Learned Visual Features to Textual Explanations

Interpreting the learned features of vision models has posed a longstanding challenge in the field of machine learning. To address this issue, we propose a novel method that leverages the capabilities of large language models (LLMs) to interpret the learned features of pre-trained image classifiers. Our method, called TExplain, tackles this task by training a neural network to establish a connection between the feature space of image classifiers and LLMs. Then, during inference, our approach generates a vast number of sentences to explain the features learned by the classifier for a given image. These sentences are then used to extract the most frequent words, providing a comprehensive understanding of the learned features and patterns within the classifier. Our method, for the first time, utilizes these frequent words corresponding to a visual representation to provide insights into the decision-making process of the independently trained classifier, enabling the detection of spurious correlations, biases, and a deeper comprehension of its behavior. To validate the effectiveness of our approach, we conduct experiments on diverse datasets, including ImageNet-9L and Waterbirds. The results demonstrate the potential of our method to enhance the interpretability and robustness of image classifiers

Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly

Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of dealing with arbitrary 3D shapes and the highly constrained motion required for real-world assemblies. In this work, we propose a novel method to efficiently plan physically plausible assembly motion and sequences for real-world assemblies. Our method leverages the assembly-by-disassembly principle and physics-based simulation to efficiently explore a reduced search space. To evaluate the generality of our method, we define a large-scale dataset consisting of thousands of physically valid industrial assemblies with a variety of assembly motions required. Our experiments on this new benchmark demonstrate we achieve a state-of-the-art success rate and the highest computational efficiency compared to other baseline algorithms. Our method also generalizes to rotational assemblies (e.g., screws and puzzles) and solves 80-part assemblies within several minutes.Comment: Accepted by SIGGRAPH Asia 2022. Project website: http://assembly.csail.mit.edu

Myocardial Perfusion Pressure: A Predictor of 24Hour Survival During Prolonged Cardiac Arrest in Dogs

Myocardial perfusion pressure, defined as the aortic diastolic pressure minus the right atria1 diastolic pressure, correlates with coronary blood flow during cardiopulmonary resuscitation (CPR) and predicts initial resuscitation success. Whether this hemodynamic parameter can predict 24-h survival is not known. We examined the relationship between myocardial perfusion pressure and 24-h survival in 60 dogs that underwent prolonged (20 min) ventricular fibrillation and CPR. Forty-two (70%) animals were initially resuscitated and 20 (33%) survived for 24 h. Myocardial perfusion pressure was significantly greater when measured at 5, 10, 15 and 20 min of ventricular fibrillation in the resuscitated animals than in the non-resuscitated animals (P \u3c 0.01). Likewise, the myocardial perfusion pressure was also greater in the animals that survived 24 h than in animals that were resuscitated, but died before 24 h (P \u3c 0.02). Myocardial perfusion pressure measured after 10 min of CPR was 11 2 mmHg in animals never resuscitated, 20 3 mmHg in those resuscitated that died before 24 h and 29 2 mmHg in those that survived 24 h (P \u3c 0.05). A myocardial perfusion pressure at 10 min of CPR of 20 mmHg or less is an excellent predictor of poor survival (negative predictive value = 96%). Myocardial perfusion pressure is a useful index of CPR effectiveness and therefore may be a useful guide in helping to optimize resuscitation efforts

Changes in Expired End-Tidal Carbon Dioxide During Cardiopulmonary Resuscitation in Dogs: A Prognostic Guide for Resuscitation Efforts

Expired end-tidal carbon dioxide (PCO2) measurements made during cardiopulmonary resuscitation have correlated with cardiac output and coronary perfusion pressure when wide ranges of blood flow are included. The utility of such measurements for predicting resuscitation outcome during the low flow state associated with closed chest cardiopulmonary resuscitation remains uncertain. Expired end-tidal PCO2 and coronary perfusion pressures were measured in 15 mongrel dogs undergoing 15 min of closed chest cardiopulmonary resuscitation after a 3 min period of untreated ventricular fibrillation. In six successfully resuscitated dogs, the mean expired end-tidal PCO2 was significantly higher than that in nine nonresuscitated dogs only after 14 min of cardiopulmonary resuscitation (6.2 ± 1.2 versus 3.4 ± 0.8 mmHg; p \u3c 0.05). No differences in expired end-tidal PCO2 values were found at 2, 7 or 12 min of cardiopulmonary resuscitation. A significant decline in end-tidal PCO2 levels during the resuscitation effort was seen in the nonresuscitated group (from 6.3 ± 0.8 to 3.4 ± 0.8 mmHg; p \u3c 0.05); while the successfully resuscitated group had constant PCO2 levels throughout the 15 min of cardiac arrest (ranging from 6.8 ± 1.1 to 6.2 ± 1.2 mmHg). Changes in expired PCO2 levels during cardiopulmonary resuscitation may be a useful noninvasive predictor of successful resuscitation and survival from cardiac arrest

Next Steps for Human-Centered Generative AI: A Technical Perspective

Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI) from a technical perspective. We contribute a roadmap that lays out future directions of Generative AI spanning three levels: Aligning with human values; Accommodating humans' expression of intents; and Augmenting humans' abilities in a collaborative workflow. This roadmap intends to draw interdisciplinary research teams to a comprehensive list of emergent ideas in HGAI, identifying their interested topics while maintaining a coherent big picture of the future work landscape

Phased Array Feed Calibration, Beamforming and Imaging

Phased array feeds (PAFs) for reflector antennas offer the potential for increased reflector field of view and faster survey speeds. To address some of the development challenges that remain for scientifically useful PAFs, including calibration and beamforming algorithms, sensitivity optimization, and demonstration of wide field of view imaging, we report experimental results from a 19 element room temperature L-band PAF mounted on the Green Bank 20-Meter Telescope. Formed beams achieved an aperture efficiency of 69% and system noise temperature of 66 K. Radio camera images of several sky regions are presented. We investigate the noise performance and sensitivity of the system as a function of elevation angle with statistically optimal beamforming and demonstrate cancelation of radio frequency interference sources with adaptive spatial filtering.Comment: 19 pages, 13 figure

Structural Analysis and Development of Notum Fragment Screening Hits

The Wnt signaling suppressor Notum is a promising target for osteoporosis, Alzheimer's disease, and colorectal cancers. To develop novel Notum inhibitors, we used an X-ray crystallographic fragment screen with the Diamond-SGC Poised Library (DSPL) and identified 59 fragment hits from the analysis of 768 data sets. Fifty-eight of the hits were found bound at the enzyme catalytic pocket with potencies ranging from 0.5 to >1000 μM. Analysis of the fragments' diverse binding modes, enzymatic inhibitory activities, and chemical properties led to the selection of six hits for optimization, and five of these resulted in improved Notum inhibitory potencies. One hit, 1-phenyl-1,2,3-triazole 7, and its related cluster members, have shown promising lead-like properties. These became the focus of our fragment development activities, resulting in compound 7d with IC50 0.0067 μM. The large number of Notum fragment structures and their initial optimization provided an important basis for further Notum inhibitor development