4,313 research outputs found
Femtosecond x-ray absorption spectroscopy of spin and orbital angular momentum in photoexcited Ni films during ultrafast demagnetization
We follow for the first time the evolution of the spin and orbital angular
momentum of a thin Ni film during ultrafast demagnetization, by means of x-ray
magnetic circular dichroism. Both components decrease with a 130 +/- 40 fs time
constant upon excitation with a femtosecond laser pulse. Additional x-ray
absorption measurements reveal an increase in the spin-orbit interaction by 6
+/- 2 % during this process. This is the experimental demonstration quantifying
the importance of spin-orbit mediated processes during the demagnetization
Exposing Fake Images with Forensic Similarity Graphs
We propose new image forgery detection and localization algorithms by
recasting these problems as graph-based community detection problems. To do
this, we introduce a novel abstract, graph-based representation of an image,
which we call the Forensic Similarity Graph, that captures key forensic
relationships among regions in the image. In this representation, small image
patches are represented by graph vertices with edges assigned according to the
forensic similarity between patches. Localized tampering introduces unique
structure into this graph, which aligns with a concept called ``community
structure'' in graph-theory literature. In the Forensic Similarity Graph,
communities correspond to the tampered and unaltered regions in the image. As a
result, forgery detection is performed by identifying whether multiple
communities exist, and forgery localization is performed by partitioning these
communities. We present two community detection techniques, adapted from
literature, to detect and localize image forgeries. We experimentally show that
our proposed community detection methods outperform existing state-of-the-art
forgery detection and localization methods, which do not capture such community
structure.Comment: 16 pages, under review at IEEE Journal of Selected Topics in Signal
Processin
Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces
Recent advances in deep learning have enabled forensics researchers to
develop a new class of image splicing detection and localization algorithms.
These algorithms identify spliced content by detecting localized
inconsistencies in forensic traces using Siamese neural networks, either
explicitly during analysis or implicitly during training. At the same time,
deep learning has enabled new forms of anti-forensic attacks, such as
adversarial examples and generative adversarial network (GAN) based attacks.
Thus far, however, no anti-forensic attack has been demonstrated against image
splicing detection and localization algorithms. In this paper, we propose a new
GAN-based anti-forensic attack that is able to fool state-of-the-art splicing
detection and localization algorithms such as EXIF-Net, Noiseprint, and
Forensic Similarity Graphs. This attack operates by adversarially training an
anti-forensic generator against a set of Siamese neural networks so that it is
able to create synthetic forensic traces. Under analysis, these synthetic
traces appear authentic and are self-consistent throughout an image. Through a
series of experiments, we demonstrate that our attack is capable of fooling
forensic splicing detection and localization algorithms without introducing
visually detectable artifacts into an attacked image. Additionally, we
demonstrate that our attack outperforms existing alternative attack approaches.
Role of critical spin fluctuations in ultrafast demagnetization of transition-metal rare-earth alloys
Ultrafast magnetization dynamics induced by femtosecond laser pulses have
been measured in ferrimagnetic Co0.8Gd0.2, Co.74Tb.26 and Co.86Tb.14 alloys.
Using element sensitivity of X-ray magnetic circular dichroism at the Co L3, Tb
M5 and Gd M5 edges we evidence that the demagnetization dynamics is element
dependent. We show that a thermalization time as fast as 280 fs is observed for
the rare-earth in the alloy, when the laser excited state temperature is below
the compensation temperature. It is limited to 500 fs when the laser excited
state temperature is below the Curie temperature (Tc). We propose critical spin
fluctuations in the vicinity of TC as the mechanism which reduces the
demagnetization rates of the 4f electrons in transition-metal rare-earth alloys
whereas at any different temperature the limited demagnetization rates could be
avoided.Comment: 11 pages, 4 figure
Open Set Synthetic Image Source Attribution
AI-generated images have become increasingly realistic and have garnered
significant public attention. While synthetic images are intriguing due to
their realism, they also pose an important misinformation threat. To address
this new threat, researchers have developed multiple algorithms to detect
synthetic images and identify their source generators. However, most existing
source attribution techniques are designed to operate in a closed-set scenario,
i.e. they can only be used to discriminate between known image generators. By
contrast, new image-generation techniques are rapidly emerging. To contend with
this, there is a great need for open-set source attribution techniques that can
identify when synthetic images have originated from new, unseen generators. To
address this problem, we propose a new metric learning-based approach. Our
technique works by learning transferrable embeddings capable of discriminating
between generators, even when they are not seen during training. An image is
first assigned to a candidate generator, then is accepted or rejected based on
its distance in the embedding space from known generators' learned reference
points. Importantly, we identify that initializing our source attribution
embedding network by pretraining it on image camera identification can improve
our embeddings' transferability. Through a series of experiments, we
demonstrate our approach's ability to attribute the source of synthetic images
in open-set scenarios
VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces
Fake videos represent an important misinformation threat. While existing
forensic networks have demonstrated strong performance on image forgeries,
recent results reported on the Adobe VideoSham dataset show that these networks
fail to identify fake content in videos. In this paper, we show that this is
due to video coding, which introduces local variation into forensic traces. In
response, we propose VideoFACT - a new network that is able to detect and
localize a wide variety of video forgeries and manipulations. To overcome
challenges that existing networks face when analyzing videos, our network
utilizes both forensic embeddings to capture traces left by manipulation,
context embeddings to control for variation in forensic traces introduced by
video coding, and a deep self-attention mechanism to estimate the quality and
relative importance of local forensic embeddings. We create several new video
forgery datasets and use these, along with publicly available data, to
experimentally evaluate our network's performance. These results show that our
proposed network is able to identify a diverse set of video forgeries,
including those not encountered during training. Furthermore, we show that our
network can be fine-tuned to achieve even stronger performance on challenging
AI-based manipulations
Möglichkeiten zur Attraktivitätssteigerung der Hausarztmedizin aus der Sicht junger Ärztinnen und Ärzte
Hintergrund: Ziel der Studie ist es zu untersuchen, welche berufliche Laufbahn junge Ärztinnen und Ärzte gegen Ende ihrer fachärztlichen Weiterbildung anstreben und welche Faktoren aus ihrer Sicht eine Tätigkeit in der ärztlichen Grundversorgung attraktiver machen könnten.
Methodik: Im Rahmen einer seit 2001 laufenden Schweizer prospektiven Studie zu Determinanten der Karriereentwicklung nahmen 534 junge Ärztinnen und Ärzte im Jahr 2007 an der vierten Befragung teil. Sie machten Angaben zur angestrebten beruflichen Laufbahn, zum geplanten Praxismodell und Praxisstandort, ferner benannten sie Faktoren, die für bzw. gegen die Hausarztmedizin sprechen und welche die Attraktivität der Hausarztmedizin steigern würden.
Ergebnisse: 84 Personen (42% Männer, 58% Frauen) streben eine Tätigkeit als Hausarzt/-ärztin an (60% spezialisieren sich in Allgemeinmedizin, 40% in Allgemeiner Innerer Medizin), 450 spezialisieren sich in einem anderen Fachgebiet. Von den 534 Studienteilnehmenden möchten 208 später in einer Praxis arbeiten, mehrheitlich in einer Gruppenpraxis (88%). 49% der zukünftigen Hausärzte planen eine Praxis in einer Stadt, von den Spezialisten sind es 77%. Als wesentliche Gründe gegen die Hausarztmedizin werden die unsichere Entwicklung der Hausarztmedizin und das niedrige Einkommen genannt, als positive Faktoren die Vielseitigkeit, das breite Patientenspektrum sowie die Kontinuität der Arzt-Patient-Beziehung. Um die Attraktivität der Hausarztmedizin zu steigern, müssten interdisziplinäre Gruppenpraxen gefördert und die finanziellen Rahmenbedingungen verbessert werden.
Schlussfolgerung: Die Hausarztmedizin wird als interessantes Berufsfeld eingeschätzt, die gegenwärtigen Rahmenbedingungen für die Ausübung der hausärztlichen Tätigkeit wirken jedoch abschreckend
Mesenchymal Stem Cells and Inflammatory Cardiomyopathy: Cardiac Homing and Beyond
Under conventional heart failure therapy, inflammatory cardiomyopathy usually has a progressive course, merging for alternative interventional strategies. There is accumulating support for the application of cellular transplantation as a strategy to improve myocardial function. Mesenchymal stem cells (MSCs) have the advantage over other stem cells that they possess immunomodulatory features, making them attractive candidates for the treatment of inflammatory cardiomyopathy. Studies in experimental models of inflammatory cardiomyopathy have consistently demonstrated the potential of MSCs to reduce cardiac injury and to improve cardiac function. This paper gives an overview about how inflammation triggers the functionality of MSCs and how it induces cardiac homing. Finally, the potential of intravenous application of MSCs by inflammatory cardiomyopathy is discussed
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