23,114 research outputs found
Quantum resonances and analysis of the survival amplitude in the nonlinear Winter's model
In this paper we show that the typical effects of quantum resonances, namely,
the exponential-type decay of the survival amplitude, continue to exist even
when a nonlinear perturbative term is added to the time-dependent Schroedinger
equation. The difficulty in giving a rigorous and appropriate definition of
quantum resonances by means of the notions already used for linear equations is
also highlighted.Comment: 31 pages, 8 figure
BRAMAC: Compute-in-BRAM Architectures for Multiply-Accumulate on FPGAs
Deep neural network (DNN) inference using reduced integer precision has been
shown to achieve significant improvements in memory utilization and compute
throughput with little or no accuracy loss compared to full-precision
floating-point. Modern FPGA-based DNN inference relies heavily on the on-chip
block RAM (BRAM) for model storage and the digital signal processing (DSP) unit
for implementing the multiply-accumulate (MAC) operation, a fundamental DNN
primitive. In this paper, we enhance the existing BRAM to also compute MAC by
proposing BRAMAC (Compute-in-AM
rchitectures for
ultiply-cumulate). BRAMAC supports
2's complement 2- to 8-bit MAC in a small dummy BRAM array using a hybrid
bit-serial & bit-parallel data flow. Unlike previous compute-in-BRAM
architectures, BRAMAC allows read/write access to the main BRAM array while
computing in the dummy BRAM array, enabling both persistent and tiling-based
DNN inference. We explore two BRAMAC variants: BRAMAC-2SA (with 2 synchronous
dummy arrays) and BRAMAC-1DA (with 1 double-pumped dummy array).
BRAMAC-2SA/BRAMAC-1DA can boost the peak MAC throughput of a large Arria-10
FPGA by 2.6/2.1, 2.3/2.0, and
1.9/1.7 for 2-bit, 4-bit, and 8-bit precisions, respectively at
the cost of 6.8%/3.4% increase in the FPGA core area. By adding
BRAMAC-2SA/BRAMAC-1DA to a state-of-the-art tiling-based DNN accelerator, an
average speedup of 2.05/1.7 and 1.33/1.52 can
be achieved for AlexNet and ResNet-34, respectively across different model
precisions.Comment: 11 pages, 13 figures, 3 tables, FCCM conference 202
Satisfiability of Non-Linear Transcendental Arithmetic as a Certificate Search Problem
For typical first-order logical theories, satisfying assignments have a
straightforward finite representation that can directly serve as a certificate
that a given assignment satisfies the given formula. For non-linear real
arithmetic with transcendental functions, however, no general finite
representation of satisfying assignments is available. Hence, in this paper, we
introduce a different form of satisfiability certificate for this theory,
formulate the satisfiability verification problem as the problem of searching
for such a certificate, and show how to perform this search in a systematic
fashion. This does not only ease the independent verification of results, but
also allows the systematic design of new, efficient search techniques.
Computational experiments document that the resulting method is able to prove
satisfiability of a substantially higher number of benchmark problems than
existing methods
Norsk rÄ kumelk, en kilde til zoonotiske patogener?
The worldwide emerging trend of eating ânaturalâ foods, that has not been
processed, also applies for beverages. According to Norwegian legislation, all
milk must be pasteurized before commercial sale but drinking milk that has
not been heat-treated, is gaining increasing popularity. Scientist are warning
against this trend and highlights the risk of contracting disease from milkborne
microorganisms. To examine potential risks associated with drinking
unpasteurized milk in Norway, milk- and environmental samples were
collected from dairy farms located in south-east of Norway. The samples
were analyzed for the presence of specific zoonotic pathogens; Listeria
monocytogenes, Campylobacter spp., and Shiga toxin-producing Escherichia
coli (STEC). Cattle are known to be healthy carriers of these pathogens, and
Campylobacter spp. and STEC have a low infectious dose, meaning that
infection can be established by ingesting a low number of bacterial cells. L.
monocytogenes causes one of the most severe foodborne zoonotic diseases,
listeriosis, that has a high fatality rate. All three pathogens have caused milk
borne disease outbreaks all over the world, also in Norway.
During this work, we observed that the prevalence of the three examined
bacteria were high in the environment at the examined farms. In addition, 7%
of the milk filters were contaminated by STEC, 13% by L. monocytogenes and
4% by Campylobacter spp. Four of the STEC isolates detected were eaepositive,
which is associated with the capability to cause severe human
disease. One of the eae-positive STEC isolates were collected from a milk
filter, which strongly indicate that Norwegian raw milk may contain potential
pathogenic STEC.
To further assess the possibilities of getting ill by STEC after consuming raw
milk, we examined the growth of the four eae-positive STEC isolates in raw milk at different temperatures. All four isolates seemed to have ability to multiply in raw milk at 8°C, and one isolate had significant growth after 72 hours. Incubation at 6°C seemed to reduce the number of bacteria during the
first 24 hours before cell death stopped. These findings highlight the
importance of stable refrigerator temperatures, preferable < 4°C, for storage
of raw milk.
The L. monocytogenes isolates collected during this study show genetic
similarities to isolates collected from urban and rural environmental
locations, but different clones were predominant in agricultural
environments compared to clinical and food environments. However, the
results indicate that the same clone can persist in a farm over time, and that
milk can be contaminated by L. monocytogenes clones present in farm
environment.
Despite testing small volumes (25 mL) of milk, we were able to isolate both
STEC and Campylobacter spp. directly from raw milk. A proportion of 3% of
the bulk tank milk and teat milk samples were contaminated by
Campylobacter spp. and one STEC was isolated from bulk tank milk. L
monocytogenes was not detected in bulk tank milk, nor in teat milk samples.
The agricultural evolvement during the past decades have led to larger
production units and new food safety challenges. Dairy cattle production in
Norway is in a current transition from tie-stall housing with conventional
pipeline milking systems, to modern loose housing systems with robotic
milking. The occurrence of the three pathogens in this project were higher in
samples collected from farms with loose housing compared to those with tiestall
housing.
Pasteurization of cowâs milk is a risk reducing procedure to protect
consumers from microbial pathogens and in most EU countries, commercial
distribution of unpasteurized milk is legally restricted. Together, the results
presented in this thesis show that the animal housing may influence the level
of pathogenic bacteria in the raw milk and that ingestion of Norwegian raw
cowâs milk may expose consumers to pathogenic bacteria which can cause
severe disease, especially in children, elderly and in persons with underlying
diseases. The results also highlight the importance of storing raw milk at low
temperatures between milking and consumption.Ă
spise mat som er mindre prosessert og mer «naturlig» er en pÄgÄende
trend i Norge og i andre deler av verden. Interessen for Ă„ drikke melk som
ikke er varmebehandlet, sÄkalt rÄ melk, er ogsÄ Þkende. I Norge er det pÄbudt
Ă„ pasteurisere melk fĂžr kommersielt salg for Ă„ beskytte forbrukeren mot
sykdomsfremkallende mikroorganismer. Fagfolk advarer mot Ä drikke rÄ
melk, og pÄpeker risikoen for Ä bli syk av patogene bakterier som kan finnes i
melken.
I denne avhandlingen undersĂžker vi den potensielle risikoen det medfĂžrer Ă„
drikke upasteurisert melk fra Norge. I tillegg til Ă„ samle inn tankmelk- og
speneprÞver fra melkegÄrder i sÞrÞst Norge, samlet vi ogsÄ miljÞprÞver fra
de samme gÄrdene for Ä kartlegge forekomst og for Ä identifisere potensielle
mattrygghetsrisikoer i melkeproduksjonen. Alle prĂžvene ble analysert for de
zoonotiske sykdomsfremkallende bakteriene Listeria monocytogenes,
Campylobacter spp., og Shiga toksin-produserende Escherichia coli (STEC).
Kyr kan vĂŠre friske smittebĂŠrere av disse bakteriene, som dermed kan
etablere et reservoar pÄ gÄrdene. Bakteriene kan overfÞres fra gÄrdsmiljÞet
til melkekjeden og dermed utfordre mattryggheten. Disse bakteriene har
forÄrsaket melkebÄrne sykdomsutbrudd over hele verden, ogsÄ i Norge.
Campylobacter spp. og STEC har lav infeksiĂžs dose, som vil si at man kan bli
syk selv om man bare inntar et lavt antall bakterieceller. L. monocytogenes
kan gi sykdommen listeriose, en av de mest alvorlige matbÄrne zoonotiske
sykdommene vi har i den vestlige verden.
Resultater fra denne oppgaven viser en hĂžy forekomst av de tre patogenene i
gÄrdsmiljÞet. I tillegg var 7% av melkefiltrene vi testet positive for STEC, 13%
positive for L. monocytogenes og 4% positive for Campylobacter spp.. Fire av
STEC isolatene bar genet for Intimin, eae, som er ansett som en viktig
virulensfaktor som Ăžker sjansen for alvorlig sykdom. Ett av de eae-positive
isolatene ble funnet i et melkefilter, noe som indikerer at norsk rÄ melk kan
inneholde patogene STEC. For Ă„ videre vurdere risikoen for Ă„ bli syk av STEC
fra rÄ melk undersÞkte vi hvordan de fire eae-positive isolatene vokste i rÄ
melk lagret ved forskjellige temperaturer. For alle isolatene Ăžkte antall
bakterier etter lagring ved 8°C, og for et isolat var veksten signifikant. Etter
lagring ved 6°C ble antallet bakterier redusert de fÞrste 24 timene, deretter
stoppet reduksjonen i antall bakterier. Disse resultatene viser hvor viktig det
er Ä ha stabil lav lagringstemperatur for rÄ melk, helst < 4°C.
L. monocytogenes isolatene som ble samlet inn fra melkegÄrdene viste
genetiske likheter med isolater samlet inn fra urbane og rurale miljĂžer rundt
omkring i Norge. Derimot var kloner som dominerte i landbruksmiljĂžet
forskjellige fra kliniske isolater og isolater fra matproduksjonslokaler. Videre
sÄ man at en klone kan persistere pÄ en gÄrd over tid og at melk kan
kontamineres av L. monocytogenes kloner som er til stede i gÄrdsmiljÞet.
Til tross for smÄ testvolum av tankmelken (25 mL) fant vi bÄde STEC og
Campylobacter spp. i melkeprĂžvene. 3% av tankmelkprĂžvene og
speneprĂžvene var positive for Campylobacter spp. og ett STEC isolat ble
funnet i tankmelk. L. monocytogenes ble ikke funnet direkte i melkeprĂžvene.
Landbruket i Norge er i stadig utvikling der besetningene blir stĂžrre, men
fĂŠrre. Melkebesetningene er midt i en overgang der tradisjonell oppstalling
med melking pÄ bÄs byttes ut med lÞsdriftssystemer og melkeroboter.
Forekomsten av de tre patogenene funnet i denne studien var hĂžyere i
besetningene med lĂžsdrift sammenliknet med besetningene som hadde
melkekyrne oppstallet pÄ bÄs.
Pasteurisering er et viktig forebyggende tiltak for Ă„ beskytte konsumenter fra
mikrobielle patogener, og i de fleste EU-land er kommersielt salg av rÄ melk
juridisk begrenset. Denne studien viser at oppstallingstype kan pÄvirke
nivÄene av patogene bakterier i gÄrdsmiljÞet og i rÄ melk. Inntak av rÄ melk
kan eksponere forbruker for patogene bakterier som kan gi alvorlig sykdom,
spesielt hos barn, eldre og personer med underliggende sykdommer.
Resultatene underbygger viktigheten av Ă„ pasteurisere melk for Ă„ sikre
mattryggheten, og at det er avgjÞrende Ä lagre rÄ melk ved kontinuerlig lave
temperaturer for Ă„ forebygge vekst av zoonotiske patogener
Boosting the Cycle Counting Power of Graph Neural Networks with I-GNNs
Message Passing Neural Networks (MPNNs) are a widely used class of Graph
Neural Networks (GNNs). The limited representational power of MPNNs inspires
the study of provably powerful GNN architectures. However, knowing one model is
more powerful than another gives little insight about what functions they can
or cannot express. It is still unclear whether these models are able to
approximate specific functions such as counting certain graph substructures,
which is essential for applications in biology, chemistry and social network
analysis. Motivated by this, we propose to study the counting power of Subgraph
MPNNs, a recent and popular class of powerful GNN models that extract rooted
subgraphs for each node, assign the root node a unique identifier and encode
the root node's representation within its rooted subgraph. Specifically, we
prove that Subgraph MPNNs fail to count more-than-4-cycles at node level,
implying that node representations cannot correctly encode the surrounding
substructures like ring systems with more than four atoms. To overcome this
limitation, we propose I-GNNs to extend Subgraph MPNNs by assigning
different identifiers for the root node and its neighbors in each subgraph.
I-GNNs' discriminative power is shown to be strictly stronger than Subgraph
MPNNs and partially stronger than the 3-WL test. More importantly, I-GNNs
are proven capable of counting all 3, 4, 5 and 6-cycles, covering common
substructures like benzene rings in organic chemistry, while still keeping
linear complexity. To the best of our knowledge, it is the first linear-time
GNN model that can count 6-cycles with theoretical guarantees. We validate its
counting power in cycle counting tasks and demonstrate its competitive
performance in molecular prediction benchmarks
PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators
Analog compute-in-memory (CIM) accelerators are becoming increasingly popular
for deep neural network (DNN) inference due to their energy efficiency and
in-situ vector-matrix multiplication (VMM) capabilities. However, as the use of
DNNs expands, protecting user input privacy has become increasingly important.
In this paper, we identify a security vulnerability wherein an adversary can
reconstruct the user's private input data from a power side-channel attack,
under proper data acquisition and pre-processing, even without knowledge of the
DNN model. We further demonstrate a machine learning-based attack approach
using a generative adversarial network (GAN) to enhance the reconstruction. Our
results show that the attack methodology is effective in reconstructing user
inputs from analog CIM accelerator power leakage, even when at large noise
levels and countermeasures are applied. Specifically, we demonstrate the
efficacy of our approach on the U-Net for brain tumor detection in magnetic
resonance imaging (MRI) medical images, with a noise-level of 20% standard
deviation of the maximum power signal value. Our study highlights a significant
security vulnerability in analog CIM accelerators and proposes an effective
attack methodology using a GAN to breach user privacy
Hierarchical distributed scenario-based model predictive control of interconnected microgrids
Microgrids are autonomous clusters of generators, storage units and loads.
Special requirements arise in interconnected operation: control schemes that do
not require individual microgrids to disclose data about their internal
structure and operating objectives are preferred for privacy reasons. Moreover,
a safe and economically meaningful operation shall be achieved in presence of
uncertain load and weather-dependent availability of renewable infeed. In this
paper, we propose a distributed model predictive control approach that
satisfies these requirements. Specifically, we demonstrate that costs and
safety of supply can be improved through a scenario-based stochastic control
scheme. In a numerical case study, our approach is compared to a certainty
equivalence scheme. The results illustrate the improved safety and reduced
runtime costs as well as sufficiently fast convergence
Genome diversity of Leishmania aethiopica
Leishmania aethiopica is a zoonotic Old World parasite transmitted by Phlebotomine sand flies and causing cutaneous leishmaniasis in Ethiopia and Kenya. Despite a range of clinical manifestations and a high prevalence of treatment failure, L. aethiopica is one of the most neglected species of the Leishmania genus in terms of scientific attention. Here, we explored the genome diversity of L. aethiopica by analyzing the genomes of twenty isolates from Ethiopia. Phylogenomic analyses identified two strains as interspecific hybrids involving L. aethiopica as one parent and L. donovani and L. tropica respectively as the other parent. High levels of genome-wide heterozygosity suggest that these two hybrids are equivalent to F1 progeny that propagated mitotically since the initial hybridization event. Analyses of allelic read depths further revealed that the L. aethiopica - L. tropica hybrid was diploid and the L. aethiopica - L. donovani hybrid was triploid, as has been described for other interspecific Leishmania hybrids. When focusing on L. aethiopica, we show that this species is genetically highly diverse and consists of both asexually evolving strains and groups of recombining parasites. A remarkable observation is that some L. aethiopica strains showed an extensive loss of heterozygosity across large regions of the nuclear genome, which likely arose from gene conversion/mitotic recombination. Hence, our prospection of L. aethiopica genomics revealed new insights into the genomic consequences of both meiotic and mitotic recombination in Leishmania
-invariant subspaces in growth spaces, boundary zero sets and model spaces
We investigate certain classes of -invariant subspaces for a wide range
of growth spaces on the unit disc determined by a majorant ,
which include the classical Korenblum growth spaces. Our main result
generalizes the classical Korenblum-Roberts Theorem on the description of
-invariant subspaces generated by bounded analytic functions, in terms of
the corresponding Nevanlinna measure. It turns out that sets of finite
-entropy, which are boundary zero sets for analytic functions in
having modulus of continuity not exceeding on
, play the decisive role in this setting. This further
enables us to establish an intimate link between -invariant subspace
generated by inner functions and the containment of the above
mentioned analytic function spaces in the corresponding model spaces
.Comment: 29 page
Discovering the hidden structure of financial markets through bayesian modelling
Understanding what is driving the price of a financial asset is a question that is currently mostly unanswered. In this work we go beyond the classic one step ahead prediction and instead construct models that create new information on the behaviour of these time series. Our aim is to get a better understanding of the hidden structures that drive the moves of each financial time series and thus the market as a whole.
We propose a tool to decompose multiple time series into economically-meaningful variables to explain the endogenous and exogenous factors driving their underlying variability. The methodology we introduce goes beyond the direct model forecast. Indeed, since our model continuously adapts its variables and coefficients, we can study the time series of coefficients and selected variables. We also present a model to construct the causal graph of relations between these time series and include them in the exogenous factors.
Hence, we obtain a model able to explain what is driving the move of both each specific time series and the market as a whole. In addition, the obtained graph of the time series provides new information on the underlying risk structure of this environment. With this deeper understanding of the hidden structure we propose novel ways to detect and forecast risks in the market. We investigate our results with inferences up to one month into the future using stocks, FX futures and ETF futures, demonstrating its superior performance according to accuracy of large moves, longer-term prediction and consistency over time. We also go in more details on the economic interpretation of the new variables and discuss the created graph structure of the market.Open Acces
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