries, thereby proposing a way of curating a collection of OoD data for subsequent analysis. Our proposed method leverages the recent advances in OoD segmentation and multi-modal foundation models to identify and efficiently extract safety-relevant scenes from unlabeled videos. We present a first approach for the novel task of text-based OoD object retrieval, which addresses the question ''Have we ever encountered this before?''.
On the performance of an integrated communication and localization system: an analytical framework
Venue: arXiv cs.IT 2023
Year: 2023

Quantifying the performance bound of an integrated localization and communication (ILAC) system and the trade-off between communication and localization performance is critical. In this letter, we consider an ILAC system that can perform communication and localization via time-domain or frequency-domain resource allocation. We develop an analytical framework to derive the closed-form expression of the capacity loss versus localization Cramer-Rao lower bound (CRB) loss via time-domain and frequency-domain resource allocation. Simulation results validate the analytical model and demonstrate that frequency-domain resource allocation is preferable in scenarios with a smaller number of antennas at the next generation nodeB (gNB) and a larger distance between user equipment (UE) and gNB, while time-domain resource allocation is preferable in scenarios with a larger number of antennas and smaller distance between UE and the gNB.