Foundations of a Multi-way Spectral Clustering Framework for Hybrid Linear Modeling

The problem of Hybrid Linear Modeling (HLM) is to model and segment data using a mixture of affine subspaces. Different strategies have been proposed to solve this problem, however, rigorous analysis justifying their performance is missing. This paper suggests the Theoretical Spectral Curvature Clustering (TSCC) algorithm for solving the HLM problem, and provides careful analysis to justify it. The TSCC algorithm is practically a combination of Govindu's multi-way spectral clustering framework (CVPR 2005) and Ng et al.'s spectral clustering algorithm (NIPS 2001). The main result of this paper states that if the given data is sampled from a mixture of distributions concentrated around affine subspaces, then with high sampling probability the TSCC algorithm segments well the different underlying clusters. The goodness of clustering depends on the within-cluster errors, the between-clusters interaction, and a tuning parameter applied by TSCC. The proof also provides new insights for the analysis of Ng et al. (NIPS 2001).Comment: 40 pages. Minor changes to the previous version (mainly revised Sections 2.2 & 2.3, and added references). Accepted to the Journal of Foundations of Computational Mathematic

Flavor Alignment via Shining in RS

We present a class of warped extra dimensional models whose flavor violating interactions are much suppressed compared to the usual anarchic case due to flavor alignment. Such suppression can be achieved in models where part of the global flavor symmetry is gauged in the bulk and broken in a controlled manner. We show that the bulk masses can be aligned with the down type Yukawa couplings by an appropriate choice of bulk flavon field representations and TeV brane dynamics. This alignment could reduce the flavor violating effects to levels which allow for a Kaluza-Klein scale as low as 2-3 TeV, making the model observable at the LHC. However, the up-type Yukawa couplings on the IR brane, which are bounded from below by recent bounds on CP violation in the D system, induce flavor misalignment radiatively. Off-diagonal down-type Yukawa couplings and kinetic mixings for the down quarks are both consequences of this effect. These radiative Yukawa corrections can be reduced by raising the flavon VEV on the IR brane (at the price of some moderate tuning), or by extending the Higgs sector. The flavor changing effects from the radiatively induced Yukawa mixing terms are at around the current upper experimental bounds. We also show the generic bounds on UV-brane induced flavor violating effects, and comment on possible additional flavor violations from bulk flavor gauge bosons and the bulk Yukawa scalars.Comment: 28 page

History information emerges in the cortex during learning

We learn from our experience but the underlying neuronal mechanisms incorporating past information to facilitate learning is relatively unknown. Specifically, which cortical areas encode history-related information and how is this information modulated across learning? To study the relationship between history and learning, we continuously imaged cortex-wide calcium dynamics as mice learn to use their whiskers to discriminate between two different textures. We mainly focused on comparing the same trial type with different trial history, that is, a different preceding trial. We found trial history information in barrel cortex (BC) during stimulus presentation. Importantly, trial history in BC emerged only as the mouse learned the task. Next, we also found learning-dependent trial history information in rostrolateral (RL) association cortex that emerges before stimulus presentation, preceding activity in BC. Trial history was also encoded in other cortical areas and was not related to differences in body movements. Interestingly, a binary classifier could discriminate trial history at the single trial level just as well as current information both in BC and RL. These findings suggest that past experience emerges in the cortex around the time of learning, starting from higher-order association area RL and propagating down (i.e., top-down projection) to lower-order BC where it can be integrated with incoming sensory information. This integration between the past and present may facilitate learning

Off-Path TCP Exploits of the Mixed IPID Assignment

In this paper, we uncover a new off-path TCP hijacking attack that can be used to terminate victim TCP connections or inject forged data into victim TCP connections by manipulating the new mixed IPID assignment method, which is widely used in Linux kernel version 4.18 and beyond to help defend against TCP hijacking attacks. The attack has three steps. First, an off-path attacker can downgrade the IPID assignment for TCP packets from the more secure per-socket-based policy to the less secure hash-based policy, building a shared IPID counter that forms a side channel on the victim. Second, the attacker detects the presence of TCP connections by observing the shared IPID counter on the victim. Third, the attacker infers the sequence number and the acknowledgment number of the detected connection by observing the side channel of the shared IPID counter. Consequently, the attacker can completely hijack the connection, i.e., resetting the connection or poisoning the data stream. We evaluate the impacts of this off-path TCP attack in the real world. Our case studies of SSH DoS, manipulating web traffic, and poisoning BGP routing tables show its threat on a wide range of applications. Our experimental results show that our off-path TCP attack can be constructed within 215 seconds and the success rate is over 88%. Finally, we analyze the root cause of the exploit and develop a new IPID assignment method to defeat this attack. We prototype our defense in Linux 4.18 and confirm its effectiveness through extensive evaluation over real applications on the Internet

Astrophysical Implications of a Visible Dark Matter Sector from a Custodially Warped-GUT

We explore, within the warped extra dimensional framework, the possibility of finding anti-matter signals in cosmic rays (CRs) from dark matter (DM) annihilation. Exchange of order 100 GeV radion, an integral part of our setup, generically results in Sommerfeld enhancement of the annihilation rate for TeV DM mass. No dark sector is required to obtain boosted annihilation cross sections. A mild hierarchy between the radion and DM masses can be natural due to the pseudo-Goldstone boson nature of the radion. Implications of Sommerfeld enhancement in warped grand unified theory (GUT) models, where proton stability implies a DM candidate, are studied. We show, via partially unified Pati-Salam group, how to incorporate a custodial symmetry for Z->b\bar b into the GUT framework such that a few TeV Kaluza-Klein (KK) mass scale is allowed by precision tests. The model with smallest fully unified SO(10) representation allows us to decouple the DM from the electroweak sector. Thus, a correct DM relic density is obtained and direct detection bounds are satisfied. Looking at robust CR observables, a possible future signal in the \bar p / p flux ratio is found. We show how to embed a similar custodial symmetry for the right handed tau, allowing it to be strongly coupled to KK particles. Such a scenario might lead to observed signal in CR positrons; however, the DM candidate in this case can not constitute all of the DM in the universe. Independently of the above, the strong coupling between KK particles and tau's can lead to striking LHC signals.Comment: 53 pages, 9 figure

Electronic Post-Compensation of Optical Transmission Impairments Using Digital Backward Propagation

Systems and method of compensating for transmission impairment are disclosed. One such method comprises: receiving an optical signal which has been distorted in the physical domain by an optical transmission channel; and propagating the distorted optical signal backward in the electronic domain in a corresponding virtual optical transmission channel

An overview of next-generation architectures for machine learning: roadmap, opportunities and challenges in the IoT era

The number of connected Internet of Things (IoT) devices are expected to reach over 20 billion by 2020. These range from basic sensor nodes that log and report the data to the ones that are capable of processing the incoming information and taking an action accordingly. Machine learning, and in particular deep learning, is the de facto processing paradigm for intelligently processing these immense volumes of data. However, the resource inhibited environment of IoT devices, owing to their limited energy budget and low compute capabilities, render them a challenging platform for deployment of desired data analytics. This paper provides an overview of the current and emerging trends in designing highly efficient, reliable, secure and scalable machine learning architectures for such devices. The paper highlights the focal challenges and obstacles being faced by the community in achieving its desired goals. The paper further presents a roadmap that can help in addressing the highlighted challenges and thereby designing scalable, high-performance, and energy efficient architectures for performing machine learning on the edge

Evolution of Olfactory Receptor Genes in Primates Dominated by Birth-and-Death Process

Olfactory receptor (OR) is a large family of G protein–coupled receptors that can detect odorant in order to generate the sense of smell. They constitute one of the largest multiple gene families in animals including primates. To better understand the variation in odor perception and evolution of OR genes among primates, we computationally identified OR gene repertoires in orangutans, marmosets, and mouse lemurs and investigated the birth-and-death process of OR genes in the primate lineage. The results showed that 1) all the primate species studied have no more than 400 intact OR genes, fewer than rodents and canine; 2) Despite the similar number of OR genes in the genome, the makeup of the OR gene repertoires between different primate species is quite different as they had undergone dramatic birth-and-death evolution with extensive gene losses in the lineages leading to current species; 3) Apes and Old World monkey (OWM) have similar fraction of pseudogenes, whereas New World monkey (NWM) have fewer pseudogenes. To measure the selective pressure that had affected the OR gene repertoires in primates, we compared the ratio of nonsynonymous with synonymous substitution rates by using 70 one-to-one orthologous quintets among five primate species. We found that OR genes showed relaxed selective constraints in apes (humans, chimpanzees, and orangutans) than in OWMs (macaques) and NWMs (marmosets). We concluded that OR gene repertoires in primates have evolved in such a way to adapt to their respective living environments. Differential selective constraints might play important role in the primate OR gene evolution in each primate species