Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time

We give a polynomial-time algorithm for learning high-dimensional halfspaces with margins in $d$-dimensional space to within desired TV distance when the ambient distribution is an unknown affine transformation of the $d$-fold product of an (unknown) symmetric one-dimensional logconcave distribution, and the halfspace is introduced by deleting at least an $\epsilon$ fraction of the data in one of the component distributions. Notably, our algorithm does not need labels and establishes the unique (and efficient) identifiability of the hidden halfspace under this distributional assumption. The sample and time complexity of the algorithm are polynomial in the dimension and $1/\epsilon$. The algorithm uses only the first two moments of suitable re-weightings of the empirical distribution, which we call contrastive moments; its analysis uses classical facts about generalized Dirichlet polynomials and relies crucially on a new monotonicity property of the moment ratio of truncations of logconcave distributions. Such algorithms, based only on first and second moments were suggested in earlier work, but hitherto eluded rigorous guarantees. Prior work addressed the special case when the underlying distribution is Gaussian via Non-Gaussian Component Analysis. We improve on this by providing polytime guarantees based on Total Variation (TV) distance, in place of existing moment-bound guarantees that can be super-polynomial. Our work is also the first to go beyond Gaussians in this setting.Comment: Preliminary version in NeurIPS 202

Preliminary Functional-Structural Modeling on Poplar (Salicaceae)

Poplar is one of the best fast-growing trees in the world, widely used for windbreak and wood product. Although architecture of poplar has direct impact on its applications, it has not been descried in previous poplar models, probably because of the difficulties raised by measurement, data processing and parameterization. In this paper, the functional-structural model GreenLab is calibrated by using poplar data of 3, 4, 5, 6 years old. The data was acquired by simplifying measurement. The architecture was also simplified by classifying the branches into several types (physiological age) using clustering analysis, which decrease the number of parameters. By multi-fitting the sampled data of each tree, the model parameters were identified and the plant architectures at different tree ages were simulated

Laser facilitates week-long sustained transdermal drug delivery at high doses

Traditional patches are most successful in transdermal delivery of low-dose hydrophobic drugs. Week-long transdermal delivery of high-dose hydrophilic drugs remains a big challenge. This study explored ablative fractional laser (AFL) to assist 3-day to week-long sustained transdermal delivery of powder hydrophilic drugs in murine models. Bulk drug powder was coated into reservoir patches followed by topical application onto AFL-treated skin. Water evaporated from AFL-generated skin microchannels (MCs) gradually dissolve topical drug powder to elicit multi-day sustained drug delivery. Using sulforhodamine b, zidovudine, and bovine serum albumin as model hydrophilic drugs, we found tapped coating could coat 10–20 mg drug per 0.5 cm2 reservoir patch to elicit 3-day sustained delivery, while compression coating could coat ~35–70 mg drug per 0.5 cm2 reservoir patch to elicit week-long sustained delivery. Besides sustained drug delivery, AFL-assisted powder reservoir patch delivery showed a good safety. AFL-generated skin MCs resealed in 1–2 days and completely recovered in 3 days after the week-long sustained delivery. AFL-assisted powder reservoir patch delivery involves no complex powder formulation and only requires incorporation of highly water-soluble mannitol or a similar excipient to elicit the high-efficient delivery. Enlarging reservoir patch size to 10 cm2 can conveniently expand the delivery capacity to gram scale. To our knowledge, this is the first time that high-dose week-long sustained transdermal delivery of hydrophilic drugs was achieved via a simple laser-based powder delivery platform

Improving immunogenicity and safety of flagellin as vaccine carrier by high-density display on virus-like particle surface

Flagellin is a protein-based adjuvant that activates toll-like receptor (TLR) 5. Flagellin has been actively explored as vaccine adjuvants and carriers. Preclinical and clinical studies find flagellin-based vaccines have a risk to induce systemic adverse reactions potentially due to its overt activation of TLR5. To improve safety and immunogenicity of flagellin as vaccine carriers, FljB was displayed at high densities on hepatitis b core (HBc) virus-like particle (VLP) surface upon c/e1 loop insertion. FljB-HBc (FH) VLPs showed significantly reduced ability to activate TLR5 or induce systemic interleukin-6 release as compared to FljB. FH VLPs also failed to significantly increase rectal temperature of mice, while FljB could significantly increase rectal temperature of mice. These data indicated systemic safety of FljB could be significantly improved by high-density display on HBc VLP surface. Besides improved safety, FH VLPs and FljB similarly boosted co-administered ovalbumin immunization and FH VLPs were found to induce two-fold higher anti-FljB antibody titer than FljB. These data indicated preserved adjuvant potency and improved immunogenicity after high-density display of FljB on HBc VLP surface. Consistent with the high immunogenicity, FH VLPs were found to be more efficiently taken up by bone marrow-derived dendritic cells and stimulate more potent dendritic cell maturation than FljB. Lastly, FH VLPs were found to be a more immunogenic carrier than FljB, HBc VLPs, or the widely used keyhole limpet hemocyanin for nicotine vaccine development with a good local and systemic safety. Our data support FH VLPs to be a potentially safer and more immunogenic carrier than FljB for vaccine development

Vaccine delivery alerts innate immune systems for more immunogenic vaccination

Vaccine delivery technologies are mainly designed to minimally invasively deliver vaccines to target tissues with little or no adjuvant effects. This study presents a prototype laser-based powder delivery (LPD) with inherent adjuvant effects for more immunogenic vaccination without incorporation of external adjuvants. LPD takes advantage of aesthetic ablative fractional laser to generate skin microchannels to support high-efficient vaccine delivery and at the same time creates photothermal stress in microchannel-surrounding tissues to boost vaccination. LPD could significantly enhance pandemic influenza 2009 H1N1 vaccine immunogenicity and protective efficacy as compared with needle-based intradermal delivery in murine models. The ablative fractional laser was found to induce host DNA release, activate NLR family pyrin domain containing 3 inflammasome, and stimulate IL-1β release despite their dispensability for laser adjuvant effects. Instead, the ablative fractional laser activated MyD88 to mediate its adjuvant effects by potentiation of antigen uptake, maturation, and migration of dendritic cells. LPD also induced minimal local or systemic adverse reactions due to the microfractional and sustained vaccine delivery. Our data support the development of self-adjuvanted vaccine delivery technologies by intentional induction of well-controlled tissue stress to alert innate immune systems for more immunogenic vaccination

Graph-Level Embedding for Time-Evolving Graphs

Graph representation learning (also known as network embedding) has been extensively researched with varying levels of granularity, ranging from nodes to graphs. While most prior work in this area focuses on node-level representation, limited research has been conducted on graph-level embedding, particularly for dynamic or temporal networks. However, learning low-dimensional graph-level representations for dynamic networks is critical for various downstream graph retrieval tasks such as temporal graph similarity ranking, temporal graph isomorphism, and anomaly detection. In this paper, we present a novel method for temporal graph-level embedding that addresses this gap. Our approach involves constructing a multilayer graph and using a modified random walk with temporal backtracking to generate temporal contexts for the graph's nodes. We then train a "document-level" language model on these contexts to generate graph-level embeddings. We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods. Our experimental results demonstrate the effectiveness of our method in generating graph-level embeddings for dynamic networks.Comment: In Companion Proceedings of the ACM Web Conference 202

Sustained epidermal powder drug delivery via skin microchannels

Transdermal delivery of hydrophilic drugs is challenging. This study presents a novel sustained epidermal powder delivery technology (sEPD) for safe, efficient, and sustained delivery of hydrophilic drugs across the skin. sEPD is based on coating powder drugs into high-aspect-ratio, micro-coating channels (MCCs) followed by topical application of powder drug-coated array patches onto ablative fractional laser-generated skin MCs to deliver drugs into the skin. We found sEPD could efficiently deliver chemical drugs without excipients and biologics drugs in the presence of sugar excipients into the skin with a duration of ~ 12 h. Interestingly the sEPD significantly improved zidovudine bioavailability by ~ 100% as compared to oral gavage delivery. sEPD of insulin was found to maintain blood glucose levels in normal range for at least 6 h in chemical-induced diabetes mice, while subcutaneous injection failed to maintain blood glucose levels in normal range. sEPD of anti-programmed death-1 antibody showed more potent anti-tumor efficacy than intraperitoneal injection in B16F10 melanoma models. Tiny skin MCs and ‘bulk’ drug powder inside relatively deep MCCs are crucial to induce the sustained drug release. The improved bioavailability and functionality warrants further development of the novel sEPD for clinical use

Personalized local heating neutralizing individual, spatial and temporal thermo-physiological variances in extreme cold environments

In this paper, we investigate the feasibility, robustness and optimization of introducing personal comfort systems (PCS), apparatuses that promises in energy saving and comfort improvement, into a broader range of environments. We report a series of laboratory experiments systematically examining the effect of personalized heating in neutralizing individual, spatial and temporal variations of thermal demands. The experiments were conducted in an artificial climate chamber at -15 degC in order to simulate extreme cold environments. We developed a heating garment with 20 pieces of 20 * 20 cm2 heating cloth (grouped into 9 regions) comprehensively covering human body. Surface temperatures of the garment can be controlled independently, quickly (within 20 seconds), precisely (within 1 degC) and easily (through a tablet) up to 45 degC. Participants were instructed to adjust surface temperatures of each segment to their preferences, with their physiological, psychological and adjustment data collected. We found that active heating could significantly and stably improve thermal satisfaction. The overall TSV and TCV were improved 1.50 and 1.53 during the self-adjustment phase. Preferred heating surface temperatures for different segments varied widely. Further, even for the same segment, individual differences among participants were considerable. Such variances were observed through local heating powers, while unnoticeable among thermal perception votes. In other words, all these various differences could be neutralized given the flexibility in personalized adjustments. Our research reaffirms the paradigm of "adaptive thermal comfort" and will promote innovations on human-centric design for more efficient PCSs

Learning-Augmented B-Trees

We study learning-augmented binary search trees (BSTs) and B-Trees via Treaps with composite priorities. The result is a simple search tree where the depth of each item is determined by its predicted weight $w_x$. To achieve the result, each item $x$ has its composite priority $-\lfloor\log\log(1/w_x)\rfloor + U(0, 1)$ where $U(0, 1)$ is the uniform random variable. This generalizes the recent learning-augmented BSTs [Lin-Luo-Woodruff ICML`22], which only work for Zipfian distributions, to arbitrary inputs and predictions. It also gives the first B-Tree data structure that can provably take advantage of localities in the access sequence via online self-reorganization. The data structure is robust to prediction errors and handles insertions, deletions, as well as prediction updates.Comment: 25 page