Reduction method for dimensionally regulated one-loop N-point Feynman integrals

We present a systematic method for reducing an arbitrary one-loop N-point massless Feynman integral with generic 4-dimensional momenta to a set comprised of eight fundamental scalar integrals: six box integrals in D=6, a triangle integral in D=4, and a general two-point integral in D space time dimensions. All the divergences present in the original integral are contained in the general two-point integral and associated coefficients. The problem of vanishing of the kinematic determinants has been solved in an elegant and transparent manner. Being derived with no restrictions regarding the external momenta, the method is completely general and applicable for arbitrary kinematics. In particular, it applies to the integrals in which the set of external momenta contains subsets comprised of two or more collinear momenta, which are unavoidable when calculating one-loop contributions to the hard-scattering amplitude for exclusive hadronic processes at large momentum transfer in PQCD. The iterative structure makes it easy to implement the formalism in an algebraic computer program.Comment: 22 pages, 2 figures; one appendix added, discussions clarified, version to appear in Eur. Phys. J.

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

Embodied stress: The physiological resonance of psychosocial stress

Psychosocial stress is a ubiquitous phenomenon in our society. While acute stress responses are necessary and adaptive, excessive activation of neurobiological stress systems can predispose an individual to far-reaching adverse health outcomes. Living in a complex social environment, experiencing stress is not limited to challenges humans face individually. Possibly linked with our capacity for empathy, we also display the tendency to physiologically resonate with others’ stress responses. This recently identified source of stress raises many interesting questions. In comparison to the wealth of studies that have advanced our understanding of sharing others’ affective states, the physiological resonance of stress has only recently begun to be more closely investigated. The aim of the current paper is to review the existing literature surrounding the emerging area of “stress contagion”, “empathic stress” or “stress resonance”, as it has been variably called. After a brief introduction of the concepts of stress and empathy, we discuss several key studies that paved the way for the merging of empathy with the concept of physiological resonance. We then delineate recent empirical studies specifically focusing on the physiological resonance of stress. In the final section of this review, we highlight differences between these studies and discuss the variability in terminology used for what seems to be the same phenomenon. Lastly, potential health implications of chronic empathic stress are presented and possible mechanisms of physiological stress transmission are discussed

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