OpenRaiser/Intern-Atlas huggingface dataset:
ReLoki: Infrastructure-free Distributed Relative Localization using On-board UWB Antenna Arrays
Venue: arXiv cs.RO 2024
Year: 2024

Coordination of multi-robot systems require some form of localization between agents, but most methods today rely on some external infrastructure. Ultra Wide Band (UWB) sensing has gained popularity in relative localization applications, and we see many implementations that use cooperative agents augmenting UWB range measurements with other sensing modalities (e.g., ViO, IMU, VSLAM) for infrastructure-free relative localization. A lesser researched option is using Angle of Arrival (AoA) readings obtained from UWB Antenna pairs to perform relative localization. In this paper we present a UWB platform called ReLoki that can be used for ranging and AoA-based relative localization in~3D. ReLoki enables any message sent from a transmitting agent to be localized by using a Regular Tetrahedral Antenna Array (RTA). As a full scale proof of concept, we deploy ReLoki on a 3-robot system and compare its performance in terms of accuracy and speed with prior methods.
Towards Optimal Grammars for RNA Structures
Venue: arXiv cs.DS 2024
Year: 2024

In past work (Onokpasa, Wild, Wong, DCC 2023), we showed that (a) for joint compression of RNA sequence and structure, stochastic context-free grammars are the best known compressors and (b) that grammars which have better compression ability also show better performance in ab initio structure prediction. Previous grammars were manually curated by human experts. In this work, we develop a framework for automatic and systematic search algorithms for stochastic grammars with better compression (and prediction) ability for RNA. We perform an exhaustive search of small grammars and identify grammars that surpass the performance of human-expert grammars.
A Family of Low-Complexity Binary Codes with Constant Hamming Weights
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we focus on the design of binary constant weight codes that admit low-complexity encoding and decoding algorithms, and that have a size $M=2^k$. For every integer $\ell \geq 3$, we construct a $(n=2^\ell, M=2^{k_{\ell}}, d=2)$ constant weight code ${\cal C}[\ell]$ of weight $\ell$ by encoding information in the gaps between successive $1$'s. The code is associated with an integer sequence of length $\ell$ with a constraint defined as {\em anchor-decodability} that ensures low complexity for encoding and decoding. The complexity of the encoding is linear in the input size $k$, and that of the decoding is poly-logarithmic in the input size $n$, discounting the linear time spent on parsing the input. Both the algorithms do not require expensive computation of binomial coefficients, unlike the case in many existing schemes. Among codes generated by all anchor-decodable sequences, we show that ${\cal C}[\ell]$ has the maximum size with $k_{\ell} \geq \ell^2-\ell\log_2\ell + \log_2\ell - 0.279\ell - 0.721$. As $k$ is upper bounded by $\ell^2-\ell\log_2\ell +O(\ell)$ information-theoretically, the code ${\cal C}[\ell]$ is optimal in its size with respect to two higher order terms of $\ell$. In particular, $k_\ell$ meets the upper bound for $\ell=3$ and one-bit away for $\ell=4$. On the other hand, we show that ${\cal C}[\ell]$ is not unique in attaining $k_{\ell}$ by constructing an alternate code ${\cal \hat{C}}[\ell]$ again parameterized by an integer $\ell \geq 3$ with a different low-complexity decoder, yet having the same size $2^{k_{\ell}}$ when $3 \leq \ell \leq 7$. Finally, we also derive new codes by modifying ${\cal C}[\ell]$ that offer a wider range on blocklength and weight while retaining low complexity for encoding and decoding. For certain selected values of parameters, these modified codes too have an optimal $k$.
Gradient-Based Language Model Red Teaming
Venue: arXiv cs.CL 2024
Year: 2024

Red teaming is a common strategy for identifying weaknesses in generative language models (LMs), where adversarial prompts are produced that trigger an LM to generate unsafe responses. Red teaming is instrumental for both model alignment and evaluation, but is labor-intensive and difficult to scale when done by humans. In this paper, we present Gradient-Based Red Teaming (GBRT), a red teaming method for automatically generating diverse prompts that are likely to cause an LM to output unsafe responses. GBRT is a form of prompt learning, trained by scoring an LM response with a safety classifier and then backpropagating through the frozen safety classifier and LM to update the prompt. To improve the coherence of input prompts, we introduce two variants that add a realism loss and fine-tune a pretrained model to generate the prompts instead of learning the prompts directly. Our experiments show that GBRT is more effective at finding prompts that trigger an LM to generate unsafe responses than a strong reinforcement learning-based red teaming approach, and succeeds even when the LM has been fine-tuned to produce safer outputs.
Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo
Venue: arXiv cs.AI 2024
Year: 2024

Simulating sampling algorithms with people has proven a useful method for efficiently probing and understanding their mental representations. We propose that the same methods can be used to study the representations of Large Language Models (LLMs). While one can always directly prompt either humans or LLMs to disclose their mental representations introspectively, we show that increased efficiency can be achieved by using LLMs as elements of a sampling algorithm. We explore the extent to which we recover human-like representations when LLMs are interrogated with Direct Sampling and Markov chain Monte Carlo (MCMC). We found a significant increase in efficiency and performance using adaptive sampling algorithms based on MCMC. We also highlight the potential of our method to yield a more general method of conducting Bayesian inference \textit{with} LLMs.
InteractOut: Leveraging Interaction Proxies as Input Manipulation Strategies for Reducing Smartphone Overuse
Venue: arXiv cs.HC 2024
Year: 2024

Smartphone overuse poses risks to people's physical and mental health. However, current intervention techniques mainly focus on explicitly changing screen content (i.e., output) and often fail to persistently reduce smartphone overuse due to being over-restrictive or over-flexible. We present the design and implementation of InteractOut, a suite of implicit input manipulation techniques that leverage interaction proxies to weakly inhibit the natural execution of common user gestures on mobile devices. We present a design space for input manipulations and demonstrate 8 Android implementations of input interventions. We first conducted a pilot lab study (N=30) to evaluate the usability of these interventions. Based on the results, we then performed a 5-week within-subject field experiment (N=42) to evaluate InteractOut in real-world scenarios. Compared to the traditional and common timed lockout technique, InteractOut significantly reduced the usage time by an additional 15.6% and opening frequency by 16.5% on participant-selected target apps. InteractOut also achieved a 25.3% higher user acceptance rate, and resulted in less frustration and better user experience according to participants' subjective feedback. InteractOut demonstrates a new direction for smartphone overuse intervention and serves as a strong complementary set of techniques with existing methods.
T3: Transparent Tracking & Triggering for Fine-grained Overlap of Compute & Collectives
Venue: arXiv cs.AR 2024
Year: 2024

Large Language Models increasingly rely on distributed techniques for their training and inference. These techniques require communication across devices which can reduce scaling efficiency as the number of devices increases. While some distributed techniques can overlap, and thus, hide this communication with independent computations, techniques such as Tensor Parallelism (TP) inherently serialize communication with model execution. One approach to hide this serialized communication is to interleave it with the producer operation (of the communicated data) in a fine-grained manner. However, this fine-grained interleaving of communication and computation in software can be difficult. Furthermore, as with any concurrent execution, it requires compute and memory resources to be shared between computation and communication, causing resource contention that reduces overlapping efficacy. To overcome these challenges, we propose T3 which applies hardware-software co-design to transparently overlap serialized communication while minimizing resource contention with compute. T3 transparently fuses producer operations with the subsequent communication via a simple configuration of the producer's output address space and requires minor software changes. At the hardware level, T3 adds a lightweight track and trigger mechanism to orchestrate the producer's compute, and communication. It further uses compute-enhanced memories for communication's attendant compute. As a result, T3 reduces resource contention, and efficiently overlaps serialized communication with computation. For important Transformer models like T-NLG, T3 speeds up communication-heavy sublayers by 30% geomean (max 47%) and reduces data movement by 22% geomean (max 36%). Furthermore, T3's benefits persist as models scale: geomean 29% for sublayers in $\sim$500-billion parameter models, PALM and MT-NLG.
Optimal Redundancy in Exact Channel Synthesis
Venue: arXiv cs.IT 2024
Year: 2024

We consider the redundancy of the exact channel synthesis problem under an i.i.d. assumption. Existing results provide an upper bound on the unnormalized redundancy that is logarithmic in the block length. We show, via an improved scheme, that the logarithmic term can be halved for most channels and eliminated for all others. For full-support discrete memoryless channels, we show that this is the best possible.
Multivariate Beta Mixture Model: Probabilistic Clustering With Flexible Cluster Shapes
Venue: arXiv cs.LG 2024
Year: 2024

This paper introduces the multivariate beta mixture model (MBMM), a new probabilistic model for soft clustering. MBMM adapts to diverse cluster shapes because of the flexible probability density function of the multivariate beta distribution. We introduce the properties of MBMM, describe the parameter learning procedure, and present the experimental results, showing that MBMM fits diverse cluster shapes on synthetic and real datasets. The code is released anonymously at https://github.com/hhchen1105/mbmm/.
A Random Coding Approach to Performance Analysis of the Ordered Statistic Decoding with Local Constraints
Venue: arXiv cs.IT 2024
Year: 2024

This paper is concerned with the ordered statistic decoding with local constraints (LC-OSD) of binary linear block codes, which is a near maximum-likelihood decoding algorithm. Compared with the conventional OSD, the LC-OSD significantly reduces both the maximum and the average number of searches. The former is achieved by performing the serial list Viterbi algorithm (SLVA) or a two-way flipping pattern tree (FPT) algorithm with local constraints on the test error patterns, while the latter is achieved by incorporating tailored early termination criteria. The main objective of this paper is to explore the relationship between the performance of the LC-OSD and decoding parameters, such as the constraint degree and the maximum list size. To this end, we approximate the local parity-check matrix as a totally random matrix and then estimate the performance of the LC-OSD by analyzing with a saddlepoint approach the performance of random codes over the channels associated with the most reliable bits (MRBs). The random coding approach enables us to derive an upper bound on the performance and predict the average rank of the transmitted codeword in the list delivered by the LC-OSD. This allows us to balance the constraint degree and the maximum list size for the average (or maximum) time complexity reduction. Simulation results show that the approximation by random coding approach is numerically effective and powerful. Simulation results also show that the RS codes decoded by the LC-OSD can approach the random coding union (RCU) bounds, verifying the efficiency and universality of the LC-OSD.
LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field Cameras
Venue: arXiv cs.CV 2024
Year: 2024

Leveraging rich information is crucial for dense prediction tasks. Light field (LF) cameras are instrumental in this regard, as they allow data to be sampled from various perspectives. This capability provides valuable spatial, depth, and angular information, enhancing scene-parsing tasks. However, we have identified two overlooked issues for the LF salient object detection (SOD) task. (1): Previous approaches predominantly employ a customized two-stream design to discover the spatial and depth features within light field images. The network struggles to learn the implicit angular information between different images due to a lack of intra-network data connectivity. (2): Little research has been directed towards the data augmentation strategy for LF SOD. Research on inter-network data connectivity is scant. In this study, we propose an efficient paradigm (LF Tracy) to address those issues. This comprises a single-pipeline encoder paired with a highly efficient information aggregation (IA) module (around 8M parameters) to establish an intra-network connection. Then, a simple yet effective data augmentation strategy called MixLD is designed to bridge the inter-network connections. Owing to this innovative paradigm, our model surpasses the existing state-of-the-art method through extensive experiments. Especially, LF Tracy demonstrates a 23% improvement over previous results on the latest large-scale PKU dataset. The source code is publicly available at: https://github.com/FeiBryantkit/LF-Tracy.
SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing
Venue: arXiv cs.LG 2024
Year: 2024

There has been a proliferation of artificial intelligence applications, where model training is key to promising high-quality services for these applications. However, the model training process is both time-intensive and energy-intensive, inevitably affecting the user's demand for application efficiency. Layer freezing, an efficient model training technique, has been proposed to improve training efficiency. Although existing layer freezing methods demonstrate the great potential to reduce model training costs, they still remain shortcomings such as lacking generalizability and compromised accuracy. For instance, existing layer freezing methods either require the freeze configurations to be manually defined before training, which does not apply to different networks, or use heuristic freezing criteria that is hard to guarantee decent accuracy in different scenarios. Therefore, there lacks a generic and smart layer freezing method that can automatically perform ``in-situation'' layer freezing for different networks during training processes. To this end, we propose a generic and efficient training framework (SmartFRZ). The core proposed technique in SmartFRZ is attention-guided layer freezing, which can automatically select the appropriate layers to freeze without compromising accuracy. Experimental results show that SmartFRZ effectively reduces the amount of computation in training and achieves significant training acceleration, and outperforms the state-of-the-art layer freezing approaches.
Optimal-Landmark-Guided Image Blending for Face Morphing Attacks
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we propose a novel approach for conducting face morphing attacks, which utilizes optimal-landmark-guided image blending. Current face morphing attacks can be categorized into landmark-based and generation-based approaches. Landmark-based methods use geometric transformations to warp facial regions according to averaged landmarks but often produce morphed images with poor visual quality. Generation-based methods, which employ generation models to blend multiple face images, can achieve better visual quality but are often unsuccessful in generating morphed images that can effectively evade state-of-the-art face recognition systems~(FRSs). Our proposed method overcomes the limitations of previous approaches by optimizing the morphing landmarks and using Graph Convolutional Networks (GCNs) to combine landmark and appearance features. We model facial landmarks as nodes in a bipartite graph that is fully connected and utilize GCNs to simulate their spatial and structural relationships. The aim is to capture variations in facial shape and enable accurate manipulation of facial appearance features during the warping process, resulting in morphed facial images that are highly realistic and visually faithful. Experiments on two public datasets prove that our method inherits the advantages of previous landmark-based and generation-based methods and generates morphed images with higher quality, posing a more significant threat to state-of-the-art FRSs.
Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models
Venue: arXiv cs.CL 2024
Year: 2024

In the evolving landscape of online communication, moderating hate speech (HS) presents an intricate challenge, compounded by the multimodal nature of digital content. This comprehensive survey delves into the recent strides in HS moderation, spotlighting the burgeoning role of large language models (LLMs) and large multimodal models (LMMs). Our exploration begins with a thorough analysis of current literature, revealing the nuanced interplay between textual, visual, and auditory elements in propagating HS. We uncover a notable trend towards integrating these modalities, primarily due to the complexity and subtlety with which HS is disseminated. A significant emphasis is placed on the advances facilitated by LLMs and LMMs, which have begun to redefine the boundaries of detection and moderation capabilities. We identify existing gaps in research, particularly in the context of underrepresented languages and cultures, and the need for solutions to handle low-resource settings. The survey concludes with a forward-looking perspective, outlining potential avenues for future research, including the exploration of novel AI methodologies, the ethical governance of AI in moderation, and the development of more nuanced, context-aware systems. This comprehensive overview aims to catalyze further research and foster a collaborative effort towards more sophisticated, responsible, and human-centric approaches to HS moderation in the digital era. WARNING: This paper contains offensive examples.
Multi-Group Multicasting Systems Using Multiple RISs
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, practical utilization of multiple distributed reconfigurable intelligent surfaces (RISs), which are able to conduct group-specific operations, for multi-group multicasting systems is investigated. To tackle the inter-group interference issue in the multi-group multicasting systems, the block diagonalization (BD)-based beamforming is considered first. Without any inter-group interference after the BD operation, the multiple distributed RISs are operated to maximize the minimum rate for each group. Since the computational complexity of the BD-based beamforming can be too high, a multicasting tailored zero-forcing (MTZF) beamforming technique is proposed to efficiently suppress the inter-group interference, and the novel design for the multiple RISs that makes up for the inevitable loss of MTZF beamforming is also described. Effective closed-form solutions for the loss minimizing RIS operations are obtained with basic linear operations, making the proposed MTZF beamforming-based RIS design highly practical. Numerical results show that the BD-based approach has ability to achieve high sum-rate, but it is useful only when the base station deploys large antenna arrays. Even with the small number of antennas, the MTZF beamforming-based approach outperforms the other schemes in terms of the sum-rate while the technique requires low computational complexity. The results also prove that the proposed techniques can work with the minimum rate requirement for each group.
Secure ISAC MIMO Systems: Exploiting Interference With Bayesian Cramér-Rao Bound Optimization
Venue: arXiv cs.IT 2024
Year: 2024

In this paper, we present a signaling design for secure integrated sensing and communication (ISAC) systems comprising a dual-functional multi-input multi-output (MIMO) base station (BS) that simultaneously communicates with multiple users while detecting targets present in their vicinity, which are regarded as potential eavesdroppers. In particular, assuming that the distribution of each parameter to be estimated is known \textit{a priori}, we focus on optimizing the targets' sensing performance. To this end, we derive and minimize the Bayesian Cramér-Rao bound (BCRB), while ensuring certain communication quality of service (QoS) by exploiting constructive interference (CI). The latter scheme enforces that the received signals at the eavesdropping targets fall into the destructive region of the signal constellation, to deteriorate their decoding probability, thus enhancing the ISAC's system physical-layer security (PLS) capability. To tackle the nonconvexity of the formulated problem, a tailored successive convex approximation method is proposed for its efficient solution. Our extensive numerical results verify the effectiveness of the proposed secure ISAC design showing that the proposed algorithm outperforms block-level precoding techniques.
HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function
Venue: arXiv cs.LG 2024
Year: 2024

Support vector regression (SVR) has garnered significant popularity over the past two decades owing to its wide range of applications across various fields. Despite its versatility, SVR encounters challenges when confronted with outliers and noise, primarily due to the use of the $\varepsilon$-insensitive loss function. To address this limitation, SVR with bounded loss functions has emerged as an appealing alternative, offering enhanced generalization performance and robustness. Notably, recent developments focus on designing bounded loss functions with smooth characteristics, facilitating the adoption of gradient-based optimization algorithms. However, it's crucial to highlight that these bounded and smooth loss functions do not possess an insensitive zone. In this paper, we address the aforementioned constraints by introducing a novel symmetric loss function named the HawkEye loss function. It is worth noting that the HawkEye loss function stands out as the first loss function in SVR literature to be bounded, smooth, and simultaneously possess an insensitive zone. Leveraging this breakthrough, we integrate the HawkEye loss function into the least squares framework of SVR and yield a new fast and robust model termed HE-LSSVR. The optimization problem inherent to HE-LSSVR is addressed by harnessing the adaptive moment estimation (Adam) algorithm, known for its adaptive learning rate and efficacy in handling large-scale problems. To our knowledge, this is the first time Adam has been employed to solve an SVR problem. To empirically validate the proposed HE-LSSVR model, we evaluate it on UCI, synthetic, and time series datasets. The experimental outcomes unequivocally reveal the superiority of the HE-LSSVR model both in terms of its remarkable generalization performance and its efficiency in training time.