In wireless communication systems, the intended receiver is able to obtain the perfect channel state information (CSI) as long as the training sequence is sufficiently long, and then through a quantized feedback channel (QFC), the transmitter gets imperfect CSI caused by the quantized noise. In general, the design of efficient coding schemes for wireless channels with imperfect CSI at the transmitter (I-CSIT) is difficult and challenging, and one possible method is to construct such a scheme by using channel feedback. In the literature, it has already been shown that the classical Schalkwijk-Kailath (SK) scheme for the additive white Gaussian noise (AWGN) channel with noiseless feedback is a highly efficient coding scheme since its coding complexity is extremely low and the decoding error doubly exponentially decays as the coding blocklength tends to infinity. Existing SK-type schemes mainly focus on the case that perfect CSI is known at the transceiver, and adopt modulo lattice function to eliminate the impact of feedback channel noise (often considered as AWGN) on the performance of SK-type schemes. However, for channels with I-CSIT and QFC, this does not work since the distribution of quantized noise is unknown and I-CSIT brings additional non-convergence estimation error to the transceiver, leading to the application of SK-type schemes to practical wireless scenarios becomes challenging. In this paper, first, for the quasi-static fading channel with I-CSIT and QFC, we design a modulo lattice based SK-type scheme where the receiver adopts an auxiliary signal to decode the message, and numerical results show that the rate of our scheme almost approaches that of the same model with perfect CSI at the transceiver and noise-free feedback for some cases. Next, we further extend the above scheme to the two-path fading scenario, which is modeled as the two-ray channel with I-CSIT and QFC. Treating the signal of the second path as a relay, our extended scheme combines the amplify-and-forward relay strategy and the previously proposed scheme for the same model without ISI. However, this extended scheme cannot be applied to multi-path fading scenario, to this end, we propose a new SK-type scheme for the multi-path fading case. The intuition behind this scheme is to transform the multi-path fading channel into a fading MIMO channel by discrete fourier transform (DFT), and then apply the SK-type scheme to the MIMO channel in frequency domain and use inverse DFT to obtain the codeword in time domain. The study of this paper may provide a way to design efficient coding scheme for fading channels in the presence of imperfect CSI and quantized feedback.

Advancements in large Vision-Language Models have brought precise, accurate image captioning, vital for advancing multi-modal image understanding and processing. Yet these captions often carry lengthy, intertwined contexts that are difficult to parse and frequently overlook essential cues, posing a great barrier for models like GroundingDINO and SDXL, which lack the strong text encoding and syntax analysis needed to fully leverage dense captions. To address this, we propose BACON, a prompting method that breaks down VLM-generated captions into disentangled, structured elements such as objects, relationships, styles, and themes. This approach not only minimizes confusion from handling complex contexts but also allows for efficient transfer into a JSON dictionary, enabling models without linguistic processing capabilities to easily access key information. We annotated 100,000 image-caption pairs using BACON with GPT-4V and trained an LLaVA captioner on this dataset, enabling it to produce BACON-style captions without relying on costly GPT-4V. Evaluations of overall quality, precision, and recall—as well as user studies—demonstrate that the resulting caption model consistently outperforms other SOTA VLM models in generating high-quality captions. Besides, we show that BACON-style captions exhibit better clarity when applied to various models, enabling them to accomplish previously unattainable tasks or surpass existing SOTA solutions without training. For example, BACON-style captions help GroundingDINO achieve 1.51× higher recall scores on open-vocabulary object detection tasks compared to leading methods.

Text-based diffusion models have made significant breakthroughs in generating high-quality images and videos from textual descriptions. However, the lengthy sampling time of the denoising process remains a significant bottleneck in practical applications. Previous methods either ignore the statistical relationships between adjacent steps or rely on attention or feature similarity between them, which often only works with specific network structures. To address this issue, we discover a new statistical relationship in the transition operator between adjacent steps, focusing on the relationship of the outputs from the network. This relationship does not impose any requirements on the network structure. Based on this observation, we propose a novel training-free acceleration method called LTC-Accel, which uses the identified relationship to estimate the current transition operator based on adjacent steps. Due to no specific assumptions regarding the network structure, LTC-Accel is applicable to almost all diffusion-based methods and orthogonal to almost all existing acceleration techniques, making it easy to combine with them. Experimental results demonstrate that LTC-Accel significantly speeds up sampling in text-to-image and text-to-video synthesis while maintaining competitive sample quality. Specifically, LTC-Accel achieves a speedup of 1.67-fold in Stable Diffusion v2 and a speedup of 1.55-fold in video generation models. When combined with distillation models, LTC-Accel achieves a remarkable 10-fold speedup in video generation, allowing real-time generation of more than 16FPS.

In this paper, coding for the covert computation over Gaussian multiple-access channel (GMAC) with feedback is investigated, where a receiver wishes to decode a function of the sources transmitted over the GMAC while ensuring a low probability of detection by a warden. Traditionally, in covert communication, the feedback from the receiver to the transmitters helps to share a secret key which is used to confuse the warden. This paper shows that a slight modification of the existing Schalkwijk-Kailath (SK) type feedback scheme for computation over GMAC satisfies covert constraint by itself, which indicates that the SK-type feedback coding schemes in the literature can also be viewed as covert computation/communication schemes for channels with feedback.

In the literature, it has been shown that feedback does not increase the optimal rate-distortion region of the dirty paper channel with state estimation at the receiver (SE-R). On the other hand, it is well-known that feedback helps to construct low-complexity coding schemes in Gaussian channels, such as the elegant Schalkwijk-Kailath (SK) feedback scheme. This motivates us to explore capacity-achieving SK-type schemes in dirty paper channels with SE-R and feedback. In this paper, we first propose a capacity-achieving feedback scheme for the dirty paper channel with SE-R (DPC-SE-R), which combines the superposition coding and the classical SK-type scheme. Then, we extend this scheme to the dirty paper multiple-access channel with SE-R and feedback, and also show the extended scheme is capacity-achieving. Finally, we discuss how to extend our scheme to a noisy state observation case of the DPC-SE-R. However, the capacity-achieving SK-type scheme for such a case remains unknown.

The classical Schalkwijk-Kailath (SK) scheme for the additive Gaussian noise channel with noiseless feedback is highly efficient since its coding complexity is extremely low and the decoding error doubly exponentially decays as the coding blocklength tends to infinity. However, its application to the fading channel with imperfect CSI at the transmitter (I-CSIT) is challenging since the SK scheme is sensitive to the CSI. In this paper, we investigate how to design SK-type scheme for the quasi-static fading channel with I-CSIT and quantized feedback. By introducing modulo lattice function and an auxiliary signal into the SK-type encoder-decoder of the transceiver, we show that the decoding error caused by the I-CSIT can be perfectly eliminated, resulting in the success of designing SK-type scheme for such a case. The study of this paper provides a way to design efficient coding scheme for fading channels in the presence of imperfect CSI and quantized feedback.

Image compression for both human and machine vision has become prevailing to accommodate to rising demands for machine-machine and human-machine communications. Scalable human-machine image compression is recently emerging as an efficient alternative to simultaneously achieve high accuracy for machine vision in the base layer and obtain high-fidelity reconstruction for human vision in the enhancement layer. However, existing methods achieve scalable coding with heuristic mechanisms, which cannot fully exploit the inter-layer correlations and evidently sacrifice rate-distortion performance. In this paper, we propose task-adapted learnable embedded quantization to address this problem in an analytically optimized fashion. We first reveal the relationship between the latent representations for machine and human vision and demonstrate that optimal representation for machine vision can be approximated with post-training optimization on the learned representation for human vision. On such basis, we propose task-adapted learnable embedded quantization that leverages learnable step predictor to adaptively determine the optimal quantization step for diverse machine vision tasks such that inter-layer correlations between representations for human and machine vision are sufficiently exploited using embedded quantization. Furthermore, we develop a human-machine scalable coding framework by incorporating the proposed embedded quantization into pre-trained learned image compression models. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on machine vision tasks like object detection, instance segmentation, and panoptic segmentation with negligible loss in rate-distortion performance for human vision.

Privacy-preserving federated learning (PPFL) is a promising secure distributed learning paradigm, which enables collaborative training of a global machine learning model through sharing encrypted local models instead of sensitive raw data. PPFL, however, is vulnerable to model poisoning attacks. Most existing Byzantine-robust PPFL solutions typically employ two non-colluding servers to achieve secure model detection and aggregation by executing interactive security protocols, which incur considerable computation and communication overheads. To tackle this issue, we propose an efficient and secure federated learning (ESFL) technique to accelerate the detection of poisonous models in PPFL. First, to improve computational efficiency, we construct a lightweight non-interactive efficient decryption functional encryption (NED-FE) scheme to protect the data privacy of local models. Then, to ensure high communication performance, we elaborately design a non-interactive privacy-preserving robust aggregation strategy, which efficiently detects the blind poisonous models and aggregates benign models. Finally, we implement ESFL and conduct extensive theoretical analysis and experiments. The numerical results demonstrate that ESFL not only achieves the confidentiality and robustness design goals but also maintains high efficiency. Compared with the baseline, ESFL effectively reduces the aggregation latency by up to 88%.

As the adoption of electric vehicles continues to grow, the demand for extensive charging infrastructure in urban areas is concurrently rising. In response to the evolving charging infrastructure shortage, private charging piles have emerged as crucial supplementary energy sources, especially in areas lacking public charging infrastructure. The sharing of private charging piles, however, introduces several challenges. Notably, the variable availability time and extremely limited usage space of private charging piles pose scheduling complexities for charging pile owners. Furthermore, the completely peer-to-peer operation of private charging piles may lead to suboptimal solutions for fulfilling overall charging demand. To comprehensively address these challenges, we explore the potential for cooperation among geographically proximate charging piles. We introduce a novel online booking mechanism paired with specialized scheduling algorithms designed for scenarios involving both multiple private charging piles and single private charging piles. Our objective is to maximize the attained revenue of charging pile owners under fully dynamic conditions on both the supply and demand sides. Through meticulous theoretical proofs, we show that our mechanism achieves advantageous competitive ratios for both scenarios when compared to the offline optimal solutions. Numerous experiments, conducted with real charging sessions, consistently demonstrate that the proposed mechanism achieves the highest revenue, providing substantial evidence for its superior performance.

Diffusion models, which employ stochastic differential equations to sample images through integrals, have emerged as a dominant class of generative models. However, the rationality of the diffusion process itself receives limited attention, leaving the question of whether the problem is well-posed and well-conditioned. In this paper, we uncover a vexing propensity of diffusion models: they frequently exhibit the infinite Lipschitz near the zero point of timesteps. We provide theoretical proofs to illustrate the presence of infinite Lipschitz constants and empirical results to confirm it. The Lipschitz singularities pose a threat to the stability and accuracy during both the training and inference processes of diffusion models. Therefore, the mitigation of Lipschitz singularities holds great potential for enhancing the performance of diffusion models. To address this challenge, we propose a novel approach, dubbed E-TSDM, which alleviates the Lipschitz singularities of the diffusion model near the zero point. Remarkably, our technique yields a substantial improvement in performance. Moreover, as a byproduct of our method, we achieve a dramatic reduction in the Fréchet Inception Distance of acceleration methods relying on network Lipschitz, including DDIM and DPM-Solver, by over 33\%. Extensive experiments on diverse datasets validate our theory and method. Our work may advance the understanding of the general diffusion process, and also provide insights for the design of diffusion models.

The problem of reliably reconstructing a function of sources over a multiple-access channel (MAC) is revisited by considering channel feedback. First, a linear feedback scheme is proposed to compute the average of the Gaussian sources transmitted over the Gaussian MAC. Next, numerical results show that in some cases, our proposed scheme performs better than the existing ones without using channel feedback. Finally, we show that with a slight modification, our scheme can be directly extended to compute any linear function of the Gaussian sources transmitted over the Gaussian MAC.

Wireless communications are often affected by out-age events caused by fading and interference. This paper focuses on investigating the communication throughput and latency in a line-topology, multi-hop network where outages may occur on network links. We focus on three types of intermediate network node schemes: random linear network coding (RLNC), store-and-forward (SF), and hop-by-hop retransmission. The analytical formulas for the maximum throughput and the end-to-end latency are provided for each scheme. To gain a more explicit understanding, we conducted a scalability analysis of the maximum throughput and latency as the network length$L$increases. We observed that the same order of throughput/latency holds across a wide range of outage functions for each scheme. Specifically, the SF scheme achieves at most$\Theta(\frac{1}{L})$throughput, while retransmission and RLNC achieve a constant throughput. However, the retransmission scheme relies on ideal feedback, which is rarely satisfied in practice, whereas RLNC does not. We conducted latency comparisons among various schemes under several constraints regarding the volume of data for transmission.

Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension. We argue that, considering the spatial redundancy in image signals, there is no need to maintain a high dimensionality in the evolution process, especially in the early generation phase. To this end, we make a theoretical generalization of the forward diffusion process via signal decomposition. Concretely, we manage to decompose an image into multiple orthogonal components and control the attenuation of each component when perturbing the image. That way, along with the noise strength increasing, we are able to diminish those inconsequential components and thus use a lower-dimensional signal to represent the source, barely losing information. Such a reformulation allows to vary dimensions in both training and inference of diffusion models. Extensive experiments on a range of datasets suggest that our approach substantially reduces the computational cost and achieves on-par or even better synthesis performance compared to baseline methods. We also show that our strategy facilitates high-resolution image synthesis and improves FID of diffusion model trained on FFHQ at$1024\times 1024$resolution from 52.40 to 10.46. Code is available at https://github.com/damo-vilab/dvdp.

Zhoujun Cheng, Haoyu Dong, Ran Jia, Pengfei Wu, Shi Han, Fan Cheng, Dongmei Zhang. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

Existing auto-regressive pre-trained language models (PLMs) like T5 and BART, have been well applied to table question answering by UNIFIEDSKG and TAPEX, respectively, and demonstrated state-of-the-art results on multiple benchmarks.However, auto-regressive PLMs are challenged by recent emerging numerical reasoning datasets, such as TAT-QA, due to the error-prone implicit calculation.In this paper, we present TACUBE, to precompute aggregation/arithmetic results for the table in advance, so that they are handy and readily available for PLMs to answer numerical reasoning questions.TACUBE systematically and comprehensively covers a collection of computational operations over table segments.By simply concatenating TACUBE to the input sequence of PLMs, it shows significant experimental effectiveness.TACUBE promotes the F1 score from 49.6% to 66.2% on TAT-QA and achieves new state-of-the-art results on WikiTQ (59.6% denotation accuracy).TACUBE 's improvements on numerical reasoning cases are even more notable: on TAT-QA, TACUBE promotes the exact match accuracy of BART-large by 39.6% on sum, 52.5% on average, 36.6% on subtraction and 22.2% on division.We believe that TACUBE is a general and portable pre-computation solution that can be potentially integrated into various numerical reasoning frameworks.Data and code will be available at https://github.com/ microsoft/TaCube.

In this paper, we study a security problem on a simple wiretap network, consisting of a source node S, a destination node D, and an intermediate node R. The intermediate node connects the source and the destination nodes via a set of noiseless parallel channels, with sizes n1and n2, respectively. A message M is to be sent from S to D. The information in the network may be eavesdropped by a set of wiretappers. The wiretappers cannot communicate with one another. Each wiretapper can access a subset of channels, called a wiretap set. All the chosen wiretap sets form a wiretap pattern. A random key K is generated at S, and a coding scheme on (M, K) is employed to protect M. We define two decoding classes at D. In Class-I, only M is required to be recovered, and in Class-II, both M and K are required to be recovered. The objective is to minimize H(K)/H(M) for a given wiretap pattern under the perfect secrecy constraint. The first question we address is whether routing is optimal on this simple network. By enumerating all the wiretap patterns on the Class-I/II (3,3) networks and harnessing the power of Shannon-type inequalities, we find that gaps exist between the bounds implied by routing and the bounds implied by Shannon-type inequalities for a small fraction (c2%) of all the wiretap patterns. The second question we investigate is the following: What is min H(K)/H(M) for the remaining wiretap patterns where gaps exist? We study some simple wiretap patterns and find that their Shannon bounds (i.e., the lower bound induced by Shannon-type inequalities) can be achieved by linear codes, which means routing is not sufficient even for the (3, 3) network. For some complicated wiretap patterns, we study the structures of linear coding schemes under the assumption that they can achieve the corresponding Shannon bounds. This paper indicates that the determination of the entropic region of six linear vector spaces cannot be sidestepped. Some subtle issues on the network models are discussed, and interesting observations are stated.

We prove that by imposing a conditional mutual independence constraint and a marginalisation constraint, the almost entropic region can be completely characterised by Shannon-type information inequalities. Such a property is applied to obtain an explicit lower bound on the generalised Wyner common information.

Let X be an arbitrary continuous random variable and Z be an independent Gaussian random variable with zero mean and unit variance. For t > 0, Costa proved that e2h(X+√t Z)is concave in t, where the proof hinged on the first and second order derivatives of h(X + √t Z). In particular, these two derivatives are signed, i.e., (∂/∂t)h(X + √tZ) ≥ 0 and (∂2/∂t2)h(X + √tZ) ≤ 0. In this paper, we show that the third order derivative of h(X + √tZ) is nonnegative, which implies that the Fisher information J(X + √tZ) is convex in t. We further show that the fourth order derivative of h(X +√tZ) is nonpositive. Following the first four derivatives, we make two conjectures on h(X +√tZ): the first is that (∂n/∂tn)h(X +√tZ) is nonnegative in t if n is odd, and nonpositive otherwise; the second is that log J(X + √tZ) is convex in t. The first conjecture can be rephrased in the context of completely monotone functions: J(X + √tZ) is completely monotone in t. The history of the first conjecture may date back to a problem in mathematical physics studied by McKean in 1966. Apart from these results, we provide a geometrical interpretation to the covariance-preserving transformation and study the concavity of h(√t X +√1 - t Z), revealing its connection with Costa's entropy power inequality.

In a point-to-point communication system, which consists of a sender, a receiver, and a set of noiseless channels, the sender wishes to transmit a private message to the receiver through the channels, which may be eavesdropped by a wiretapper. The set of wiretap sets is arbitrary. The wiretapper can access any one but not more than one wiretap set. From each wiretap set, the wiretapper can obtain some partial information about the private message, which is measured by the equivocation of the message given the symbols obtained by the wiretapper. The security strategy is to encode the message with some random key at the sender. Only the message is required to be recovered at the receiver. Under this setting, we define an achievable rate tuple consisting of the size of the message, the size of the key, and the equivocation for each wiretap set. We first prove a tight rate region when both the message and the key are required to be recovered at the receiver. Then, we extend the result to the general case when only the message is required to be recovered at the receiver. Moreover, we show that even if stochastic encoding is employed at the sender, the message rate cannot be increased.

Consider a communication network represented by a directed graph G = (V, ε), where V is the set of nodes and 8 is the set of point-to-point channels in the network. On the network, a secure message M is transmitted, and there may exist wiretappers who want to obtain information about the message. In secure network coding, we aim to find a network code, which can protect the message against the wiretapper whose power is constrained. Cai and Yeung studied the model in which the wiretapper can access any one but not more than one set of channels, called a wiretap set, out of a collection A of all possible wiretap sets. In order to protect the message, the message needs to be mixed with a random key K. They proved tight fundamental performance bounds when A consists of all subsets of ε of a fixed size r. However, beyond this special case, obtaining such bounds is much more difficult. In this paper, we investigate the problem when A consists of arbitrary subsets of ε and obtain the following results: 1) an upper bound on H(M) and 2) a lower bound on H(K) in terms of H(M). The upper bound on H(M) is explicit, while the lower bound on H(K) can be computed in polynomial time when |A| is fixed. The tightness of the lower bound for the point-to-point communication system is also proved.

In a point-to-point communication system which consists of a sender s, a receiver t and a set of noiseless channels, the senders wants to transmit a private message to the receiver t through the channels which may be eavesdropped by a wiretapper. The wiretapper can access any one but not more than one set of channels, which is referred to as a wiretap set. It is assumed that from each wiretap set, the wiretapper can obtain some partial information about the private message which is measured by the wiretapper's equivocation. The security strategy is to encode the message with some random key. Under these settings, we define an achievable rate tuple in terms of the message, the key and the wiretapper's equivocation, and prove a tight rate region of the rate tuples.