233 research outputs found
Day-Ahead Congestion Management in Distribution Systems through Household Demand Response and Distribution Congestion Prices
CALD : surviving various application-layer DDoS attacks that mimic flash crowd
Distributed denial of service (DDoS) attack is a continuous critical threat to the Internet. Derived from the low layers, new application-layer-based DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. The case may be more serious when suchattacks mimic or occur during the flash crowd event of a popular Website. In this paper, we present the design and implementation of CALD, an architectural extension to protect Web servers against various DDoS attacks that masquerade as flash crowds. CALD provides real-time detection using mess tests but is different from other systems that use resembling methods. First, CALD uses a front-end sensor to monitor thetraffic that may contain various DDoS attacks or flash crowds. Intense pulse in the traffic means possible existence of anomalies because this is the basic property of DDoS attacks and flash crowds. Once abnormal traffic is identified, the sensor sends ATTENTION signal to activate the attack detection module. Second, CALD dynamically records the average frequency of each source IP and check the total mess extent. Theoretically, the mess extent of DDoS attacks is larger than the one of flash crowds. Thus, with some parameters from the attack detection module, the filter is capable of letting the legitimate requests through but the attack traffic stopped. Third, CALD may divide the security modules away from the Web servers. As a result, it keeps maximum performance on the kernel web services, regardless of the harassment from DDoS. In the experiments, the records from www.sina.com and www.taobao.com have proved the value of CALD
Continuous particle manipulation and separation in a hurdle-combined curved microchannel using DC dielectrophoresis
This paper presents a novel dielectrophoresis (DEP)-based microfluidic device which combines round hurdle with an S-shaped curved microchannel for continuous manipulation and separation of microparticles. Local nonuniform electric fields are generated by means of both constricted gaps and curved sections having equal width. Under the effect of negative DEP, particles transporting throughout the microchannel electrokinetically will be directed away from either inner wall or hurdle edge. Both experiment and numerical simulation were conducted, the results of which showed that the trajectories of fix-sized (i.e. 10 or 15 μm) polystyrene (PS) particles could be controlled by adjusting applied voltage, and continuous size-based separation of 10 and 15 μm particles was achieved. Compared to other microchannel designs that make use of either obstacle or curvature individually for electric field gradient, the developed microchannel offers advantages such as improved controllability over particle motion, lower requirement of applied voltage, reduced fouling and particle adhesion, etc. © 2013 AIP Publishing LLC
A Market Mechanism for Participation of Electric Vehicles and Disptachable Loads in Distribution System Congestion Management
Retrieval-Augmented Multimodal Language Modeling
Recent multimodal models such as DALL-E and CM3 have achieved remarkable
progress in text-to-image and image-to-text generation. However, these models
store all learned knowledge (e.g., the appearance of the Eiffel Tower) in the
model parameters, requiring increasingly larger models and training data to
capture more knowledge. To integrate knowledge in a more scalable and modular
way, we propose a retrieval-augmented multimodal model, which enables a base
multimodal model (generator) to refer to relevant knowledge fetched by a
retriever from external memory (e.g., multimodal documents on the web).
Specifically, we implement a retriever using the pretrained CLIP model and a
generator using the CM3 Transformer architecture, and train this model using
the LAION dataset. Our resulting model, named Retrieval-Augmented CM3 (RA-CM3),
is the first multimodal model that can retrieve and generate mixtures of text
and images. We show that RA-CM3 significantly outperforms baseline multimodal
models such as DALL-E and CM3 on both image and caption generation tasks (12
FID and 17 CIDEr improvements on MS-COCO), while requiring much less compute
for training (<30% of DALL-E). Moreover, we show that RA-CM3 exhibits novel
capabilities such as knowledge-intensive image generation and multimodal
in-context learning
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
We introduce INSTRUCTOR, a new method for computing text embeddings given
task instructions: every text input is embedded together with instructions
explaining the use case (e.g., task and domain descriptions). Unlike encoders
from prior work that are more specialized, INSTRUCTOR is a single embedder that
can generate text embeddings tailored to different downstream tasks and
domains, without any further training. We first annotate instructions for 330
diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive
loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are
unseen during training), ranging from classification and information retrieval
to semantic textual similarity and text generation evaluation. INSTRUCTOR,
while having an order of magnitude fewer parameters than the previous best
model, achieves state-of-the-art performance, with an average improvement of
3.4% compared to the previous best results on the 70 diverse datasets. Our
analysis suggests that INSTRUCTOR is robust to changes in instructions, and
that instruction finetuning mitigates the challenge of training a single model
on diverse datasets. Our model, code, and data are available at
https://instructor-embedding.github.io.Comment: Accepted in ACL2023 Finding
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