Teaching Compositionality to CNNs

Convolutional neural networks (CNNs) have shown great success in computer vision, approaching human-level performance when trained for specific tasks via application-specific loss functions. In this paper, we propose a method for augmenting and training CNNs so that their learned features are compositional. It encourages networks to form representations that disentangle objects from their surroundings and from each other, thereby promoting better generalization. Our method is agnostic to the specific details of the underlying CNN to which it is applied and can in principle be used with any CNN. As we show in our experiments, the learned representations lead to feature activations that are more localized and improve performance over non-compositional baselines in object recognition tasks.Comment: Preprint appearing in CVPR 201

Wafer-Scale Nanopatterning and Translation into High-Performance Piezoelectric Nanowires

The development of a facile method for fabricating one-dimensional, precisely positioned nanostructures over large areas offers exciting opportunities in fundamental research and innovative applications. Large-scale nanofabrication methods have been restricted in accessibility due to their complexity and cost. Likewise, bottom-up synthesis of nanowires has been limited in methods to assemble these structures at precisely defined locations. Nanomaterials such as PbZr_xTi_(1−x)O_3 (PZT) nanowires (NWs)—which may be useful for nonvolatile memory storage (FeRAM), nanoactuation, and nanoscale power generation—are difficult to synthesize without suffering from polycrystallinity or poor stoichiometric control. Here, we report a novel fabrication method which requires only low-resolution photolithography and electrochemical etching to generate ultrasmooth NWs over wafer scales. These nanostructures are subsequently used as patterning templates to generate PZT nanowires with the highest reported piezoelectric performance (d_(eff) ~ 145 pm/V). The combined large-scale nanopatterning with hierarchical assembly of functional nanomaterials could yield breakthroughs in areas ranging from nanodevice arrays to nanodevice powering

Testing Radiative Neutrino Mass Models at the LHC

The Large Hadron Collider provides us new opportunities to search for the origin of neutrino mass. Beyond the minimal see-saw models a plethora of models exist which realise neutrino mass at tree- or loop-level, and it is important to be sure that these possibilities are satisfactorily covered by searches. The purpose of this paper is to advance a systematic approach to this problem. Majorana neutrino mass models can be organised by SM-gauge-invariant operators which violate lepton number by two units. In this paper we write down the minimal ultraviolet completions for all of the mass-dimension 7 operators. We predict vector-like quarks, vector-like leptons, scalar leptoquarks, a charged scalar, and a scalar doublet, whose properties are constrained by neutrino oscillation data. A detailed collider study is presented for $O_3=LLQ\bar dH$ and $O_8 = L\bar d\bar e^\dagger \bar u^\dagger H$ completions with a vector-like quark $\chi\sim(3, 2, -\frac{5}{6})$ and a leptoquark $\phi\sim(\bar 3,1,\frac{1}{3})$. The existing LHC limits extracted from searches for vector-like fermions and sbottoms/stops are $m_\chi \gtrsim 620$ GeV and $m_\phi\gtrsim 600$ GeV.Comment: 40 pages, 13 figures; references added, minor changes, matches JHEP versio

Multi-institution analysis of racial disparity among African- American men eligible for prostate cancer active surveillance

There is a significant controversy on whether race should be a factor in considering active surveillance for low-risk prostate cancer. To address this question, we analyzed a multi-institution database to assess racial disparity between African-American and White-American men with low risk prostate cancer who were eligible for active surveillance but underwent radical prostatectomy. A retrospective analysis of prospectively collected clinical, pathologic and oncologic outcomes of men with low-risk prostate cancer from seven tertiary care institutions that underwent radical prostatectomy from 2003–2014 were used to assess potential racial disparity. Of the 333 (14.8%) African-American and 1923 (85.2%) White-American men meeting active surveillance criteria, African-American men were found to be slightly younger (57.5 vs 58.5 years old; p = 0.01) and have higher BMI (29.3 v 27.9; p \u3c 0.01), pre-op PSA (5.2 v 4.7; p \u3c 0.01), and maximum percentage cancer on biopsy (15.1% v 13.6%; p \u3c 0.01) compared to White-American men. Univariate and multivariate analysis demonstrated similar rates of upgrading, upstaging, positive surgical margin, and biochemical recurrence between races. These results suggest that single institution studies recommending more stringent AS enrollment criteria for AA men with a low-risk prostate cancer may not capture the complete oncologic landscape due to institutional variability in cancer outcomes. Since all seven institutions demonstrated no significant racial disparity, current active surveillance eligibility should not be modified based upon race until a prospective study has been completed. © Dinizo et al

The Role of Wild-Type p53 in Cisplatin-Induced Chk2 Phosphorylation and the Inhibition of Platinum Resistance with a Chk2 Inhibitor

The major obstacle in platinum chemotherapy is the repair of platinum-damaged DNA that results in increased resistance, reduced apoptosis, and finally treatment failure. Our research goal is to determine and block the mechanisms of platinum resistance. Our recent studies demonstrate that several kinases in the DNA-repair pathway are activated after cells are exposed to cisplatin. These include ATM, p53, and Chk2. The increased Chk2 phosphorylation is modulated by p53 in a wild-type p53 model. Overexpression of p53 by cDNA transfection in wt-p53 (but not p53 deficient) cells doubled the amount of Chk2 phosphorylation 48 hours after cisplatin treatment. p53 knockdown by specific siRNA greatly reduced Chk2 phosphorylation. We conclude that wild-type p53, in response to cisplatin stimulation, plays a role in the upstream regulation of Chk2 phosphorylation at Thr-68. Cells without normal p53 function survive via an alternative pathway in response to the exogenous influence of cisplatin. We strongly suggest that it is very important to include the p53 mutational status in any p53 involved studies due to the functional differentiation of wt p53 and p53 mutant. Inhibition of Chk2 pathway with a Chk2 inhibitor (C3742) increased cisplatin efficacy, especially those with defective p53. Our findings suggest that inhibition of platinum resistance can be achieved with a small-molecule inhibitor of Chk2, thus improving the therapeutic indices for platinum chemotherapy

Transient two-dimensional electronic spectroscopy: coherent dynamics at arbitrary times along the reaction coordinate

Recent advances in two-dimensional electronic spectroscopy (2DES) have enabled identification of fragile quantum coherences in condensed-phase systems near the equilibrium molecular geometry. In general, traditional 2DES cannot measure such coherences associated with photophysical processes that occur at times significantly after the initially prepared state has dephased, such as the evolution of the initial excited state into a charge transfer state. We demonstrate the use of transient two-dimensional electronic spectroscopy (t-2DES) to probe coherences in an electron donor–acceptor dyad consisting of a perylenediimide (PDI) acceptor and a perylene (Per) donor. An actinic pump pulse prepares the lowest excited singlet state of PDI followed by formation of the PDI•––Per•+ ion pair, which is probed at different times following the actinic pulse using 2DES. Analysis of the observed coherences provides information about electronic, vibronic, and vibrational interactions at any time along the reaction coordinate for ion pair formation

DASZL: Dynamic Action Signatures for Zero-shot Learning

There are many realistic applications of activity recognition where the set of potential activity descriptions is combinatorially large. This makes end-to-end supervised training of a recognition system impractical as no training set is practically able to encompass the entire label set. In this paper, we present an approach to fine-grained recognition that models activities as compositions of dynamic action signatures. This compositional approach allows us to reframe fine-grained recognition as zero-shot activity recognition, where a detector is composed "on the fly" from simple first-principles state machines supported by deep-learned components. We evaluate our method on the Olympic Sports and UCF101 datasets, where our model establishes a new state of the art under multiple experimental paradigms. We also extend this method to form a unique framework for zero-shot joint segmentation and classification of activities in video and demonstrate the first results in zero-shot decoding of complex action sequences on a widely-used surgical dataset. Lastly, we show that we can use off-the-shelf object detectors to recognize activities in completely de-novo settings with no additional training.Comment: 10 pages, 4 figures, 3 tables, AAAI2021 submissio