The paper considers constrained linear systems with stochastic additive disturbances and noisy measurements transmitted over a lossy communication channel. We propose a model predictive control (MPC) law that minimizes a discounted cost subject to a discounted expectation constraint. Sensor data is assumed to be lost with known probability, and data losses are accounted for by expressing the predicted control policy as an affine function of future observations, which results in a convex optimal control problem. An online constraint-tightening technique ensures recursive feasibility of the online optimization and satisfaction of the expectation constraint without bounds on the distributions of the noise and disturbance inputs. The cost evaluated along trajectories of the closed loop system is shown to be bounded by the optimal predicted cost. A numerical example is given to illustrate these results.

We prove an inequality between the sum of the Betti numbers of a complex projective manifold and its total curvature, and we characterize the complex projective manifolds whose total curvature is minimal. These results extend the classical theorems of Chern and Lashof to complex projective space.

The main aim of this paper is to detect dynamical properties of the Gy"orgyi-Field model of the Belousov-Zhabotinsky chemical reaction. The corresponding three-variable model given as a set of nonlinear ordinary differential equations depends on one parameter, the flow rate. As certain values of this parameter can give rise to chaos, the analysis was performed in order to identify different dynamics regimes. Dynamical properties were qualified and quantified using classical and also new techniques. Namely, phase portraits, bifurcation diagrams, the Fourier spectra analysis, the 0-1 test for chaos, and approximate entropy. The correlation between approximate entropy and the 0-1 test for chaos was observed and described in detail. Moreover, the three-stage system of nested subintervals of flow rates, for which in every level the 0-1 test for chaos and approximate entropy was computed, is showing the same pattern. The study leads to an open problem whether the set of flow rate parameters has Cantor like structure.

In this paper, we give a criterion of the Gorenstein property of the Ehrhart ring of the stable set polytope of an h-perfect graph: the Ehrhart ring of the stable set polytope of an h-perfect graph $G$ is Gorenstein if and only if (1) sizes of maximal cliques are constant (say $n$) and (2) (a) $n=1$, (b) $n=2$ and there is no odd cycle without chord and length at least 7 or (c) $ngeq 3$ and there is no odd cycle without chord and length at least 5.

In 1978, Dwight Duffus---editor-in-chief of the journal "Order" from 2010 to 2018 and chair of the Mathematics Department at Emory University from 1991 to 2005---wrote that "it is not obvious that $P$ is connected and $P^P$ isomorphic to $Q^Q$ implies that $Q$ is connected," where $P$ and $Q$ are finite non-empty posets. We show that, indeed, under these hypotheses $Q$ is connected and $Pcong Q$.

Given a standard graded polynomial ring over a commutative Noetherian ring $A$, we prove that the cohomological dimension and the height of the ideals defining any of its Veronese subrings are equal. This result is due to Ogus when $A$ is a field of characteristic zero, and follows from a result of Peskine and Szpiro when $A$ is a field of positive characteristic; our result applies, for example, when $A$ is the ring of integers.

Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore sizes are less than 50 nm. At this scale, the fluid properties are affected by nanoconfinement effects due to the increased fluid-solid interactions. For instance, gas adsorption to the pore walls could account for up to 85% of the total hydrocarbon volume in a tight reservoir. Although there are analytical solutions that describe this phenomenon for simple geometries, they are not suitable for describing realistic pores, where surface roughness and geometric anisotropy play important roles. To describe these, molecular dynamics (MD) simulations are used since they consider fluid-solid and fluid-fluid interactions at the molecular level. However, MD simulations are computationally expensive, and are not able to simulate scales larger than a few connected nanopores. We present a method for building and training physics-based deep learning surrogate models to carry out fast and accurate predictions of molecular configurations of gas inside nanopores. Since training deep learning models requires extensive databases that are computationally expensive to create, we employ active learning (AL). AL reduces the overhead of creating comprehensive sets of high-fidelity data by determining where the model uncertainty is greatest, and running simulations on the fly to minimize it. The proposed workflow enables nanoconfinement effects to be rigorously considered at the mesoscale where complex connected sets of nanopores control key applications such as hydrocarbon recovery and CO2 sequestration.

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.

Large transformer-based language models have been shown to be very effective in many classification tasks. However, their computational complexity prevents their use in applications requiring the classification of a large set of candidates. While previous works have investigated approaches to reduce model size, relatively little attention has been paid to techniques to improve batch throughput during inference. In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers. Each ranker is used to prune a subset of candidates in a batch, thus dramatically increasing throughput at inference time. Partial encodings from the transformer model are shared among rerankers, providing further speed-up. When compared to a state-of-the-art transformer model, our approach reduces computation by 37% with almost no impact on accuracy, as measured on two English Question Answering datasets.

Event-related potential (ERP) speller can be utilized in device control and communication for locked-in or severely injured patients. However, problems such as inter-subject performance instability and ERP-illiteracy are still unresolved. Therefore, it is necessary to predict classification performance before performing an ERP speller in order to use it efficiently. In this study, we investigated the correlations with ERP speller performance using a resting-state before an ERP speller. In specific, we used spectral power and functional connectivity according to four brain regions and five frequency bands. As a result, the delta power in the frontal region and functional connectivity in the delta, alpha, gamma bands are significantly correlated with the ERP speller performance. Also, we predicted the ERP speller performance using EEG features in the resting-state. These findings may contribute to investigating the ERP-illiteracy and considering the appropriate alternatives for each user.

Stabilizing multi-steered articulated vehicles in backward motion is a complex task for any human driver. Unless the vehicle is accurately steered, its structurally unstable joint-angle kinematics during reverse maneuvers can cause the vehicle segments to fold and enter a jack-knife state. In this work, a model predictive path-following controller is proposed enabling automatic low-speed steering control of multi-steered articulated vehicles, comprising a car-like tractor and an arbitrary number of trailers with passive or active steering. The proposed path-following controller is tailored to follow nominal paths that contains full state and control-input information, and is designed to satisfy various physical constraints on the vehicle states as well as saturations and rate limitations on the tractor's curvature and the trailer steering angles. The performance of the proposed model predictive path-following controller is evaluated in a set of simulations for a multi-steered 2-trailer with a car-like tractor where the last trailer has steerable wheels.

Introduction of spectrum-sharing in 5G and subsequent generation networks demand base-station(s) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.) of occupied bands. Such characterization at Nyquist sampling is area and power-hungry due to the need for high-speed digitization. Though sub-Nyquist sampling (SNS) offers an excellent alternative when the spectrum is sparse, it suffers from poor performance at low signal to noise ratio (SNR) and demands careful design and integration of digital reconstruction, tunable channelizer and characterization algorithms. In this paper, we propose a novel deep-learning framework via a single unified pipeline to accomplish two tasks: 1)~Reconstruct the signal directly from sub-Nyquist samples, and 2)~Wideband spectrum characterization. The proposed approach eliminates the need for complex signal conditioning between reconstruction and characterization and does not need complex tunable channelizers. We extensively compare the performance of our framework for a wide range of modulation schemes, SNR and channel conditions. We show that the proposed framework outperforms existing SNS based approaches and characterization performance approaches to Nyquist sampling-based framework with an increase in SNR. Easy to design and integrate along with a single unified deep learning framework make the proposed architecture a good candidate for reconfigurable platforms.

This paper considers the multi-group multicast beamforming optimization problem, for which the optimal solution has been unknown due to the non-convex and NP-hard nature of the problem. By utilizing the successive convex approximation numerical method and Lagrangian duality, we obtain the optimal multicast beamforming solution structure for both the quality-of-service (QoS) problem and the max-min fair (MMF) problem. The optimal structure brings valuable insights into multicast beamforming: We show that the notion of uplink-downlink duality can be generalized to the multicast beamforming problem. The optimal multicast beamformer is a weighted MMSE filter based on a group-channel direction: a generalized version of the optimal downlink multi-user unicast beamformer. We also show that there is an inherent low-dimensional structure in the optimal multicast beamforming solution independent of the number of transmit antennas, leading to efficient numerical algorithm design, especially for systems with large antenna arrays. We propose efficient algorithms to compute the multicast beamformer based on the optimal beamforming structure. Through asymptotic analysis, we characterize the asymptotic behavior of the multicast beamformers as the number of antennas grows, and in turn, provide simple closed-form approximate multicast beamformers for both the QoS and MMF problems. This approximation offers practical multicast beamforming solutions with a near-optimal performance at very low computational complexity for large-scale antenna systems.

We design and implement the first private and anonymous decentralized crowdsourcing system ZebraLancer, and overcome two fundamental challenges of decentralizing crowdsourcing, i.e., data leakage and identity breach.

First, our outsource-then-prove methodology resolves the tension between the blockchain transparency and the data confidentiality to guarantee the basic utilities/fairness requirements of data crowdsourcing, thus ensuring: (i) a requester will not pay more than what data deserve, according to a policy announced when her task is published via the blockchain; (ii) each worker indeed gets a payment based on the policy, if he submits data to the blockchain; (iii) the above properties are realized not only without a central arbiter, but also without leaking the data to the open blockchain. Second, the transparency of blockchain allows one to infer private information about workers and requesters through their participation history. Simply enabling anonymity is seemingly attempting but will allow malicious workers to submit multiple times to reap rewards. ZebraLancer also overcomes this problem by allowing anonymous requests/submissions without sacrificing accountability. The idea behind is a subtle linkability: if a worker submits twice to a task, anyone can link the submissions, or else he stays anonymous and unlinkable across tasks. To realize this delicate linkability, we put forward a novel cryptographic concept, i.e., the common-prefix-linkable anonymous authentication. We remark the new anonymous authentication scheme might be of independent interest. Finally, we implement our protocol for a common image annotation task and deploy it in a test net of Ethereum. The experiment results show the applicability of our protocol atop the existing real-world blockchain.

Visualization-oriented natural language interfaces (V-NLIs) have been explored and developed in recent years. One challenge faced by V-NLIs is in the formation of effective design decisions that usually requires a deep understanding of user queries. Learning-based approaches have shown potential in V-NLIs and reached state-of-the-art performance in various NLP tasks. However, because of the lack of sufficient training samples that cater to visual data analytics, cutting-edge techniques have rarely been employed to facilitate the development of V-NLIs. We present a new dataset, called Quda, to help V-NLIs understand free-form natural language. Our dataset contains 14;035 diverse user queries annotated with 10 low-level analytic tasks that assist in the deployment of state-of-the-art techniques for parsing complex human language. We achieve this goal by first gathering seed queries with data analysts who are target users of V-NLIs. Then we employ extensive crowd force for paraphrase generation and validation. We demonstrate the usefulness of Quda in building V-NLIs by creating a prototype that makes effective design decisions for free-form user queries. We also show that Quda can be beneficial for a wide range of applications in the visualization community by analyzing the design tasks described in academic publications.

We review and critique Boyu Sima's paper, "A solution of the P versus NP problem based on specific property of clique function," (arXiv:1911.00722) which claims to prove that ${ m P} eq{ m NP}$ by way of removing the gap between the nonmonotone circuit complexity and the monotone circuit complexity of the clique function. We first describe Sima's argument, and then we describe where and why it fails. Finally, we present a simple example that clearly demonstrates the failure.

Bernstein polynomials, long a staple of approximation theory and computational geometry, have also increasingly become of interest in finite element methods. Many fundamental problems in interpolation and approximation give rise to interesting linear algebra questions. When attempting to find a polynomial approximation of boundary or initial data, one encounters the Bernstein-Vandermonde matrix, which is found to be highly ill-conditioned. Previously, we used the relationship between monomial Bezout matrices and the inverse of Hankel matrices to obtain a decomposition of the inverse of the Bernstein mass matrix in terms of Hankel, Toeplitz, and diagonal matrices. In this paper, we use properties of the Bernstein-Bezout matrix to factor the inverse of the Bernstein-Vandermonde matrix into a difference of products of Hankel, Toeplitz, and diagonal matrices. We also use a nonstandard matrix norm to study the conditioning of the Bernstein-Vandermonde matrix, showing that the conditioning in this case is better than in the standard 2-norm. Additionally, we use properties of multivariate Bernstein polynomials to derive a block $LU$ decomposition of the Bernstein-Vandermonde matrix corresponding to equispaced nodes on the $d$-simplex.

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.

The invention relates to a granulated powder intended, in particular, for the production of ceramic sintered parts, said powder having the following chemical weight composition, based on dry matter, namely: a zirconia stabiliser selected from the group containing Y2O3, Sc2O3, MgO, CaO, CeO2, and mixtures thereof, the weight content of stabiliser, based on the total zirconia and stabiliser content, being between 2% and 20% and the MgO+CaO content being less than 5% based on the total zirconia and stabiliser content; at least 1% of a first binder having a glass transition temperature less than or equal to 25° C.; 0-4% of an additional binder having a glass transition temperature greater than 25° C.; 5-50% alumina; 0-4% of a temporary additive different from the first binder and the additional binder, the total content of the first binder, the additional binder and the temporary additive being less than 9%; less than 2% impurities; and ZrO2 to make up 100%. According to the invention, the median diameter D50 of the powder is between 80 and 130 μm, the percentile D99.5 is less than 500 μm and the relative density of the granules is between 30% and 60%.

Disclosed are methods of purifying (20R) and (20S) analogs of 2-methylene-19-nor-22-dimethyl-1α,25-dihydroxyvitamin D3 to obtain the (20R) and (20S) analogs in crystalline form. The method includes the steps of preparing a solvent of either diethyl ether or a mixture of 2-propanol and hexane, dissolving a product containing the (20R) and (20S) analog to be purified in the solvent, cooling the solvent and dissolved product below ambient temperature for a sufficient amount of time to form a precipitate of crystals, and recovering the crystals.

Methods and compositions for treating 25-hydroxyvitamin D insufficiency and deficiency in a patient are described herein. The method includes orally administering to the patient a delayed, sustained release formulation including a first ingredient selected from the group consisting of 25-hydroxyvitamin D2, 25-hydroxyvitamin D3, or a combination of 25-hydroxyvitamin D2 and 25-hydroxyvitamin D3, or it includes gradually administering to the patient a sterile intravenous formulation including a first ingredient selected from the group consisting of 25-hydroxyvitamin D2, 25-hydroxyvitamin D3, or a combination of 25-hydroxyvitamin D2 and 25-hydroxyvitamin D3.

The present invention provides novel 16,23-diene 25-oxime ether analogs of 1,25-dihydroxy vitamin D3, compositions comprising these compounds and methods of using these compounds as inhibitors of CYP24. In particular, the novel compound of the invention are useful for treating diseases which benefit from a modulation of the levels of 1,25-dihydroxy vitamin D3, for example, cell-proliferative disorders.

Disclosed are methods of purifying the compound (20R)-2-methylene-19-nor-24-difluoro-1α,25-dihydroxyvitamin D3 to obtain the compound in crystalline form. The methods typically include the steps of dissolving a product containing the compound in a solvent comprising hexane and 2-propanol, cooling the solvent and dissolved product below ambient temperature for a sufficient amount of time to form a precipitate of crystals, and recovering the crystals.

This invention discloses N-cyclopropyl-(20R)-2-methylene-19,26,27-trinor-25-aza-vitamin D analogs, and specifically N-cyclopropyl-(20R)-2-methylene-19,26,27-trinor-25-aza-1α-hydroxyvitamin D3 and pharmaceutical uses therefor. This compound exhibits relatively high binding activity and pronounced activity in arresting the proliferation of undifferentiated cells and inducing their differentiation to the monocyte thus evidencing use as an anti-cancer agent especially for the treatment or prevention of leukemia, colon cancer, breast cancer, skin cancer or prostate cancer.

Disclosed are methods of purifying the compound (20R)-19-nor-24-difluoro-1α,25-dihydroxyvitamin D3 to obtain the compound in crystalline form. The methods typically include the steps of dissolving a product containing the compound in a solvent comprising hexane and 2-propanol, cooling the solvent and dissolved product below ambient temperature for a sufficient amount of time to form a precipitate of crystals, and recovering the crystals.

A modulator generates a baseband digital signal from an information-bearing digital signal. The baseband signal has time-varying phase and amplitude defined by a sequence of complex data words, each having an in-phase (I) component and a quadrature (Q) component. A noise-shaping modulator generates a noise-shaped digital signal from the baseband digital signal such that quantization noise in the noise-shaping modulator is attenuated by a spectral null of its noise transfer function. The spectral null is selected by a noise-shaping parameter corresponding to a selected one of a plurality of output frequencies. A signal converter generates an analog signal conveying the information of the information-bearing digital signal on an analog carrier signal having the selected output frequency.

Provided are a detection agent for detecting hydroxy vitamin D, preparation method thereof, and use thereof in 25-hydroxy vitamin D immunological detection. The detection agent comprises a conjugate formed by a 25-hydroxy vitamin D antigen derivative and protein carrier, and magnetic spheres coated by the conjugate. Also provided is a 25-hydroxy vitamin D detection kit comprising the detection agent.

Be extra kind to kind people. ARIES (March 21-April 19): Your oracle comes from Aries poet Octavio Paz: "The path the ancestors cleared is overgrown, unused. The other path, smooth and broad, is crowded with travelers.…

SOUTHAMPTON'S buzzing live music scene has always been criminally underrated in my humble opinion.

NEW proposals are being put together for the first new railway station in Hampshire for 25 years, the Daily Echo can reveal.

THIS fine country farmhouse in the New Forest is on the market for £2.25 million

In this episode of the AppleVis Extra, Dave Nason is joined by Scott Davert, Tyler Stephen and Club AppleVis Member Tate Luck to talk about Apple's service oriented March 25 event, titled "It's Showtime". At the event, Apple unveiled updates to the Apple News and TV apps, alongside new subscription services called Apple News+ and Apple TV+. In addition they introduced a gaming subscription service called Apple Arcade and a new Apple credit card.
You can also check out our blog post recapping the event.

In this week’s episode, Liam & Gaz bring you new music from Marcus King & The Wood Brothers.

There’s also classic blues Jesse Fuller, Northern Soul from Jo Ann Henderson, Rock N Roll from Tom Petty, RnB from Ted Taylor and Southern Soul from Stax legend Eddie Floyd.

UK Americana legend and Rock N Roll pianist, Geraint Watkins is in session this week – playing a version of ‘I Ain’t Got Nothing But The Blues’ originally by Duke Ellington.

Be sure to check out:

The Blues Chronicles
Bush Hall - London
Thurs 27th 2020
Fri 28th Feb 2020
https://theblueschronicles.com/events/

50th Anniversary of Abbey Road [Live]
The Jazz Café - London
Thursday 19th December 2019
https://thejazzcafelondon.com/event/beatles-abbey-road-50th-anniversary-celebration-3/

When you've caught up with the podcast, head over to The Blues Kitchen YouTube channel:
www.youtube.com/BluesKitchenTV

Email the show: radio@theblueskitchen.com

Listen on Apple Podcasts, TuneIn, Mixcloud, Acast & many more…

In the final episode of 2019, Gaz & Liam celebrate 10 years of The Blues Kitchen Podcast with a look back at the finest blues, soul, folk, roots and indie music from the last decade. 

Expect music from Charles Bradley, Leon Bridges, Mavis Staples, Gary Clark Jnr, Blind Boy Paxton, JD McPherson, The Arcs, Margo Price, Alabama Shakes, Dr John, David Bowie & much much more... 

Be sure to check out:

The Blues Chronicles
Bush Hall - London
Thurs 27th 2020
Fri 28th Feb 2020
https://theblueschronicles.com/events/

When you've caught up with the podcast, head over to The Blues Kitchen YouTube channel:
www.youtube.com/BluesKitchenTV

Email the show: radio@theblueskitchen.com

Listen on Apple Podcasts, TuneIn, Mixcloud, Acast & many more…

This week, we talk to Peter Rosenberg, DJ at NYC’s legendary Hot 97. Peter is a co-host of the popular morning show and also has a Sunday night underground program. He’s the host of the game show Hip Hop Squares, runs the popular Noisemakers interview series, and has his own Juan Epstein podcast. He talked to us about his history, the Summer Jam controversy, his definition of “real hip-hop,” and more.

See http://theciphershow.com/episode/25/ for full show notes and comments.

Prof is a Minneapolis-based rapper whose fun and deceptively intricate tracks and entertaining, sometimes chaotic live shows have been entertaining fans since the mid-2000s. He has an outsized, outrageous comic persona, but that doesn’t stop him from frequently rapping movingly and artfully about his difficult childhood and family life.

Prof quickly rose through the hip-hop ranks in his hometown, and is one of only a handful of rappers - local or otherwise - who can sell out Minneapolis’ biggest venues. His success led to him joining forces with the city’s pre-eminent hip-hop label, Rhymesayers. Prof’s debut album on the label, Liability, just dropped this past Friday, October 16th.

See http://theciphershow.com/episode/125/ for full show notes and comments.

Mathematics, or Allah Mathematics, is a DJ and producer who has been part of the Wu-Tang Clan since before there was a Wu-Tang Clan. He DJ’d for GZA in the early 1990s, back when the rapper was known as “The Genius.” At the same time, Mathematics kept seeing a young rapper named Rakeem around town. That guy, Rakeem, would soon become the RZA, and would assemble an all-star team of MCs to form one of the most influential rap groups of all time.

Mathematics DJ’d for almost all of the Wu members at different points, as well as spinoff groups like Red and Meth. But starting in the mid-1990s, he really made his mark as a producer. Mathematics produced some of the best-loved songs from Wu-related projects, including “Mighty Healthy,” “Cobra Clutch,” “Wu Banga 101,” and more. He also designed the crew’s famous “W” logo.

In recent years, he’s been heavily involved with group albums like Iron Flag and A Better Tomorrow. All of this culminated in his new album, Wu-Tang: The Saga Continues. The record features contributions from almost every member of the Clan, and is executive-produced by RZA himself.

See http://theciphershow.com/episode/225/ for full show notes and comments.

Masta Ace is a legendary rhymer whose career began with the Juice Crew and has continued through a series of beloved solo and group albums. Marco Polo is a Toronto-born producer who moved to New York City in the early 2000s, and took his adopted hometown’s traditional boom-bap aesthetic to heart. Marco has brought his rugged sound to Torae, Pharoahe Monch, Ruste Juxx, Sean Price, Vinnie Paz, Rah Digga, and many more.

Now, Marco and Ace have joined up for a new album. A Breukelen Story combines a set of incredible songs with a series of skits that follow Marco’s journey from Toronto to NYC—an Ace-written storyline that both mirrors and features people from the producer’s real life.

See http://theciphershow.com/episode/250/ for full show notes and comments.

Every Christmas since 2013, we’ve sat down with Bill—who is the former head of PR at Def Jam, among numerous other accomplishments—to talk about his world-famous Xmas Jollies mix. Bill spends the whole year scouring record stores, flea markets, the internet, and everywhere in between to find the perfect batch of great holiday music, and then shares it with his select mailing list, and with us. Now, we talk to him about some of this year’s selections.

You can hear Bill’s mix in its entirety at our Soundcloud page.

See http://theciphershow.com/episode/251/ for full show notes and comments.

As many of you know, The Cipher is going on an indefinite hiatus following this episode. For more information about why, visit us on Twitter or Facebook, or just listen to this episode. But in short, we have no plans to make any more new episodes after this one. Our archives, however, will still be available and free on our website, on iTunes and other podcast apps, and on SoundCloud.

For this goodbye episode, we called up our friend and episode 201 guest Jay Smooth to interview The Cipher’s host Shawn Setaro and its producer Josh Kross. Jay, as you’ll hear, did an amazing job, and got us to touch on so many aspects of the show’s history, and of ways we thought about it over the years.

So here’s the end of the road, for now. Thanks for listening.

See http://theciphershow.com/episode/252/ for full show notes and comments.

Joey and Lee are back. Joey talks about losing his dear friend Marilyn, the movie Once Upon a Time in the West, and some of the famous singers the Joey cannot stand. Joey's friend Mike calls in to talk about elections in North Bergen.

Streamed live on 11/05/2012

Joey and Lee talk about Thanksgivng, Marvin Gaye, and Football on another Sunday gambling addition. Danny B calls back in and tells stories about his brother and also seeing Amare Stodamire. We also have our first podcast with two calls. Joey's friend Jimmy calls in. Jimmy was the person who orignally convinced Joey to move to Colorado.

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Streamed live on 11/25/2012

Tim Donaghy, the ex NBA ref calls back in to talk about his book and his upcoming website refpicks.com.

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Streamed live on 02/25/2013

Two great guests today. Television writer/producer and actor in Gangster Squad Mick Betancourt calls in. Also, boxer and story teller Vinny Curto calls in ahead of him joining Joey and Lee at the live Church at the Ice House.

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Streamed live on 03/25/2013

Ari Shaffir calls in.

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Streamed live on 09/25/2013.

Uncle Mike Kessler calls in to motivate us.

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Streamed live on 11/06/2013.

Cassius Morris "That Reporter Kid" calls in.

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Recorded live on 11/25/2013.

Andre Carter, Former NFL Player who played for The 49ers, Washington Redskins, Oakland Raiders and The New England Patriots, joins Joey Diaz and Lee Syatt live in studio.

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