TDMA is Optimal for All-unicast DoF Region of TIM if and only if Topology is Chordal Bipartite

The main result of this work is that an orthogonal access scheme such as TDMA achieves the all-unicast degrees of freedom (DoF) region of the topological interference management (TIM) problem if and only if the network topology graph is chordal bipartite, i.e., every cycle that can contain a chord, does contain a chord. The all-unicast DoF region includes the DoF region for any arbitrary choice of a unicast message set, so e.g., the results of Maleki and Jafar on the optimality of orthogonal access for the sum-DoF of one-dimensional convex networks are recovered as a special case. The result is also established for the corresponding topological representation of the index coding problem

Reliability of voting in fault-tolerant software systems for small output spaces

Under a voting strategy in a fault-tolerant software system there is a difference between correctness and agreement. An independent N-version programming reliability model is proposed for treating small output spaces which distinguishes between correctness and agreement. System reliability is investigated using analytical relationships and simulation. A consensus majority voting strategy is proposed and its performance is analyzed and compared with other voting strategies. Consensus majority strategy automatically adapts the voting to different component reliability and output space cardinality characteristics. It is shown that absolute majority voting strategy provides a lower bound on the reliability provided by the consensus majority, and 2-of-n voting strategy an upper bound. If r is the cardinality of the output space it is proved the 1/r is a lower bound on the average reliability of fault-tolerant system components below which the system reliability begins to deteriorate as more versions are added

Dominant-negative ATF5 rapidly depletes survivin in tumor cells.

Survivin (BIRC5, product of the BIRC5 gene) is highly expressed in many tumor types and has been widely identified as a potential target for cancer therapy. However, effective anti-survivin drugs remain to be developed. Here we report that both vector-delivered and cell-penetrating dominant-negative (dn) forms of the transcription factor ATF5 that promote selective death of cancer cells in vitro and in vivo cause survivin depletion in tumor cell lines of varying origins. dn-ATF5 decreases levels of both survivin mRNA and protein. The depletion of survivin protein appears to be driven at least in part by enhanced proteasomal turnover and depletion of the deubiquitinase USP9X. Survivin loss is rapid and precedes the onset of cell death triggered by dn-ATF5. Although survivin downregulation is sufficient to drive tumor cell death, survivin over-expression does not rescue cancer cells from dn-ATF5-promoted apoptosis. This indicates that dn-ATF5 kills malignant cells by multiple mechanisms that include, but are not limited to, survivin depletion. Cell-penetrating forms of dn-ATF5 are currently being developed for potential therapeutic use and the present findings suggest that they may pose an advantage over treatments that target only survivin

Modeling neural dynamics during speech production using a state space variational autoencoder

Characterizing the neural encoding of behavior remains a challenging task in many research areas due in part to complex and noisy spatiotemporal dynamics of evoked brain activity. An important aspect of modeling these neural encodings involves separation of robust, behaviorally relevant signals from background activity, which often contains signals from irrelevant brain processes and decaying information from previous behavioral events. To achieve this separation, we develop a two-branch State Space Variational AutoEncoder (SSVAE) model to individually describe the instantaneous evoked foreground signals and the context-dependent background signals. We modeled the spontaneous speech-evoked brain dynamics using smoothed Gaussian mixture models. By applying the proposed SSVAE model to track ECoG dynamics in one participant over multiple hours, we find that the model can predict speech-related dynamics more accurately than other latent factor inference algorithms. Our results demonstrate that separately modeling the instantaneous speech-evoked and slow context-dependent brain dynamics can enhance tracking performance, which has important implications for the development of advanced neural encoding and decoding models in various neuroscience sub-disciplines.Comment: 5 page

Application of MML to motor skills acquisition

Study on modeling human psychomotor behaviour based on tracked motion data is reported. The motion data is acquired through various integrated inertial sensors, and represented as Euler angles and accelerations. The Minimum Message Length (MML) algorithm is used to identify frames of intrinsic segmentations and to acquire a classification basis for unsupervised machine learning. The classification model can ultimately be deployed in recognizing certain skilled behaviors. The prior results are analyzed as FSMs\u27 (Finite State Machines) to extract the potential rules underlying behaviors. The progress made so far and plan for further work is reported

Human behaviour recognition with segmented inertial data

The development and recent advancements of integrated inertial sensors has afforded substantive new possibilities for the acquisition and study of complex human motor skills and ultimately their imitation within robotic systems. This paper describes continuing work on kinetic models that are derived through unsupervised learning from a continuous stream of signals, including Euler angles and accelerations in three spatial dimensions, acquired from motions of a human arm. An intrinsic classification algorithm, MML (Minimum Message Length encoding) is used to segment the complex data, formulating a Gaussian Mixture Model of the dynamic modes it represents. Subsequent representation and analysis as FSM (Finite State Machines) has found distinguishing and consistent sequences of modes that persist across both, a variety of tasks as well as multiple candidates. An exemplary “standard” sequence for each behaviour can be abstracted from a corpus of suitable data and in turn utilised together with alignment techniques to identify behaviours of new sequences, as well as detail the homologous extent between each. The progress in contrast to previous work and future objectives are discussed

A Human Gut Commensal Ferments Cranberry Carbohydrates to Produce Formate

Commensal bifidobacteria colonize the human gastrointestinal tract and catabolize glycans that are impervious to host digestion. Accordingly, Bifidobacterium longum typically secretes acetate and lactate as fermentative end products. This study tested the hypothesis that B. longum utilizes cranberry-derived xyloglucans in a strain-dependent manner. Interestingly, the B. longum strain that efficiently utilizes cranberry xyloglucans secretes 2.0 to 2.5 mol of acetate-lactate. The 1.5 acetate:lactate ratio theoretical yield obtained in hexose fermentations shifts during xyloglucan metabolism. Accordingly, this metabolic shift is characterized by increased acetate and formate production at the expense of lactate. α-L-Arabinofuranosidase, an arabinan endo-1,5-α-L-arabinosidase, and a β-xylosidase with a carbohydrate substrate-binding protein and carbohydrate ABC transporter membrane proteins are upregulated (\u3e2-fold change), which suggests carbon flux through this catabolic pathway. Finally, syntrophic interactions occurred with strains that utilize carbohydrate products derived from initial degradation from heterologous bacteria