nils-herrmann/clean_scirepeval huggingface dataset:
Deformable trellises on factor graphs for robust microtubule tracking in clutter

A main challenge in microtubule tracking is due to clutter, or the presence of many similar intersecting structures. This paper proposes a two-layered probabilistic formulation which has at its foundation a factor graph serving as a multi-label inference engine designed to provide distinction between open contours of interest and other microtubules or noise. The second layer is a deformable trellis defined over the resulting label probability map, where a Hidden Markov Model (HMM) is employed to determine the most probable current location of the microtubule body. The overall framework enjoys the “best of both worlds” - the factor graph is effective in discriminating between contours of interest and others that exhibit similar statistical properties, while the deformable trellis with its HMM offer accurate modeling of microtubule dynamics in terms of growth and shortening, as well as precise body tracing, accounting for prior information, all within a principled Bayesian framework. Simulation results provide evidence that the proposed approach outperforms existing techniques.
Impacts of though-DRAM vias in 3D processor-DRAM integrated systems

As a promising option to address the memory wall problem, 3D processor-DRAM integration has recently received many attentions. Since DRAM tiers must be stacked between the processor tier and package substrate, we must fabricate a large number of through-DRAM through-silicon vias (TSVs) to connect the processor tier and package for power and I/O signal delivery. Although such through-DRAM TSVs will inevitably interfere with DRAM design and induce non-negligible power consumption overhead, little research has been done to study how to allocate these TSVs on the DRAM tiers and analyze their impacts. To address this open issue, this paper first presents a through-DRAM TSV allocation strategy that fits well to the regular DRAM architecture. To demonstrate this design strategy and evaluate trade-offs involved, we develop a CACTI-based modeling tool to carry out extensive simulations over a wide range of design parameters.
An Improved Tree-Structured Vector Quantizer

Tree-structured vector quantization (TSVQ) is an important alternative to full-search vector quantization since it reduces the encoding search complexity from O ( N ) to O(log(N)) , where N is the decoder codebook size, while incurring some additional distortion. Typically, the methods for designing trees are greedy, growing the trees outward from the root, node-by-node, by minimizing a suitable cost function at each node. The optimization at each non-leaf node does not, in general, reflect the node’s eventual role as a discriminant function, partitioning the input space in order to minimize the distortion incurred at the descendent leaves. In the splitting algorithm [l], for example, the test vectors at a non-leaf node are chosen to minimize the distortion incurred ut the given node, instead of minimizing the distortion at the leaves. The resulting tree may be improved simply by modifying individual nodes of the tree in order to achieve better agreement with nearest neighbor classification at the leaves. As an example, consider the TSVQ solutions of Figure 1. The sub-optimal solution of Figure l a was achieved
2D hidden Markov model with spatially adaptive state-space for tracing many cells in image sequence

In this paper we propose a two-dimensional hidden Markov model (HMM)-based framework for solving the cell tracing problem in a biological image sequence. Given label initialization in the first frame, we model the problem as pixel labeling for every consequent frame. Common Markov random field-based frameworks for this task require a fixed set of labels S = {1, 2,···, L}, while in our framework the set of labels or the state-space is spatially adaptive, i.e., available prior information is exploited to identify a smaller state-space that varies from node to node. In the cell tracing problem, specifically, temporal information on cell location in the previous frame is used to reduce the states to a small subset of the complete label set. The substantial reduction in average cardinality of the label set yields benefits not only in terms of computational complexity, but also in the labeling accuracy. The general idea can be broadly applied to many computer vision and image processing problems, where prior knowledge enables local reduction of the state-space. We consider the cell tracing problem on a publicly available challenging biological image dataset, which contains a series of electron microscopy images of high resolution and a large number of objects (neuronal processes) to be traced. Experimental results compare the approach with other recently proposed methods, and show considerable improvement.
On Constrained Randomized Quantization

Randomized (dithered) quantization is a method capable of achieving white reconstruction error independent of the source. Dithered quantizers have traditionally been considered within their natural setting of uniform quantization. In this paper we extend conventional dithered quantization to nonuniform quantization, via a subterfage: dithering is performed in the companded domain. Closed form necessary conditions for optimality of the compressor and expander mappings are derived for both fixed and variable rate randomized quantization. Numerically, mappings are optimized by iteratively imposing these necessary conditions. The resulting quantizer renders the reconstruction error white with negligible performance loss compared to the optimal quantizer. The framework is extended to include an explicit constraint that deterministic or randomized quantizers yield reconstruction error that is uncorrelated with the source. Surprising theoretical results show direct and simple connection between the optimal constrained quantizers and their unconstrained counterparts. Numerical results for the Gaussian source provide strong evidence that the proposed constrained randomized quantizer outperforms the conventional dithered quantizer, as well as the constrained deterministic quantizer.
Estimating interconnect performance for a new National Technology Roadmap for Semiconductors

The advent of smaller submicron feature sizes for CMOS technology has led to ever increasing device speeds but has singled out interconnects (which do not scale well) as the limiting constraint for microelectronics technology. This has led to extensive research in alternative materials as well as wiring configurations needed for interconnects to catch up with increasing clock speeds. Improvements in interconnect technology alone, i.e. new materials and interconnect configurations, will not provide the high performance anticipated by the 1997 NTRS. Changes in chip architecture and circuits are also required. This is where an early performance estimator is valuable. Large design spaces can be explored without committing to a particular configuration. Using the Rensselaer Interconnect Performance Estimator (RIPE), we show that it is relatively easy to evaluate the performance of proposed designs.
Decoupling capacitor modeling and characterization for power supply noise in 3D systems

Decoupling capacitors are essential to reduce high transient current noise and to provide a low impedance power delivery path. 3D technology has several advantages for power delivery, and this work investigates the impacts of decoupling capacitors on through-silicon-via (TSV)-based 3D power networks using a novel hybrid modeling approach, i.e., combining electromagnetic (EM) and SPICE simulations. We first partitioned a 3D system into a number of components, extracted the RLGC parasitics for each decomposed physical element, and imported them into an assembled system-level equivalent circuit. Through comparing and analyzing the effectiveness of several decoupling strategies, design tradeoffs are made in selecting the proper values and placement of decoupling capacitors to ensure optimal power distribution solution in 3D architectures.
Bidirectional cascaded long term prediction for frame loss concealment in polyphonic audio signals

This paper proposes a frame loss concealment technique for audio signals, which is designed to overcome the main challenge due to the polyphonic nature of most music signals and is inspired by our recent research on compression of such signals. The underlying idea is to employ a cascade of long term prediction filters (tailored to the periodic components) to circumvent the pitfalls of naive waveform repetition, and to enable effective time-domain prediction of every periodic component from the immediate history. In the first phase, a cascaded filter is designed from available past samples and is used to predict across the lost frame(s). Available future reconstructed samples allow refinement of the filter parameters to minimize the squared prediction error across such samples. In the second phase a prediction is similarly performed in reverse from future samples. Finally the lost frame is interpolated as a weighted average of forward and backward predicted samples. Objective and subjective evaluation results for the proposed approach, in comparison with existing techniques, all incorporated within an MPEG AAC low delay decoder, provide strong evidence for considerable gains across a variety of polyphonic signals.
Hierarchical, Unsupervised Learning with Growing via Phase Transitions

We address unsupervised learning subject to structural constraints, with particular emphasis placed on clustering with an imposed decision tree structure. Most known methods are greedy, optimizing one node of the tree at a time to minimize a local cost. By constrast, we develop a joint optimization method, derived based on information-theoretic principles and closely related to known methods in statistical physics. The approach is inspired by the deterministic annealing algorithm for unstructured data clustering, which was based on maximum entropy inference. The new approach is founded on the principle of minimum cross-entropy, using informative priors to approximate the unstructured clustering solution while imposing the structural constraint. The resulting method incorporates supervised learning principles applied in an unsupervised problem setting. In our approach, the tree grows by a sequence of bifurcations that occur while optimizing an effective free energy cost at decreasing temperature scales. Thus, estimates of the tree size and structure are naturally obtained at each temperature in the process. Examples demonstrate considerable improvement over known methods.
Graph capacities and zero-error transmission over compound channels

This correspondence investigates the behavior of the compound channel under a zero-error constraint. We derive expressions for the capacity when a) neither the encoder nor decoder has side-information about the channel; b) when only the encoder has such side-information. These expressions are given in terms of capacities of appropriately defined sets of graphs. We clarify that an earlier treatment of the zero-error capacity of a compound channel corresponds to the case where the decoder has side-information about the channel. We also characterize the minimum asymptotic rate for the source coding dual of the compound channel problem, namely, source coding with compound side information. Finally, we contrast the zero-error and asymptotically vanishing error capacities of the compound channel.
Asymptotic closed-loop design of error resilient predictive compression systems

Prediction is used in virtually all compression systems. When such a compressed signal is transmitted over unreliable networks, packet losses can lead to significant error propagation through the prediction loop. Despite this, the conventional design technique completely ignores the effect of packet losses, and estimates the prediction parameters to minimize the mean squared prediction error, and optimizes the quantizer to minimize the reconstruction error at the encoder. While some design techniques have been proposed to accurately estimate and minimize the end-to-end distortion at the decoder that accounts for packet losses, they operate in a closed-loop, which introduces a mismatch between statistics used for design and statistics used in operation, causing a negative impact on convergence and stability of the design procedure. Instead, we propose in this paper an effective technique for predictive compression system design that accounts for the instability caused by error propagation due to packet losses, and enjoys stable statistics during design by employing open-loop iterations that on convergence mimic closed-loop operation. Simulation results for a compression system with a first order linear predictor demonstrate the utility of the proposed approach, which offers significant performance improvements over existing design techniques.
On Transform Coding with Dithered Quantizers

This paper is concerned with optimal transform coding in conjunction with dithered quantization.While the optimal deterministic quantizer's error is uncorrelated with the reconstructed value, the dithered quantizer yields quantization errors that are correlated with the reconstruction but are white and independent of the source. These properties offer potential benefits, but also have implications on the optimization of the rest of the coder. We derive the optimal transform for consequent dithered quantization. For fixed rate coding, we show that the transform derived for dithered quantization is universally optimal (for all sources), unlike the conventional quantization case where optimality of the Karhunen-Loeve transform is guaranteed for Gaussian sources. Moreover, we establish variable rate coding optimality for Gaussian sources.
Towards optimum cost in multi-hop networks with arbitrary network demands

This paper considers the problem of minimizing the communication cost for a general multi-hop network with correlated sources and multiple sinks. For the single sink scenario, it has been shown that this problem can be decoupled, without loss of optimality, into two separate subproblems of distributed source coding and finding the optimal routing (transmission structure). It has further been established that, under certain assumptions, such decoupling also applies in the general case of multiple sinks and arbitrary network demands. We show that these assumptions are significantly restrictive, and further provide examples to substantiate the loss, including settings where removing the assumptions yields unbounded performance gains. Finally, an approach to solving the unconstrained problem, where routing and coding cannot be decoupled, is derived based on Han and Kobayashi's achievability region for multi-terminal coding.
Error exponents in scalable source coding

The characterization of the set of achievable rate and distortion values for scalable source coding is extended to additionally account for error exponents, namely, the negative normalized asymptotic log likelihood of error events at different layers. The "error" at each layer is defined as the event that the source block is not reproduced within the prespecified fidelity at the corresponding decoder. We consider separate error events at each layer so as to allow a tradeoff analysis for the error exponents when the rate and distortion values are fixed. For two-step coding of discrete memoryless sources, we derive a single-letter characterization of the region of all achievable 6-tuples (R/sub 1/, R/sub 2/, E/sub 1/, E/sub 2/, D/sub 1/, D/sub 2/), i.e., the rate, error exponent, and distortion levels at each layer. We also analyze the special case of successive refinability, where (R/sub 1/, E/sub 1/, D/sub 1/) and (R/sub 2/, E/sub 2/, D/sub 2/) individually achieve the nonscalable bounds. A surprising outcome of the analysis is that for any D/sub 1/, D/sub 2/, and E/sub 1/, there exists a finite threshold E/spl circ//sub 2//spl ges/E/sub 1/ such that successive refinability is ensured for all E/sub 2//spl ges/E/spl circ//sub 2/.
Optimization of Correlated Source Coding for Event-Based Monitoring in Sensor Networks

Motivated by the paradigm of event-based monitoring,which can potentially alleviate the inherent bandwidth and energy constraints associated with wireless sensor networks, we consider the problem of joint coding of correlated sources under a cost criterion that is appropriately conditioned on event occurrences. The underlying premise is that individual sensors only have access to partial information and, in general, cannot reliably detect events. Hence, sensors optimally compress and transmit the data to a fusion center, so as to minimize the {\emph{expected distortion in segments containing events}}. In this work, we derive and demonstrate the approach in the setting of entropy constrained distributed vector quantizer design,using a modified distortion criterion that appropriately accounts for the joint statistics of the events and the observation data. Simulation results show significant gains over conventional design as well as existing heuristic based methods, and provide experimental evidence to support the promise of our approach.
Source-channel prediction in error resilient video coding

Motion compensated prediction in conventional video coders is based on the encoder reconstruction of the previous frame where motion is estimated so as to minimize the encoder prediction error. Such design paradigms optimize the prediction at the encoder and ignore the effects of packet loss. In this paper, we propose a new prediction framework, which uses the expected decoder reconstruction of past frames for prediction. Moreover, the motion estimation criterion is accordingly modified to minimize the expected decoder prediction error. The approach builds on the recursive optimal per-pixel estimate (ROPE), which ensures accurate estimation of the expected values of decoder quantities. The proposed prediction scheme achieves better error resilience performance than its conventional counterpart.
A trellis-based optimal parameter value selection for audio coding

This paper considers the problem of selecting a set of parameter values from a given parameter space, in order to perform rate-distortion optimization in the context of audio compression. Due to interdependencies between parameters, separate optimization of parameter values is inherently suboptimal, yet a straightforward brute-force joint search involves prohibitive computational complexity. This work proposes a new method for joint rate-distortion optimization, while accounting for interparameter dependencies. The optimal solution is achieved, at significantly reduced complexity as compared to a brute-force search, by employing a Viterbi search over a trellis. Two objective distortion metrics are specifically considered: the average, and the maximum noise-to-mask ratio. Subjective (AB/MOS) and objective (average/maximum noise-to-mask ratio) tests demonstrate considerable gains at low bit rates of 16 kbps per channel for a 44.1-kHz sampled audio signal using the proposed approach.
On Complementary Graph Entropy

It has been recently discovered that complementary graph entropy characterizes (and offers new insights into) the minimum asymptotic rate for zero-error source coding with decoder side information. This paper presents new results that build on and complement this discovery. Specifically, i) previously unknown subadditivity properties of complementary graph entropy are derived, and ii) zero-error coding rates are characterized in terms of complementary graph entropy for two multiterminal coding scenarios. For both scenarios, the rate characterization implies no rate loss relative to point-to-point source coding.
Constrained-storage vector quantization with a universal codebook

Many compression applications consist of compressing multiple sources with significantly different distributions. In the context of vector quantization (VQ) these sources are typically quantized using separate codebooks. Since memory is limited in most applications, a convenient way to gracefully trade between performance and storage is needed. Earlier work addressed this problem by clustering the multiple sources into a small number of source groups, where each group shares a codebook. As a natural generalization, we propose the design of a size-limited universal codebook consisting of the union of overlapping source codebooks. This framework allows each source codebook to consist of any desired subset of the universal codevectors and provides greater design flexibility which improves the storage-constrained performance. Further advantages of the proposed approach include the fact that no two sources need be encoded at the same rate, and the close relation to universal, adaptive, and classified quantization. Necessary conditions for optimality of the universal codebook and the extracted source codebooks are derived. An iterative descent algorithm is introduced to impose these conditions on the resulting quantizer. Possible applications of the proposed technique are enumerated and its effectiveness is illustrated for coding of images using finite-state vector quantization.
Deterministically annealed mixture of experts models for statistical regression

A new and effective design method is presented for statistical regression functions that belong to the class of mixture models. The class includes the hierarchical mixture of experts (HME) and the normalized radial basis functions (NRBF). Design algorithms based on the maximum likelihood (ML) approach, which emphasize a probabilistic description of the model, have attracted much interest in HME and NRBF models. However, their design objective is mismatched to the original squared-error regression cost and the algorithms are easily trapped by poor local minima on the cost surface. In this paper, we propose an extension of the deterministic annealing (DA) method for the design of mixture-based regression models. We construct a probabilistic framework, but unlike the ML method, we directly optimize the squared-error regression cost, while avoiding poor local minima. Experimental results show that the DA method outperforms standard design methods for both HME and NRBF regression models.
On relaxing the strict hierarchical constraints in layered coding of audio signals

Scalable coders generate hierarchically layered bitstreams to serve content at different quality levels, wherein the base layer provides a coarse quality reconstruction and successive layers incrementally refine the quality. However, it is widely recognized that there is an inherent performance penalty due to the scalable coding structure, when compared to independently encoded copies. To mitigate this loss we propose a layered compression framework, having roots in information theoretic concepts, which relaxes the strict hierarchical constraints, wherein only a “subset” of the information of a lower quality level is shared with higher quality levels. In other words, there is flexibility to also have “private” information at each quality level, beside information that is common to multiple levels. We employ this framework within the MPEG Scalable AAC and propose an optimization scheme to jointly select parameters of all the layers. Experimental evaluation results demonstrate the utility of the flexibility provided by the proposed framework.