Debiased Regression Adjustment in Completely Randomized Experiments with Moderately High-dimensional Covariates

Completely randomized experiment is the gold standard for causal inference. When the covariate information for each experimental candidate is available, one typical way is to include them in covariate adjustments for more accurate treatment effect estimation. In this paper, we investigate this problem under the randomization-based framework, i.e., that the covariates and potential outcomes of all experimental candidates are assumed as deterministic quantities and the randomness comes solely from the treatment assignment mechanism. Under this framework, to achieve asymptotically valid inference, existing estimators usually require either (i) that the dimension of covariates $p$ grows at a rate no faster than $O(n^{2 / 3})$ as sample size $n \to \infty$; or (ii) certain sparsity constraints on the linear representations of potential outcomes constructed via possibly high-dimensional covariates. In this paper, we consider the moderately high-dimensional regime where $p$ is allowed to be in the same order of magnitude as $n$. We develop a novel debiased estimator with a corresponding inference procedure and establish its asymptotic normality under mild assumptions. Our estimator is model-free and does not require any sparsity constraint on potential outcome's linear representations. We also discuss its asymptotic efficiency improvements over the unadjusted treatment effect estimator under different dimensionality constraints. Numerical analysis confirms that compared to other regression adjustment based treatment effect estimators, our debiased estimator performs well in moderately high dimensions

Maximum saliency bias in binocular fusion

Subjective experience at any instant consists of a single (“unitary”), coherent interpretation of sense data rather than a “Bayesian blur” of alternatives. However, computation of Bayes-optimal actions has no role for unitary perception, instead being required to integrate over every possible action-percept pair to maximise expected utility. So what is the role of unitary coherent percepts, and how are they computed? Recent work provided objective evidence for non-Bayes-optimal, unitary coherent, perception and action in humans; and further suggested that the percept selected is not the maximum a posteriori percept but is instead affected by utility. The present study uses a binocular fusion task first to reproduce the same effect in a new domain, and second, to test multiple hypotheses about exactly how utility may affect the percept. After accounting for high experimental noise, it finds that both Bayes optimality (maximise expected utility) and the previously proposed maximum-utility hypothesis are outperformed in fitting the data by a modified maximum-salience hypothesis, using unsigned utility magnitudes in place of signed utilities in the bias function

Study of Vertical Ga\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e3\u3c/sub\u3e FinFET Short Circuit Ruggedness using Robust TCAD Simulation

In this paper, the short circuit ruggedness of Gallium Oxide (Ga2O3) vertical FinFET is studied using Technology Computer-Aided-Design (TCAD) simulations. Ga2O3 is an emerging ultra-wide bandgap material and Ga2O3 vertical FinFET can achieve the normally-off operation for high voltage applications. Ga2O3 has a relatively low thermal conductivity and, thus, it is critical to explore the design space of Ga2O3 vertical FinFETs to achieve an acceptable short-circuit capability for power applications. In this study, appropriate TCAD models and parameters calibrated to experimental data are used. For the first time, the breakdown voltage simulation accuracy of Ga2O3 vertical FinFETs is studied systematically. It is found that a background carrier generation rate between 105 cm−3s−1 and 1012 cm−3s−1 is required in simulation to obtain correct results. The calibrated and robust setup is then used to study the short circuit withstand time (SCWT) of an 800 V-rated Ga2O3 vertical FinFET with different inter-fin architectures. It is found that, due to the high thermal resistance in Ga2O3, to achieve an SCWT \u3e1 μs, low gate overdrive is needed which increases Ron,sp by 66% and that Ga2O3 might melt before the occurrence of thermal runaway. These results provide important guidance for developing rugged Ga2O3 power transistors

TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation

Multi-spectral object Re-identification (ReID) aims to retrieve specific objects by leveraging complementary information from different image spectra. It delivers great advantages over traditional single-spectral ReID in complex visual environment. However, the significant distribution gap among different image spectra poses great challenges for effective multi-spectral feature representations. In addition, most of current Transformer-based ReID methods only utilize the global feature of class tokens to achieve the holistic retrieval, ignoring the local discriminative ones. To address the above issues, we step further to utilize all the tokens of Transformers and propose a cyclic token permutation framework for multi-spectral object ReID, dubbled TOP-ReID. More specifically, we first deploy a multi-stream deep network based on vision Transformers to preserve distinct information from different image spectra. Then, we propose a Token Permutation Module (TPM) for cyclic multi-spectral feature aggregation. It not only facilitates the spatial feature alignment across different image spectra, but also allows the class token of each spectrum to perceive the local details of other spectra. Meanwhile, we propose a Complementary Reconstruction Module (CRM), which introduces dense token-level reconstruction constraints to reduce the distribution gap across different image spectra. With the above modules, our proposed framework can generate more discriminative multi-spectral features for robust object ReID. Extensive experiments on three ReID benchmarks (i.e., RGBNT201, RGBNT100 and MSVR310) verify the effectiveness of our methods. The code is available at https://github.com/924973292/TOP-ReID.Comment: This work is accepted by AAAI202

Visualizing drug-induced lipid accumulation in lysosomes of live cancer cells with stimulated Raman imaging

The low pH of the lysosomal compartment often results in sequestration of chemotherapeutic agents that contain positively charged basic functional groups, leading to anti-cancer drug resistance. To visualize drug localization in lysosomes and its influence on lysosomal functions, we synthesize a group of drug-like compounds that contain both a basic functional group and a bisarylbutadiyne (BADY) group as a Raman probe. With quantitative stimulated Raman scattering (SRS) imaging, we validate that the synthesized lysosomotropic (LT) drug analogs show high lysosomal affinity, which can also serve as a photostable lysosome tracker. We find that long-term retention of the LT compounds in lysosomes leads to the increased amount and colocalization of both lipid droplets (LDs) and lysosomes in SKOV3 cells. With hyperspectral SRS imaging, further studies find that the LDs stuck in lysosomes are more saturated than the LDs staying out of the lysosomes, indicating impaired lysosomal lipid metabolism by the LT compounds. These results demonstrate that SRS imaging of the alkyne-based probes is a promising approach to characterizing the lysosomal sequestration of drugs and its influence on cell functions