Thermophilic Sulfate Reduction in Hydrothermal Sediment of Lake Tanganyika, East Africa

In environments with temperatures above 60 degrees C, thermophilic prokaryotes are the only metabolically active life-forms. By using the (SO42-)-S-35 tracer technique, we studied the activity of sulfate-reducing microorganisms (SRM) in hot sediment from a hydrothermal vent site in the northern part of freshwater Lake Tanganyika (East Africa). Incubation of slurry samples at 8 to 90 degrees C demonstrated meso- and thermophilic sulfate reduction with optimum temperatures of 34 to 45 degrees C and 56 to 65 degrees C, respectively, and with an upper temperature limit of 80 degrees C. Sulfate reduction was stimulated at all temperatures by the addition of short-chain fatty acids and benzoate or complex substrates (yeast extract and peptone). A time course experiment showed that linear thermophilic sulfate consumption occurred after a lag phase (12 h) and indicated the presence of a large population of SRM in the hydrothermal sediment. Thermophilic sulfate reduction had a pH optimum of about 7 and was completely inhibited at pH 8.8 to 9.2. SRM could be enriched from hydrothermal chimney and sediment samples at 60 and 75 degrees C. In lactate-grown enrichments, sulfide production occurred at up to 70 and 75 degrees C, with optima at 63 and 71 degrees C, respectively. Several sporulating thermophilic enrichments were morphologically similar to Desulfotomaculum spp. Dissimilatory sulfate reduction in the studied hydrothermal area of Lake Tanganyika apparently has an upper temperature limit of 80 degrees C

Functionals in stochastic thermodynamics: how to interpret stochastic integrals

In stochastic thermodynamics standard concepts from macroscopic thermodynamics, such as heat, work, and entropy production, are generalized to small fluctuating systems by defining them on a trajectory-wise level. In Langevin systems with continuous state-space such definitions involve stochastic integrals along system trajectories, whose specific values depend on the discretization rule used to evaluate them (i.e. the 'interpretation' of the noise terms in the integral). Via a systematic mathematical investigation of this apparent dilemma, we corroborate the widely used standard interpretation of heat-and work-like functionals as Stratonovich integrals. We furthermore recapitulate the anomalies that are known to occur for entropy production in the presence of temperature gradients

Using Multi-Sense Vector Embeddings for Reverse Dictionaries

Popular word embedding methods such as word2vec and GloVe assign a single vector representation to each word, even if a word has multiple distinct meanings. Multi-sense embeddings instead provide different vectors for each sense of a word. However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word. In this work, we study the effect of multi-sense embeddings on the task of reverse dictionaries. We propose a technique to easily integrate them into an existing neural network architecture using an attention mechanism. Our experiments demonstrate that large improvements can be obtained when employing multi-sense embeddings both in the input sequence as well as for the target representation. An analysis of the sense distributions and of the learned attention is provided as well

Structural basis of TFIIH activation for nucleotide excision repair.

Nucleotide excision repair (NER) is the major DNA repair pathway that removes UV-induced and bulky DNA lesions. There is currently no structure of NER intermediates, which form around the large multisubunit transcription factor IIH (TFIIH). Here we report the cryo-EM structure of an NER intermediate containing TFIIH and the NER factor XPA. Compared to its transcription conformation, the TFIIH structure is rearranged such that its ATPase subunits XPB and XPD bind double- and single-stranded DNA, consistent with their translocase and helicase activities, respectively. XPA releases the inhibitory kinase module of TFIIH, displaces a 'plug' element from the DNA-binding pore in XPD, and together with the NER factor XPG stimulates XPD activity. Our results explain how TFIIH is switched from a transcription to a repair factor, and provide the basis for a mechanistic analysis of the NER pathway

Cascaded Boundary Regression for Temporal Action Detection

Temporal action detection in long videos is an important problem. State-of-the-art methods address this problem by applying action classifiers on sliding windows. Although sliding windows may contain an identifiable portion of the actions, they may not necessarily cover the entire action instance, which would lead to inferior performance. We adapt a two-stage temporal action detection pipeline with Cascaded Boundary Regression (CBR) model. Class-agnostic proposals and specific actions are detected respectively in the first and the second stage. CBR uses temporal coordinate regression to refine the temporal boundaries of the sliding windows. The salient aspect of the refinement process is that, inside each stage, the temporal boundaries are adjusted in a cascaded way by feeding the refined windows back to the system for further boundary refinement. We test CBR on THUMOS-14 and TVSeries, and achieve state-of-the-art performance on both datasets. The performance gain is especially remarkable under high IoU thresholds, e.g. map@tIoU=0.5 on THUMOS-14 is improved from 19.0% to 31.0%

Deep GrabCut for Object Selection

Most previous bounding-box-based segmentation methods assume the bounding box tightly covers the object of interest. However it is common that a rectangle input could be too large or too small. In this paper, we propose a novel segmentation approach that uses a rectangle as a soft constraint by transforming it into an Euclidean distance map. A convolutional encoder-decoder network is trained end-to-end by concatenating images with these distance maps as inputs and predicting the object masks as outputs. Our approach gets accurate segmentation results given sloppy rectangles while being general for both interactive segmentation and instance segmentation. We show our network extends to curve-based input without retraining. We further apply our network to instance-level semantic segmentation and resolve any overlap using a conditional random field. Experiments on benchmark datasets demonstrate the effectiveness of the proposed approaches.Comment: BMVC 201

Face Alignment Assisted by Head Pose Estimation

In this paper we propose a supervised initialization scheme for cascaded face alignment based on explicit head pose estimation. We first investigate the failure cases of most state of the art face alignment approaches and observe that these failures often share one common global property, i.e. the head pose variation is usually large. Inspired by this, we propose a deep convolutional network model for reliable and accurate head pose estimation. Instead of using a mean face shape, or randomly selected shapes for cascaded face alignment initialisation, we propose two schemes for generating initialisation: the first one relies on projecting a mean 3D face shape (represented by 3D facial landmarks) onto 2D image under the estimated head pose; the second one searches nearest neighbour shapes from the training set according to head pose distance. By doing so, the initialisation gets closer to the actual shape, which enhances the possibility of convergence and in turn improves the face alignment performance. We demonstrate the proposed method on the benchmark 300W dataset and show very competitive performance in both head pose estimation and face alignment.Comment: Accepted by BMVC201

Transductive Multi-label Zero-shot Learning

Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic representations in the form of attributes and more recently, semantic word vectors. However, they have thus far been constrained to the single-label case, in contrast to the growing popularity and importance of more realistic multi-label data. In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: how to exploit multi-label correlation at test time with no training data for those classes? In particular, we propose (1) a multi-output deep regression model to project an image into a semantic word space, which explicitly exploits the correlations in the intermediate semantic layer of word vectors; (2) a novel zero-shot learning algorithm for multi-label data that exploits the unique compositionality property of semantic word vector representations; and (3) a transductive learning strategy to enable the regression model learned from seen classes to generalise well to unseen classes. Our zero-shot learning experiments on a number of standard multi-label datasets demonstrate that our method outperforms a variety of baselines.Comment: 12 pages, 6 figures, Accepted to BMVC 2014 (oral