Enhanced Multimodal Representation Learning with Cross-modal KD

This paper explores the tasks of leveraging auxiliary modalities which are only available at training to enhance multimodal representation learning through cross-modal Knowledge Distillation (KD). The widely adopted mutual information maximization-based objective leads to a short-cut solution of the weak teacher, i.e., achieving the maximum mutual information by simply making the teacher model as weak as the student model. To prevent such a weak solution, we introduce an additional objective term, i.e., the mutual information between the teacher and the auxiliary modality model. Besides, to narrow down the information gap between the student and teacher, we further propose to minimize the conditional entropy of the teacher given the student. Novel training schemes based on contrastive learning and adversarial learning are designed to optimize the mutual information and the conditional entropy, respectively. Experimental results on three popular multimodal benchmark datasets have shown that the proposed method outperforms a range of state-of-the-art approaches for video recognition, video retrieval and emotion classification.Comment: Accepted by CVPR202

Redundancy-Adaptive Multimodal Learning for Imperfect Data

Multimodal models trained on complete modality data often exhibit a substantial decrease in performance when faced with imperfect data containing corruptions or missing modalities. To address this robustness challenge, prior methods have explored various approaches from aspects of augmentation, consistency or uncertainty, but these approaches come with associated drawbacks related to data complexity, representation, and learning, potentially diminishing their overall effectiveness. In response to these challenges, this study introduces a novel approach known as the Redundancy-Adaptive Multimodal Learning (RAML). RAML efficiently harnesses information redundancy across multiple modalities to combat the issues posed by imperfect data while remaining compatible with the complete modality. Specifically, RAML achieves redundancy-lossless information extraction through separate unimodal discriminative tasks and enforces a proper norm constraint on each unimodal feature representation. Furthermore, RAML explicitly enhances multimodal fusion by leveraging fine-grained redundancy among unimodal features to learn correspondences between corrupted and untainted information. Extensive experiments on various benchmark datasets under diverse conditions have consistently demonstrated that RAML outperforms state-of-the-art methods by a significant margin

Aqua­(2,9-dimethyl-1,10-phenanthroline-κ2 N,N′)bis­(3-hydroxy­benzoato-κO)manganese(II)–2,9-dimethyl-1,10-phenanthroline–water (1/1/1)

In the title compound, [Mn(C7H5O3)2(C14H12N2)(H2O)]·C14H12N2·H2O, the MnII ion is coordinated by a bidentate 2,9-dimethyl-1,10-phenanthroline (dmphen) ligand, two monodentate 3-hydroxy­benzoate anions (3-HBA) and one water mol­ecule in a distorted trigonal-bipyramidal environment. An uncoordinated dmphen and an uncoordinated water mol­ecule cocrystallized with each complex mol­ecule. Intra- and inter­molecular O—H⋯N and O—H⋯O hydrogen bonds are also present between the coordinated 3-HBA and water mol­ecules and the uncoordinated dmphen and water mol­ecules in the crystal. The packing of the structure is further stabilized by π–π stacking inter­actions involving dmphen mol­ecules, with a centroid–centroid separation of 3.705 (3) Å

Applying DTN Routing for Reservation-Driven EV Charging Management in Smart Cities

Charging management for Electric Vehicles (EVs) on-the-move (moving on the road with certain trip destinations) is becoming important, concerning the increasing popularity of EVs in urban city. However, the limited battery volume of EV certainly influences its driver’s experience. This is mainly because the EV needed for intermediate charging during trip, may experience a long service waiting time at Charging Station (CS). In this paper, we focus on CS-selection decision making to manage EVs’ charging plans, aiming to minimize drivers’ trip duration through intermediate charging at CSs. The anticipated EVs’ charging reservations including their arrival time and expected charging time at CSs, are brought for charging management, in addition to taking the local status of CSs into account. Compared to applying traditionally applying cellular network communication to report EVs’ charging reservations,we alternatively study the feasibility of applying Vehicle-to-Vehicle (V2V) communication with Delay/Disruption Tolerant Networking (DTN) nature, due primarily to its flexibility and cost-efficiency in Vehicular Ad hoc NETworks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V for reservation reporting is promisingly cost-efficient in terms of communication overhead for reservation making, while achieving a comparable performance in terms of charging waiting time and total trip duration

Computer Simulation of PAN/PVP Blends Compatibility and Preparation of Aligned PAN Porous Nanofibers via Magnetic-Field-Assisted Electrospinning PAN/PVP Blends

Binary blend compatibility of polyacrylonitrile (PAN) and polyvinylpyrrolidone (PVP) was computationally simulated at both molecular and mesoscopic levels in order to provide theoretical support for preparing PAN porous nanofibers from PAN/PVP blends. In molecular simulation, Flory-Huggins interaction parameters were calculated to estimate the blend compatibility, in which PAN and PVP were found to be immiscible. This had been further validated by the mesoscopic simulation in terms of the free energy density，the order parameters, and the mesoscopic morphology. Aligned PAN porous nanofibers were prepared by selectively removing PVP from the PAN/PVP blend nanofibers which was prepared by Magnetic-field-assisted electrospinning (MFAES).</p

One-Step Generation of Mice Carrying Reporter and Conditional Alleles by CRISPR/Cas-Mediated Genome Engineering

The type II bacterial CRISPR/Cas system is a novel genome-engineering technology with the ease of multiplexed gene targeting. Here, we created reporter and conditional mutant mice by coinjection of zygotes with Cas9 mRNA and different guide RNAs (sgRNAs) as well as DNA vectors of different sizes. Using this one-step procedure we generated mice carrying a tag or a fluorescent reporter construct in the Nanog, the Sox2, and the Oct4 gene as well as Mecp2 conditional mutant mice. In addition, using sgRNAs targeting two separate sites in the Mecp2 gene, we produced mice harboring the predicted deletions of about 700 bps. Finally, we analyzed potential off-targets of five sgRNAs in gene-modified mice and ESC lines and identified off-target mutations in only rare instances.United States. National Institutes of Health (HD 045022)United States. National Institutes of Health (R37CA084198