Text-to-image generation models are powerful but difficult to use. Users craft specific prompts to get better images, though the images can be repetitive. This paper proposes the Prompt Expansion framework that helps users generate high-quality, diverse images with less effort. The Prompt Expansion model takes a text query as input and outputs a set of expanded text prompts that are optimized such that when passed to a text-to-image model, they generate a wider variety of appealing images. We conduct a human evaluation study that shows that images generated through Prompt Expansion are more aesthetically pleasing and diverse than those generated by baseline methods. Overall, this paper presents a novel and effective approach to improving the text-to-image generation experience.

Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining high performance when applied to domains with differing sensor setups or geographic locations, often resulting in poor localization accuracy due to domain shift. To overcome this challenge, we introduce a novel diffusion-based box refinement approach. This method employs a domain-agnostic diffusion model, conditioned on the LiDAR points surrounding a coarse bounding box, to simultaneously refine the box's location, size, and orientation. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets, object classes and detectors. Our PyTorch implementation is available at https://github.com/cxy1997/DiffuBox.

This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challenging. While existing methods primarily focus on adjusting or updating the final layer of a pre-trained classifier, we explore the untapped potential of enhancing feature representations using unlabeled data at test-time. Our novel method, Online Label Shift adaptation with Online Feature Updates (OLS-OFU), leverages self-supervised learning to refine the feature extraction process, thereby improving the prediction model. By carefully designing the algorithm, theoretically OLS-OFU maintains the similar online regret convergence to the results in the literature while taking the improved features into account. Empirically, it achieves substantial improvements over existing methods, which is as significant as the gains existing methods have over the baseline (i.e., without distribution shift adaptations).

Recent work in black-box adversarial attacks for NLP systems has attracted attention. Prior black-box attacks assume that attackers can observe output labels from target models based on selected inputs. In this work, inspired by adversarial transferability, we propose a new type of black-box NLP adversarial attack that an attacker can choose a similar domain and transfer the adversarial examples to the target domain and cause poor performance in target model. Based on domain adaptation theory, we then propose a defensive strategy, called Learn2Weight, which trains to predict the weight adjustments for target model in order to defense the attack of similar-domain adversarial examples. Using Amazon multi-domain sentiment classification dataset, we empirically show that Learn2Weight model is effective against the attack compared to standard black-box defense methods such as adversarial training and defense distillation. This work contributes to the growing literature on machine learning safety.

Digital harms are widespread in the mobile ecosystem. As these devices gain ever more prominence in our daily lives, so too increases the potential for malicious attacks against individuals. The last line of defense against a range of digital harms – including digital distraction, political polarisation through hate speech, and children being exposed to damaging material – is the user interface. This work introduces GreaseTerminator to enable researchers to develop, deploy, and test interventions against these harms with end-users. We demonstrate the ease of intervention development and deployment, as well as the broad range of harms potentially covered with GreaseTerminator in five in-depth case studies.

BLASTn is a tool universally used by biologists to identify similarities between nucleotide based biological genome sequences. This report describes an hardware implementation designed to accelerate algorithm maintaining the same results yielded by the software developed at NCBI. A detailed profile study identifies the Blast_Nt_Scan function as the computationally intensive part of the algorithm. A hardware component has been designed and implemented for this critical section. Rather then trying to implement more of the computation on the FPGA chip, our focus is on improving workload performance. Hence, the hardware has been designed to be replicated and placed on the FPGA to reduce initial comparison latencies between multiple short sequences(queries) and a subject database. Tests reveal the current implementation achieves an approximate 4X speedup over the software run on a modern general purpose computer generating identical results.

Microfluidics-based biochips consist of microfluidic arrays on rigid substrates through which movement of fluids is tightly controlled to facilitate biological reactions. Biochips are soon expected to revolutionize biosensing, clinical diagnostics, environmental monitoring, and drug discovery. Critical to the deployment of the biochips in such diverse areas is the dependability of these systems. Thus robust testing and diagnosis techniques are required to ensure adequate level of system dependability. Due to the underlying mixed technology and mixed energy domains, such biochips exhibit unique failure mechanisms and defects. In this article efficient parallel testing and diagnosis algorithms are presented that can detect and locate single as well as multiple faults in a microfluidic array without flooding the array, a problem that has hampered realistic implementation of several existing strategies. The fault diagnosis algorithms are well suited for built-in self-test that could drastically reduce the operating cost of microfluidic biochip. Also, the proposed alogirthms can be used both for testing and fault diagnosis during field operation as well as increasing yield during the manufacturing phase of the biochip. Furthermore, these algorithms can be applied to both online and offline testing and diagnosis. Analytical results suggest that these strategies that can be used to design highly dependable biochip systems.

This short paper aims to describe a number of recent developments and report some key performance results. One development is additional functionality added to the custom networking board that was not originally anticipated or reported. The two other developments include Spirit's programming model for computational scientists, and the performance of two compute accelerators (an FPU and e-x) that have been implemented.

Microfluidics-based biochips consist of microfluidic arrays on rigid substrates through which, movement of fluids is tightly controlled to facilitate biological reactions. Biochips are soon expected to revolutionize biosensing, clinical diagnostics, and drug discovery. Critical to the deployment of biochips in such diverse areas is the dependability of these systems. Thus, robust testing techniques are required to ensure an adequate level of system dependability. Due to the underlying mixed technology and energy domains, such biochips exhibit unique failure mechanisms and defects. In this article we present a highly effective fault diagnosis strategy that uses a single source and sink to detect and locate multiple faults in a microfluidic array, without flooding the array, a problem that has hampered realistic implementations of all existing strategies. The strategy renders itself well for a built-in self-test that could drastically reduce the operating cost of microfluidic biochips. It can be used during both the manufacturing phase of the biochip, as well as field operation. Furthermore, the algorithm can pinpoint the actual fault, as opposed to merely the faulty regions that are typically identified by strategies proposed in the literature. Also, analytical results suggest that it is an effective strategy that can be used to design highly dependable biochip systems.