Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention. However, existing methods predominantly rely on memorizing the updated knowledge, impeding LLMs from effectively combining the new knowledge with their inherent knowledge when answering questions. To this end, we propose a Learning to Edit (LTE) framework, focusing on teaching LLMs to apply updated knowledge into input questions, inspired by the philosophy of "Teach a man to fish." LTE features a two-phase process: (i) the Alignment Phase, which fine-tunes LLMs on a meticulously curated parallel dataset to make reliable, in-scope edits while preserving out-of-scope information and linguistic proficiency; and (ii) the Inference Phase, which employs a retrieval-based mechanism for real-time and mass knowledge editing. By comparing our approach with seven advanced baselines across four popular knowledge editing benchmarks and two LLM architectures, we demonstrate LTE's superiority in knowledge editing performance, robustness in both batch and sequential editing, minimal interference on general tasks, and rapid editing speeds. The data and code are available at https://github.com/YJiangcm/LTE.

To ensure a smooth transition from LTE to NR networks while supporting legacy devices and maintaining network The Open Radio Access Network (O-RAN), an industrydriven performance, 3GPP has proposed a comprehensive set of initiative, utilizes intelligent Radio Access Network solutions [3]. These include LTE-compatible NR numerology (RAN) controllers and open interfaces to facilitate efficient with a 15 kHz subcarrier spacing for unified time/frequency spectrum sharing between LTE and NR RANs. Also, solutions include resource reservation, we introduce the Proactive Spectrum Adaptation Scheme and downlink (DL) subcarrier puncturing to support enhanced (ProSAS), a data-driven, O-RAN-compatible spectrum sharing Machine-Type Communication (eMTC) (Technical Report solution. ProSAS is an intelligent radio resource demand (TR) 37.823), and mechanisms for resource allocation within prediction and management scheme for intent-driven spectrum NR carriers for Narrowband-Internet of Things (NB-IoT) (TR management that minimizes surplus or deficit experienced by 37.824). Lastly, to help mitigate and manage interference for both RANs.

Although our method extends the training The focus of this work is on the low-rank adapter (Hu et al., samples required for convergence by 40%, we can fit models 2022, LoRA), a subclass of linear adapters. The linearity that are 3 bigger with roughly half the bandwidth. LoRA is frequently used for finetuning transformers, often resulting in less than Our work explores a new territory in the pre-training 10% of the total trainable parameters (even as low as 0.5%). Although the forward pass incurs an extra computational overhead, the significance of LoRA parameterization pertains ReLoRA (Lialin et al., 2023) sequentially trains and merges to the optimizer memory footprint. However, it AdamW (Kingma & Ba, 2015; Loshchilov & Hutter, 2019) does not yield comparable pre-training performance without typically maintain two states for each parameter, resulting in initial full-parameter training. In contrast, our work uses memory consumption that is twice the size of the trainable parallel updates to match pre-training performance without parameters. FedLoRA focuses on the distributed 2023) achieves further memory savings by storing W in finetuning of LoRA parameters. These works have catalyzed AdaMix (Wang et al., 2022) averages all MLPs in a Mixture the development of several repositories (Wang, 2023; of Experts (MOE) into a single MLP. AdaMix requires Dettmers et al., 2023; Dettmers, 2023; huggingface, 2023), constant synchronization during the forward and backward enabling finetuning of models with billions of parameters passes. Our work requires no synchronization in the forward on low-memory devices. For an in-depth discussion of related works, see Section 5. To understand the conditions required to pre-train a model with LoRA, we first identify a specific scenario where standard Unless stated otherwise, we denote x as a scalar, x a vector, This serves as a guide for developing our algorithm that X a matrix, X a distribution or a set, f() a function and retains the memory efficiency of LoRA. Although low-rank adapters (LoRAs) have proven to be an effective finetuning method, they have apparent limitations 2.1. As evidenced in Figure 2, models parameterized with LoRA demonstrate inferior performance Adapters serve as trainable functions that modify existing compared to models trained using standard optimization. They facilitate parameterefficient This performance gap isn't surprising as it can be attributed finetuning of large-scale models by minimizing the to the inherent rank constraint in LoRA.

Large Language Models (LLMs) have achieved remarkable success with their billion-level parameters, yet they incur high inference overheads. The emergence of activation sparsity in LLMs provides a natural approach to reduce this cost by involving only parts of the parameters for inference. Existing methods only focus on utilizing this naturally formed activation sparsity, overlooking the potential for further amplifying this inherent sparsity. In this paper, we hypothesize that LLMs can learn to be efficient by achieving more structured activation sparsity.To achieve this, we introduce a novel algorithm, Learn-To-be-Efficient (LTE), designed to train efficiency-aware LLMs to learn to activate fewer neurons and achieve a better trade-off between sparsity and performance. Furthermore, unlike SOTA MoEfication methods, which mainly focus on ReLU-based models, LTE can also be applied to LLMs like GPT and LLaMA with soft activation functions. We evaluate LTE on four models and eleven datasets. The experiments show that LTE achieves a better trade-off between sparsity and task performance. For instance, LTE with LLaMA provides a 1.83x-2.59x FLOPs speed-up on language generation tasks, outperforming the state-of-the-art methods.

An important issue in neural computation is the dynamic range of weights in the neural networks. Many experimental results on learning indicate that the weights in the networks can grow prohibitively large with the size of the inputs. Here we address this issue by studying the tradeoffs between the depth and the size of weights in polynomial-size networks of linear threshold elements (LTEs). We show that there is an efficient way of simulating a network of LTEs with large weights by a network of LTEs with small weights. In particular, we prove that every depth-d, polynomial-size network of LTEs with exponentially large integer weights can be simulated by a depth-(2d 1), polynomial-size network of LTEs with polynomially bounded integer weights.

Owlcam 5.0 is the premium dash cam that's ready to move forward from a troubled past. Announced Friday and available for preorder (due to ship March 19), it reboots a promising product that nearly died when its originator, startup Owl Cameras Inc., untidily dissolved about a year ago. Enterprise IoT firm Xirgo Technologies acquired the assets and forged a partnership with yet another company, CallPass, to revive the consumer Owlcam business. After further delays due to supply shortages, Owlcam 5.0 is finally ready to fly. We're seeing more apps attached to the dash cams we review, but the LTE and cloud storage available with Owlcam remain rare.

As several new spectrum bands are opening up for shared use, a new paradigm of \textit{Diverse Band-aware Dynamic Spectrum Access} (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in \textit{multiple bands}, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios.

Since Korea has a limited ICT R&D fund compared to other IT global countries, its strategy was essential to achieve its global competence in each generation of mobile communication. Just after the rollout of the world's first 5G service, the government took the next step by announcing the 5G strategy to promote the 5G application to a wide-ranging industry and create a sustainable 5G ecosystem leading to new growth engines. In this article, we focus on the government-industry 5G collaborations, including the R&D roadmap and promotion to the 5G commercialization, the global collaboration, the first 5G experience, and 5G vertical trials to make the 5G-enabled industrial transformation take place in Korea. The development of an electronic digital switching system called TDX in the 1980s, the world's first CDMA mobile service in the 1990s, and the nationwide wired and mobile broad Internet networks in the 2000s are the key advances that made it possible for Korean consumers to easily adopt new technologies such as LTE and 5G. In 2018, the handset penetration rate of South Korea was similar to western Europe, where LTE adaption was 84% with 99.95% coverage and 65Mbps downlink capacity.4

Look at the top corner of your phone screen. Right now, it probably reads 4G LTE, and you're probably fine with that. But soon--and we're talking years, not months--your phone will say 5G there instead. The mobile industry is buzzing about this next generation of high-speed wireless service, and you can expect the chatter to get even louder at Mobile World Congress in Barcelona next week. The carriers have to upgrade their massive infrastructures, for one.

At CES in January, Double Robotics announced the Double 2, a major upgrade to their super skinny telepresence platform that features better stability and turbo speed. It looked cool, but we didn't get super excited about it, because like most telepresence robots, it's designed to work very well in some very specific, usually business or education-focused environments. We've tested these things out before, and once you get past some hiccups and quirks, they generally do what they're supposed to do, which is provide you with a mobile embodied presence somewhere that you're not. When Double Robotics asked us if we wanted to test out a Double 2, we said sure, with two conditions: 1. it had to come with an LTE cellular data connection, allowing us to use the robot free of Wi-Fi; and 2. we could take it anywhere we wanted. To their credit, the company didn't even hesitate, and they shipped us a brand new Double 2, along with the camera and audio kit accessories and charging dock. Cool, now we can see what this robot can do--and maybe what it can't. Where are we taking it?