45 research outputs found
Variance-Reduced Stochastic Learning by Networked Agents under Random Reshuffling
A new amortized variance-reduced gradient (AVRG) algorithm was developed in
\cite{ying2017convergence}, which has constant storage requirement in
comparison to SAGA and balanced gradient computations in comparison to SVRG.
One key advantage of the AVRG strategy is its amenability to decentralized
implementations. In this work, we show how AVRG can be extended to the network
case where multiple learning agents are assumed to be connected by a graph
topology. In this scenario, each agent observes data that is spatially
distributed and all agents are only allowed to communicate with direct
neighbors. Moreover, the amount of data observed by the individual agents may
differ drastically. For such situations, the balanced gradient computation
property of AVRG becomes a real advantage in reducing idle time caused by
unbalanced local data storage requirements, which is characteristic of other
reduced-variance gradient algorithms. The resulting diffusion-AVRG algorithm is
shown to have linear convergence to the exact solution, and is much more memory
efficient than other alternative algorithms. In addition, we propose a
mini-batch strategy to balance the communication and computation efficiency for
diffusion-AVRG. When a proper batch size is employed, it is observed in
simulations that diffusion-AVRG is more computationally efficient than exact
diffusion or EXTRA while maintaining almost the same communication efficiency.Comment: 23 pages, 12 figures, submitted for publicatio
Globally Optimal Beamforming Design for Integrated Sensing and Communication Systems
In this paper, we propose a multi-input multi-output (MIMO) beamforming
transmit optimization model for joint radar sensing and multi-user
communications, where the design of the beamformers is formulated as an
optimization problem whose objective is a weighted combination of the sum rate
and the Cram\'{e}r-Rao bound (CRB), subject to the transmit power budget
constraint. The formulated problem is challenging to obtain a global solution,
because the sum rate maximization (SRM) problem itself (even without
considering the sensing metric) is known to be NP-hard. In this paper, we
propose an efficient global branch-and-bound algorithm for solving the
formulated problem based on the McCormick envelope relaxation and the
semidefinite relaxation (SDR) technique. The proposed algorithm is guaranteed
to find the global solution for the considered problem, and thus serves as an
important benchmark for performance evaluation of the existing local or
suboptimal algorithms for solving the same problem.Comment: 5 pages, 2 figures, submitted for possible publicatio
Diversity Order Analysis for Quantized Constant Envelope Transmission
Quantized constant envelope (QCE) transmission is a popular and effective
technique to reduce the hardware cost and improve the power efficiency of 5G
and beyond systems equipped with large antenna arrays. It has been widely
observed that the number of quantization levels has a substantial impact on the
system performance. This paper aims to quantify the impact of the number of
quantization levels on the system performance. Specifically, we consider a
downlink single-user multiple-input-single-output (MISO) system with M-phase
shift keying (PSK) constellation under the Rayleigh fading channel. We first
derive a novel bound on the system symbol error probability (SEP). Based on the
derived SEP bound, we characterize the achievable diversity order of the
quantized matched filter (MF) precoding strategy. Our results show that full
diversity order can be achieved when the number of quantization levels L is
greater than the PSK constellation order M, i.e., L>M, only half diversity
order is achievable when L=M, and the achievable diversity order is 0 when L<M.
Simulation results verify our theoretical analysis.Comment: 9 pages, 3 figures, submitted for possible publicatio
Connecting Multi-modal Contrastive Representations
Multi-modal Contrastive Representation learning aims to encode different
modalities into a semantically aligned shared space. This paradigm shows
remarkable generalization ability on numerous downstream tasks across various
modalities. However, the reliance on massive high-quality data pairs limits its
further development on more modalities. This paper proposes a novel
training-efficient method for learning MCR without paired data called
Connecting Multi-modal Contrastive Representations (C-MCR). Specifically, given
two existing MCRs pre-trained on (A, B) and (B, C) modality pairs, we project
them to a new space and use the data from the overlapping modality B to
aligning the two MCRs in the new space. Meanwhile, since the modality pairs (A,
B) and (B, C) are already aligned within each MCR, the connection learned by
overlapping modality can also be transferred to non-overlapping modality pair
(A, C). To unleash the potential of C-MCR, we further introduce a
semantic-enhanced inter- and intra-MCR connection method. We first enhance the
semantic consistency and completion of embeddings across different modalities
for more robust alignment. Then we utilize the inter-MCR alignment to establish
the connection, and employ the intra-MCR alignment to better maintain the
connection for inputs from non-overlapping modalities. To demonstrate the
effectiveness of C-MCR, we connect CLIP and CLAP via texts to derive
audio-visual representations, and integrate CLIP and ULIP via images for
3D-language representations. Remarkably, without using any paired data, C-MCR
for audio-visual achieves state-of-the-art performance on audio-image
retrieval, audio-visual source localization, and counterfactual audio-image
recognition tasks. Furthermore, C-MCR for 3D-language also attains advanced
zero-shot 3D point cloud classification accuracy on ModelNet40.Comment: NeurIPS 202
Non-Intrusive Adaptation: Input-Centric Parameter-efficient Fine-Tuning for Versatile Multimodal Modeling
Large language models (LLMs) and vision language models (VLMs) demonstrate
excellent performance on a wide range of tasks by scaling up parameter counts
from O(10^9) to O(10^{12}) levels and further beyond. These large scales make
it impossible to adapt and deploy fully specialized models given a task of
interest. Parameter-efficient fine-tuning (PEFT) emerges as a promising
direction to tackle the adaptation and serving challenges for such large
models. We categorize PEFT techniques into two types: intrusive and
non-intrusive. Intrusive PEFT techniques directly change a model's internal
architecture. Though more flexible, they introduce significant complexities for
training and serving. Non-intrusive PEFT techniques leave the internal
architecture unchanged and only adapt model-external parameters, such as
embeddings for input. In this work, we describe AdaLink as a non-intrusive PEFT
technique that achieves competitive performance compared to SoTA intrusive PEFT
(LoRA) and full model fine-tuning (FT) on various tasks. We evaluate using both
text-only and multimodal tasks, with experiments that account for both
parameter-count scaling and training regime (with and without instruction
tuning)
Effect of the Strength of Initial Aluminium on the Bonding Properties and Deformation Coordination of Ti/Al Composite Sheets by the Cold Roll Bonding Process
Ti/Al composite sheets were prepared using the cold rolling process, and different initial aluminium strengths were considered. The results showed that the peel strength of the Ti/Al composite sheet increased with the increasing initial strength of aluminium under the same reduction. A higher strength of the initial aluminium corresponds to better deformation coordination between titanium and aluminium, where the strain hardening of titanium and aluminium plays an important role. The change degree of the components of twins on the titanium side for the Ti/Al composite sheet with a low aluminium strength is stronger than that for the Ti/Al composite sheet with a high aluminium strength. The strong change in the components of twins may result in the low uniformity of the microstructure on the titanium side. The analysis of the peeling surface shows aluminium residue on the titanium side, while there was almost no titanium residue on the aluminium side. At the same reduction, a higher strength of aluminium corresponds to less aluminium residue on the titanium side. The bonding properties of Ti/Al cold-rolled composite sheets were determined by four strong bonding areas. The strength of the initial aluminium was the main factor, and the residual amount of aluminium on the titanium side of the peeling surfaces was a secondary factor