Entry effects of droplet in a micro confinement: implications for deformation-based CTC microfiltration

Deformation based circulating tumor cell (CTC) microchips are a representative diagnostic device for early cancer detection. This type of device usually involves a process of CTC trapping in a confined microgeometry. Further understanding of the CTC flow regime, as well as the threshold passing-through pressure is key to the design of deformation based CTC filtration devices. In the present numerical study, we investigate the transitional deformation and pressure signature from surface tension dominated flow to viscous shear stress dominated flow using a droplet model. Regarding whether CTC fully blocks the channel inlet, we observe two flow regimes: CTC squeezing and shearing regime. By studying the relation of CTC deformation at the exact critical pressure point for increasing inlet velocity, three different types of cell deformation are observed: 1) hemispherical front, 2) parabolic front, and 3) elongated CTC co-flowing with carrier media. Focusing on the circular channel, we observe a first increasing and then decreasing critical pressure change with increasing flow rate. By pressure analysis, the concept of optimum velocity is proposed to explain the behavior of CTC filtration and design optimization of CTC filter. Similar behavior is also observed in channels with symmetrical cross sessions like square and triangular but not in rectangular channels which only results in decreasing critical pressure

Direct Acoustics-to-Word Models for English Conversational Speech Recognition

Recent work on end-to-end automatic speech recognition (ASR) has shown that the connectionist temporal classification (CTC) loss can be used to convert acoustics to phone or character sequences. Such systems are used with a dictionary and separately-trained Language Model (LM) to produce word sequences. However, they are not truly end-to-end in the sense of mapping acoustics directly to words without an intermediate phone representation. In this paper, we present the first results employing direct acoustics-to-word CTC models on two well-known public benchmark tasks: Switchboard and CallHome. These models do not require an LM or even a decoder at run-time and hence recognize speech with minimal complexity. However, due to the large number of word output units, CTC word models require orders of magnitude more data to train reliably compared to traditional systems. We present some techniques to mitigate this issue. Our CTC word model achieves a word error rate of 13.0%/18.8% on the Hub5-2000 Switchboard/CallHome test sets without any LM or decoder compared with 9.6%/16.0% for phone-based CTC with a 4-gram LM. We also present rescoring results on CTC word model lattices to quantify the performance benefits of a LM, and contrast the performance of word and phone CTC models.Comment: Submitted to Interspeech-201

First reports of computed tomographic colonography for the screening of colorectal polyps in acromegalic patients

*Aim:* To analyze the CTC performance for the screening of colorectal polyps in acromegalic patients. 

*Materials and Methods:* A prospective study of 21 acromegalic patients, 12 male and 9 female, average age 49, who underwent CTC and CC. CTC was performed with a GE Helical Multislice Computed Tomography Apparatus. The colonoscopy was performed, in the same day, without previous knowledge of the CTC diagnostics. The study evaluated the capacity of CTC to detect patients with colorectal polyps and identify each colorectal lesion described by CC. 

*Results:* In two patients (2/21), CC was incomplete. However, in all patients CTC was complete. In Phase I (“Per Patient”), CTC diagnosed 8 of the 9 patients with colorectal polyps and showed 88% sensitivity, 75% specificity and 81% accuracy. In Phase II (“Per Polyp”), out of the 21 acromegalic patients included in this study, 12 presented normal findings at CC. A total of 19 polyps were identified in 9 patients. 10 of the 19 polyps were smaller than 10 mm, and 9 were equal to or larger than 10. CTC identified 7 of the 9 polyps ≥ 10 mm described by CC and only 6 of the 10 small polyps identified at CC were detected by CTC. The histological analysis of resected lesions revealed 12 tubular adenomas, 6 hyperplastic polyps and 1 colonic tubulo-villous adenoma with an adenocarcinoma focus. 

*Conclusion:* In this study, CTC was performed without complications and a complete and safe colorectal evaluation was possible in all acromegalic patients. Moreover, CTC showed good sensitivity to identify acromegalic patients with colorectal polyps

Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition

The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition. Separately, connectionist temporal classification (CTC) has matured as an alignment-free, non-autoregressive approach to sequence transduction, either by itself or in various multitask and decoding frameworks. We propose SAN-CTC, a deep, fully self-attentional network for CTC, and show it is tractable and competitive for end-to-end speech recognition. SAN-CTC trains quickly and outperforms existing CTC models and most encoder-decoder models, with character error rates (CERs) of 4.7% in 1 day on WSJ eval92 and 2.8% in 1 week on LibriSpeech test-clean, with a fixed architecture and one GPU. Similar improvements hold for WERs after LM decoding. We motivate the architecture for speech, evaluate position and downsampling approaches, and explore how label alphabets (character, phoneme, subword) affect attention heads and performance.Comment: Accepted to ICASSP 201

Can closed timelike curves or nonlinear quantum mechanics improve quantum state discrimination or help solve hard problems?

We study the power of closed timelike curves (CTCs) and other nonlinear extensions of quantum mechanics for distinguishing nonorthogonal states and speeding up hard computations. If a CTC-assisted computer is presented with a labeled mixture of states to be distinguished--the most natural formulation--we show that the CTC is of no use. The apparent contradiction with recent claims that CTC-assisted computers can perfectly distinguish nonorthogonal states is resolved by noting that CTC-assisted evolution is nonlinear, so the output of such a computer on a mixture of inputs is not a convex combination of its output on the mixture's pure components. Similarly, it is not clear that CTC assistance or nonlinear evolution help solve hard problems if computation is defined as we recommend, as correctly evaluating a function on a labeled mixture of orthogonal inputs.Comment: 4 pages, 3 figures. Final version. Added several references, updated discussion and introduction. Figure 1(b) very much enhance