Fine Grain Incremental Rescheduling via Architectural Retiming".

Abstract With the decreasing feature sizes during VLSI fabrication and the dominance of interconnect delay over that of gates, control logic and wiring no longer have a negligible impact on delay and area. The need thus arises for developing techniques and tools to redesign incrementally to eliminate performance b ottlenecks. Such a redesign e ort corresponds to incrementally modifying an existing schedule obtained via high-level synthesis. In this paper we demonstrate that applying architectural retiming, a technique for pipelining latencyconstrained circuits, results in incrementally modifying an existing schedule. Architectural retiming reschedules ne grain operations ones that have a delay equal to or less than one clock cycle to occur in earlier time steps, while modifying the design to preserve its correctness

ASAP-SML: An Antibody Sequence Analysis Pipeline Using Statistical Testing and Machine Learning

Antibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibody-based inhibitors is the lack of fundamental information relating sequences of antibodies to their unique properties as inhibitors. We develop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML), to identify features that distinguish one set of antibody sequences from antibody sequences in a reference set. The pipeline extracts feature fingerprints from sequences. The fingerprints represent germline, CDR canonical structure, isoelectric point and frequent positional motifs. Machine learning and statistical significance testing techniques are applied to antibody sequences and extracted feature fingerprints to identify distinguishing feature values and combinations thereof. To demonstrate how it works, we applied the pipeline on sets of antibody sequences known to bind or inhibit the activities of matrix metalloproteinases (MMPs), a family of zinc-dependent enzymes that promote cancer progression and undesired inflammation under pathological conditions, against reference datasets that do not bind or inhibit MMPs. ASAP-SML identifies features and combinations of feature values found in the MMP-targeting sets that are distinct from those in the reference sets

MC3: a steady-state model and constraint consistency checker for biochemical networks

BACKGROUND: Stoichiometric models provide a structural framework for analyzing steady-state cellular behavior. Models are developed either through augmentations of existing models or more recently through automatic reconstruction tools. There is currently no standardized practice or method for validating the properties of a model before placing it in the public domain. Considerable effort is often required to understand a model’s inconsistencies before its reuse within new research efforts. RESULTS: We present a review of common issues in stoichiometric models typically uncovered during pathway analysis and constraint-based optimization, and we detail succinct and efficient ways to find them. We present MC(3), Model and Constraint Consistency Checker, a computational tool that can be used for two purposes: (a) identifying potential connectivity and topological issues for a given stoichiometric matrix, S, and (b) flagging issues that arise during constraint-based optimization. The MC(3) tool includes three distinct checking components. The first examines the results of computing the basis for the null space for Sv = 0; the second uses connectivity analysis; and the third utilizes Flux Variability Analysis. MC(3) takes as input a stoichiometric matrix and flux constraints, and generates a report summarizing issues. CONCLUSIONS: We report the results of applying MC(3) to published models for several systems including Escherichia coli, an adipocyte cell, a Chinese Hamster Ovary cell, and Leishmania major. Several issues with no prior documentation are identified. MC(3) provides a standalone MATLAB-based comprehensive tool for model validation, a task currently performed either ad hoc or implemented in part within other computational tools

On Separate Normalization in Self-supervised Transformers

Self-supervised training methods for transformers have demonstrated remarkable performance across various domains. Previous transformer-based models, such as masked autoencoders (MAE), typically utilize a single normalization layer for both the [CLS] symbol and the tokens. We propose in this paper a simple modification that employs separate normalization layers for the tokens and the [CLS] symbol to better capture their distinct characteristics and enhance downstream task performance. Our method aims to alleviate the potential negative effects of using the same normalization statistics for both token types, which may not be optimally aligned with their individual roles. We empirically show that by utilizing a separate normalization layer, the [CLS] embeddings can better encode the global contextual information and are distributed more uniformly in its anisotropic space. When replacing the conventional normalization layer with the two separate layers, we observe an average 2.7% performance improvement over the image, natural language, and graph domains.Comment: NIPS 202

Critical Path Analysis Using a Dynamically Bounded Delay Model

This paper focuses on static timing analysis in the presence of capacitive coupling. We propose a novel gate delay model, the dynamically bounded delay model. In contrast to the rain-max or bounded delay model which assumes a fixed delay range, [d,i,d .... ], for each circuit component, ore' new model allows for the specification of delay vex'iations and the conditions upon which the variations will hold. Novel static timing analysis algorithms can thus dynamically bound the delays. To demo nstrat e the effect iveness of this modal md a ppro ach, we use our model to perform critical path analysis in the presence of capacitive coupling. We foramlate this problem as a mixed integer' linear program. Our experiments show that ore' approach avoids pessimism when compm'ed to PERT analysis assuming worst case capacitive coupling