TLDR: Text Based Last-layer Retraining for Debiasing Image Classifiers

A classifier may depend on incidental features stemming from a strong correlation between the feature and the classification target in the training dataset. Recently, Last Layer Retraining (LLR) with group-balanced datasets is known to be efficient in mitigating the spurious correlation of classifiers. However, the acquisition of group-balanced datasets is costly, which hinders the applicability of the LLR method. In this work, we propose to perform LLR based on text datasets built with large language models for a general image classifier. We demonstrate that text can be a proxy for its corresponding image beyond the image-text joint embedding space, such as CLIP. Based on this, we use generated texts to train the final layer in the embedding space of the arbitrary image classifier. In addition, we propose a method of filtering the generated words to get rid of noisy, imprecise words, which reduces the effort of inspecting each word. We dub these procedures as TLDR (\textbf{T}ext-based \textbf{L}ast layer retraining for \textbf{D}ebiasing image classifie\textbf{R}s) and show our method achieves the performance that is comparable to those of the LLR methods that also utilize group-balanced image dataset for retraining. Furthermore, TLDR outperforms other baselines that involve training the last linear layer without a group annotated dataset.Comment: 19 pages, Under Revie

Towards More Robust Interpretation via Local Gradient Alignment

Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several methods for enhancing the local smoothness of the gradient while training have been proposed for attaining robust feature attributions. However, the lack of considering the normalization of the attributions, which is essential in their visualizations, has been an obstacle to understanding and improving the robustness of feature attribution methods. In this paper, we provide new insights by taking such normalization into account. First, we show that for every non-negative homogeneous neural network, a naive l2-robust criterion for gradients is not normalization invariant, which means that two functions with the same normalized gradient can have different values. Second, we formulate a normalization invariant cosine distance-based criterion and derive its upper bound, which gives insight for why simply minimizing the Hessian norm at the input, as has been done in previous work, is not sufficient for attaining robust feature attribution. Finally, we propose to combine both l2 and cosine distance-based criteria as regularization terms to leverage the advantages of both in aligning the local gradient. As a result, we experimentally show that models trained with our method produce much more robust interpretations on CIFAR-10 and ImageNet-100 without significantly hurting the accuracy, compared to the recent baselines. To the best of our knowledge, this is the first work to verify the robustness of interpretation on a larger-scale dataset beyond CIFAR-10, thanks to the computational efficiency of our method

Pontederia crassipes invasiveness on Jeju island is linked to a decline in water pH and climate change-driven overwintering

Freshwater ecosystems are vulnerable to the invasion of exotic aquatic plant species because of the great likelihood of the introduction of exotic species, and the lack of barriers that block introduced species. Water hyacinth, Pontederia crassipes Mart., is one of the world’s most invasive alien plant species damaging freshwater ecosystems worldwide. Here, we monitored the water hyacinth population on Jeju island, Korea, to assess current invasion risks. Furthermore, we investigated how water hyacinth affects water pH because pH is an important determinant of the distribution of other aquatic plants, and thus a good indicator of aquatic ecosystem health. Water containing water hyacinth had a pH of 5.3, while that with water hyacinth and soil had a pH of 4.8 72 hours after the start of the experiment. Water hyacinth extracts contained shikimic acid, stearic acid, and palmitic acid, which are possible compounds that caused a decline in water pH. Water hyacinth also inhibited the growth of the aquatic plant species, Spirodela polyrhiza and Lemna perpusilla. These results imply that invasion of water hyacinth adversely impacts the abiotic and biotic characteristics of aquatic ecosystems. Moreover, monitoring the water hyacinth population suggests that this invasive aquatic plant overwinters on Jeju island. Therefore, regular monitoring and subsequent control of water hyacinth population can prevent its expansion in the aquatic habitats of Jeju island and the southern region of the Korean peninsula

Hyaluronic Acid-Conjugated Mesoporous Silica Nanoparticles Loaded with Dual Anticancer Agents for Chemophotodynamic Cancer Therapy

Present cancer treatments using chemotherapy are limited owing to both significant side effects to normal cells and high recurrence rates. In this study, we demonstrated cancer cell-targeting nanoparticles that load multiple anticancer agents for both specific treatments to cancer and substantial therapeutic effects. For this purpose, hyaluronic acid (HA) was conjugated to mesoporous silica nanoparticles (MSNs) for specifically targeting cancer cells. Moreover, the prepared HA-MSNs exhibited high drug loading potential and sustained drug release. Compared to bare MSNs, the HA-MSNs were internalized at an approximately three times higher rate in squamous cell carcinoma 7 (SCC7) cells. To enhance the anticancer effects of chemotherapy and photodynamic therapy (PDT), doxorubicin (DOX) and chlorin e6 (Ce6) were loaded in HA-MSNs (DOX/Ce6/HA-MSNs); the product exhibited highly effective cytotoxicity on green fluorescent protein-expressing squamous cell carcinoma 7 (SCC7) compared to the corresponding free drugs and HA-MSNs with DOX or Ce6 alone. This study indicates that the application of DOX/Ce6/HA-MSNs in chemotherapy and PDT exerts significant therapeutic effects against SCC7

A Fully-Integrated 71 nW CMOS Temperature Sensor for Low Power Wireless Sensor Nodes

We propose a fully-integrated temperature sensor for battery-operated, ultra-low power microsystems. Sensor operation is based on temperature independent/dependent current sources that are used with oscillators and counters to generate a digital temperature code. A conventional approach to generate these currents is to drop a temperature sensitive voltage across a resistor. Since a large resistance is required to achieve nWs of power consumption with typical voltage levels (100 s of mV to 1 V), we introduce a new sensing element that outputs only 75 mV to save both power and area. The sensor is implemented in 0.18 mu m CMOS and occupies 0.09 mm(2) while consuming 71 nW. After 2-point calibration, an inaccuracy of +1.5 degrees C/-1.4 degrees C is achieved across 0 degrees C to 100 degrees C. With a conversion time of 30 ms, 0.3 degrees C (rms) resolution is achieved. The sensor does not require any external references and consumes 2.2 nJ per conversion. The sensor is integrated into a wireless sensor node to demonstrate its operation at a system level.X114945sciescopu