Spectral Decomposition of Missing Transverse Energy at Hadron Colliders

We propose a spectral decomposition to systematically extract information of dark matter at hadron colliders. The differential cross section of events with missing transverse energy (MET) can be expressed by a linear combination of basis functions. In the case of $s$-channel mediator models for dark matter particle production, basis functions are identified with the differential cross sections of sub-processes of virtual mediator and visible particle production while the coefficients of basis functions correspond to dark matter invariant mass distribution in the manner of the K\"all\'en-Lehmann spectral decomposition. For a given MET data set and mediator model, we show that one can differentiate a certain dark matter-mediator interaction from another through spectral decomposition.Comment: 6+4 pages, 6 figures, PRL versio

Outage-based ergodic link adaptation for fading channels with delayed CSIT

Link adaptation in which the transmission data rate is dynamically adjusted according to channel variation is often used to deal with time-varying nature of wireless channel. When channel state information at the transmitter (CSIT) is delayed by more than channel coherence time due to feedback delay, however, the effect of link adaptation can possibly be taken away if this delay is not taken into account. One way to deal with such delay is to predict current channel quality given available observation, but this would inevitably result in prediction error. In this paper, an algorithm with different view point is proposed. By using conditional cdf of current channel given observation, outage probability can be computed for each value of transmission rate $R$. By assuming that the transmission block error rate (BLER) is dominated by outage probability, the expected throughput can also be computed, and $R$ can be determined to maximize it. The proposed scheme is designed to be optimal if channel has ergodicity, and it is shown to considerably outperform conventional schemes in certain Rayleigh fading channel model

125 GeV Higgs as a pseudo-Goldstone boson in supersymmetry with vector-like matters

We propose a possibility of the 125 GeV Higgs being a pseudo-Goldstone boson in supersymmetry with extra vector-like fermions. Higgs mass is obtained from loops of top quark and vector-like fermions from the global symmetry breaking scale f at around TeV. The mu, Bmu/mu \sim f are generated from the dynamics of global symmetry breaking and the Higgs quartic coupling vanishes at f as tan beta \simeq 1. The relation of msoft \sim $4\pi M_Z$ with f \sim mu \sim m_soft \sim TeV is obtained and large mu does not cause a fine tuning for the electroweak symmetry breaking. The Higgs to di-photon rate can be enhanced from the loop of uncolored vector-like matters. The stability problem of Higgs potential with vector-like fermions can be nicely cured by the UV completion with the Goldstone picture.Comment: 28 pages, 8 figure

List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels

In this paper, we present list autoencoder (listAE) to mimic list decoding used in classical coding theory. With listAE, the decoder network outputs a list of decoded message word candidates. To train the listAE, a genie is assumed to be available at the output of the decoder. A specific loss function is proposed to optimize the performance of a genie-aided (GA) list decoding. The listAE is a general framework and can be used with any AE architecture. We propose a specific architecture, referred to as incremental-redundancy AE (IR-AE), which decodes the received word on a sequence of component codes with non-increasing rates. Then, the listAE is trained and evaluated with both IR-AE and Turbo-AE. Finally, we employ cyclic redundancy check (CRC) codes to replace the genie at the decoder output and obtain a CRC aided (CA) list decoder. Our simulation results show that the IR-AE under CA list decoding demonstrates meaningful coding gain over Turbo-AE and polar code at low block error rates range.Comment: 6 pages with references and 7 figure

Simplified Successive Cancellation List Decoding of PAC Codes

Polar codes are the first class of structured channel codes that achieve the symmetric capacity of binary channels with efficient encoding and decoding. In 2019, Arikan proposed a new polar coding scheme referred to as polarization-adjusted convolutional (PAC)} codes. In contrast to polar codes, PAC codes precode the information word using a convolutional code prior to polar encoding. This results in material coding gain over polar code under Fano sequential decoding as well as successive cancellation list (SCL) decoding. Given the advantages of SCL decoding over Fano decoding in certain scenarios such as low-SNR regime or where a constraint on the worst case decoding latency exists, in this paper, we focus on SCL decoding and present a simplified SCL (SSCL) decoding algorithm for PAC codes. SSCL decoding of PAC codes reduces the decoding latency by identifying special nodes in the decoding tree and processing them at the intermediate stages of the graph. Our simulation results show that the performance of PAC codes under SSCL decoding is almost similar to the SCL decoding while having lower decoding latency.Comment: 7 pages, 3 figure

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

ProductAE: Toward Deep Learning Driven Error-Correction Codes of Large Dimensions

While decades of theoretical research have led to the invention of several classes of error-correction codes, the design of such codes is an extremely challenging task, mostly driven by human ingenuity. Recent studies demonstrate that such designs can be effectively automated and accelerated via tools from machine learning (ML), thus enabling ML-driven classes of error-correction codes with promising performance gains compared to classical designs. A fundamental challenge, however, is that it is prohibitively complex, if not impossible, to design and train fully ML-driven encoder and decoder pairs for large code dimensions. In this paper, we propose Product Autoencoder (ProductAE) -- a computationally-efficient family of deep learning driven (encoder, decoder) pairs -- aimed at enabling the training of relatively large codes (both encoder and decoder) with a manageable training complexity. We build upon ideas from classical product codes and propose constructing large neural codes using smaller code components. ProductAE boils down the complex problem of training the encoder and decoder for a large code dimension $k$ and blocklength $n$ to less-complex sub-problems of training encoders and decoders for smaller dimensions and blocklengths. Our training results show successful training of ProductAEs of dimensions as large as $k = 300$ bits with meaningful performance gains compared to state-of-the-art classical and neural designs. Moreover, we demonstrate excellent robustness and adaptivity of ProductAEs to channel models different than the ones used for training.Comment: arXiv admin note: text overlap with arXiv:2110.0446

Effect of charge-transfer complex on the energy level alignment between graphene and organic molecules

We performed density-functional theory calculations to study the electronic structures at the interfaces between graphene and organic molecules that have been used in organic light-emitting diodes. In terms of work function, graphene itself is not favorable as either anode or cathode for commonly used electron or hole transport molecular species. However, the formation of charge transfer complex on the chemically inert sp(2) carbon surface can provide a particular advantage. Unlike metal surfaces, the graphene surface remains non-bonded to electron-accepting molecules even after electron transfer, inducing an improved Fermi-level alignment with the highest-occupied-molecular-orbital level of the hole-injecting-layer molecules.open1