707 research outputs found
Families and states: citizenship and demography in the Greco-Roman world
This paper investigates the interrelationship between states and families. At different levels of organization, both play a large role in shaping the context in which individuals live their lives. Yet when it comes to understanding key demographic events in the ancient Mediterranean world – birth, marriage, migration, family structures, and death – they are hardly brought together. In this paper, I argue that Greek and Roman demographic patterns were tightly connected with their own specific political-institutional frameworks that developed over the course of (city-)state formation processes. This interaction was shaped in particular by the emergence of diverging notions of citizenship in the Greek and the Roman world, which went hand in hand with the installment of disparate incentives and disincentives to certain demographic behaviors. Differing citizenship criteria, in other words, invoked different demographic behaviors. A ‘political demography’ perspective, therefore, helps us understand how and why Greek and Roman individuals selected their marriage candidates on different criteria, and sheds light on divergences in their respective emphases on extended family ties.
Exploiting Features and Logits in Heterogeneous Federated Learning
Due to the rapid growth of IoT and artificial intelligence, deploying neural
networks on IoT devices is becoming increasingly crucial for edge intelligence.
Federated learning (FL) facilitates the management of edge devices to
collaboratively train a shared model while maintaining training data local and
private. However, a general assumption in FL is that all edge devices are
trained on the same machine learning model, which may be impractical
considering diverse device capabilities. For instance, less capable devices may
slow down the updating process because they struggle to handle large models
appropriate for ordinary devices. In this paper, we propose a novel data-free
FL method that supports heterogeneous client models by managing features and
logits, called Felo; and its extension with a conditional VAE deployed in the
server, called Velo. Felo averages the mid-level features and logits from the
clients at the server based on their class labels to provide the average
features and logits, which are utilized for further training the client models.
Unlike Felo, the server has a conditional VAE in Velo, which is used for
training mid-level features and generating synthetic features according to the
labels. The clients optimize their models based on the synthetic features and
the average logits. We conduct experiments on two datasets and show
satisfactory performances of our methods compared with the state-of-the-art
methods
A New Class of Materials Based on Nanoporous High Entropy Alloys with Outstanding Properties
Nanoporous metals with a random, bicontinuous structure of both pores and
ligaments exhibit many unique mechanical properties, but their technical
applications are often limited by their intrinsic brittleness under tensile
strain triggered by fracture of the weakest ligaments. Here, we use molecular
dynamics simulations to study the mechanical behavior and thermal stability of
two different bicontinuous nanoporous high entropy alloys, Al0.1CoCrFeNi and
NbMoTaW. To isolate the properties related to the nanoporous nature of our
samples, we also studied the corresponding bulk and nanocrystalline systems.
The results demonstrate that the specific modulus of nanoporous HEAs are 2 to 3
times greater than that of single element nanoporous materials with specific
strength reaching values 5 to 10 times higher, comparable to bulk metals with
the highest specific strength. Bicontinuous HEAs also displayed excellent
resistance to thermal degradation as evidenced by the absence of coarsening
ligaments up to temperatures of 1273 K which ensures the durability and
reliability in high-temperature applications. The findings uncover
unprecedented mechanical and thermal properties of bicontinuous nanoporous high
entropy alloys, paving the way for their promising utilization in advanced
engineering and structural applications
Understanding and Preventing Falls: Perspectives of First Responders and Older Adults
OBJECTIVE. The objectives of this study were to identify characteristics of older adult fallers in a local community in Marin County, California, examine the perceptions of older adults who contacted a local fire district after a fall, examine the perceptions of first responders from a local fire district regarding falls and fall prevention, explore the degree of depression in older adult fallers, and identify strategies to prevent falls in older adults.
METHODS. This research study was an exploratory and retrospective descriptive study that utilized a mixed-method design. The researchers coded narratives from Patient Care Report (PCRs) provided by the fire district and also quantitatively analyzed PCRs to identify characteristics of older adult fallers. Researchers also qualitatively analyzed data gathered from focus groups with older adults and first responders and from phone interviews with community-dwelling older adults to understand their experiences regarding falls and fall prevention.
RESULTS. Findings revealed that the majority of fallers were female, at an average age of 81 years old, living at home and alone during the fall. Older adult participants associated falls with negative emotions and expressed a strong desire to maintain their independence despite experiencing falls and fall injuries. First responder participants experienced challenges when communicating with older adult fallers due to cognitive and psychosocial factors. The lack of coordination of services with care facility staff also posed a challenge for first responder participants.
CONCLUSION. As the older adult population increases, more older adults will fall and require emergency care from first responders. A collaboration between first responders and occupational therapists to develop and implement effective fall prevention programs for the community can potentially reduce falls and fall-related injuries and costs and improve the health and well-being of older adults
Bond option pricing under the CKLS model
Consider the European call option written on a zero
coupon bond. Suppose the call option has maturity T and
strike price K while the bond has maturity S T . We propose a numerical method for evaluating the call option
price under the Chan, Karolyi, Longstaff and Sanders (CKLS)
model in which the increment of the short rate over a time
interval of length dt , apart from being independent and
stationary, is having the quadratic-normal distribution with
mean zero and variance dt. The key steps in the numerical
procedure include (i) the discretization of the CKLS model;
(ii) the quadratic approximation of the time-T bond price as a function of the short rate rT at time T; and (iii) the
application of recursive formulas to find the moments of
r(t+dt) given the value of r(t). The numerical results thus
found show that the option price decreases as the parameter
in the CKLS model increases, and the variation of the
option price is slight when the underlying distribution of the increment departs from the normal distribution
Predicting Coupled Electron and Phonon Transport Using Steepest-Entropy-Ascent Quantum Thermodynamics
The current state of the art for determining thermoelectric properties is
limited to the investigation of electrons or phonons without including the
inherent electron-phonon coupling that is in all materials. This gives rise to
limitations in accurately calculating base material properties that are in good
agreement with experimental data. Steepest-entropy-ascent quantum
thermodynamics is a general non-equilibrium thermodynamic ensemble framework
that provides a general equation of motion for non-equilibrium system state
evolution. This framework utilizes the electron and phonon density of states as
input to compute material properties, while taking into account the
electron-phonon coupling. It is able to span across multiple spatial and
temporal scales in a single analysis. Any system's thermoelectric properties
can, therefore, be attained provided the accurately determined density of
states is available.Comment: Supplementary Materials Section is the last two pages of the
manuscrip
Investigation of the nature of the oxidant (selective and unselective) in/on a vanadyl pyrophosphate catalyst
The anaerobic oxidation of CO by a (VO)2P2O7 catalyst has been used to investigate the nature of the oxidant (selective and unselective) in/on that material. Three peaks were observed in the rate of production of CO2 - at 993, 1073 and 1093 K. The temperature of the maximum in the rate of production of the first CO2 peak and the amount of oxygen associated with it are the same as that observed in the selective anaerobic oxidation of n-butane to butene and butadiene, but-1-ene to butadiene and furan and but-1,3-diene to dihydrofuran, furan and maleic anhydride. The interaction of CO with the (VO)2P2O7 catalyst forming CO2 at 993 K is therefore concluded to be with the selective oxygen. The total amount of oxygen removed by the CO from the (VO)2P2O7 lattice (>5 monolayers) is about six times greater than that of the selective oxygen. The higher activation energies for the removal of the unselective oxygen accounts for the high selectivities (~80%) encountered commercially for the anaerobic oxidation of n-butane to maleic anhydride. Re-oxidation of the CO reduced (VO)2P2O7 by N2O quantitatively replaces all of the lattice oxygen removed by the formation of CO2, but does not restore the original morphology
On the mechanism of the selective oxidation of n-butane, but-1-ene and but-1,3-diene to maleic anhydride over a vanadyl pyrophosphate catalyst
The mechanism of the selective partial oxidation of n-butane, but-1-ene and but-1,3-diene over a vanadyl phosphate catalyst has been investigated by temperature-programmed desorption (TPD) and by anaerobic temperature-programmed oxidation (TPO). TPD showed lattice oxygen to be desorbed in two states at 998 and 1023 K. The anaerobic TPO of n-butane produced butene and butadiene at 1020 K; anaerobic TPO of but-1-ene produced butadiene and furan at 990 K and dehydrofuran at 965 K, while anaerobic TPO of but-1,3-diene produced dehydrofuran at 970 K, furan at 1002 K and maleic anhydride at 1148 K. The total amount of oxygen removed from the lattice in these anaerobic selective partial oxidations was the same as that evolved from the vanadyl phosphate catalyst by TPD. This, and the fact that the selective oxidation reactions occurred at the same temperature at which the oxygen evolves from the lattice, suggests that the lattice oxygen is uniquely selective when it appears at the surface of the catalyst. (Under identical conditions of flow rate, weight of catalyst, heating rate etc., the reaction of n-butane or of but-1,3-diene in air produced only CO2 and H2O.
Estudio de un producto auxiliar no iónico como alternativa al electrolito en la tintura de la lana
Il s’agit d’une étude sur l’application d’un nouveau produit auxiliaire de type non ionique, proposé pour remplacer l’électrolyte dans la teinture de la laine. En outre, à partir des études menées par les auteurs sur des applications enzymatiques dans la teinture de la laine, ils ont essayé d’établir les effets synergiques qui résulteraient éventuellement de l’utilisation d’une enzyme avec le nouveau produit auxiliaire de type non ionique. Ils ont ensuite comparé les résultats d’absorption de colorant, les différences de couleur sur les articles teints et certaines solidités entre les teintures à basse température effectuées selon un système conventionnel comme l’électrolyte et les teintures où l’électrolyte est remplacée par le nouveau produit non ionique, une enzyme ou les deux en même temps.
Ils ont aussi déterminé certains paramètres écologiques (DQO, DBO, pH et conductivité) dans les bains résiduels des différentes teintures étudiées.El presente trabajo es un estudio de aplicación de un nuevo producto auxiliar de tipo no iónico que se propone como sustituto del electrolito en la tintura de la lana. Además, tomando como base los estudios llevados a cabo por los autores sobre aplicaciones enzimáticas en la tintura de lana, se intentan establecer los posibles efectos sinérgicos al utilizar un enzima junto con el nuevo producto auxiliar de tipo no iónico. Se comparan los resultados de absorción de colorante, diferencias de color en los artículos teñidos, así como algunas solideces entre: tinturas a baja temperatura efectuadas según un sistema convencional con electrolito y tinturas en las que se sustituye el electrolito por el nuevo auxiliar no iónico, por un enzima o por ambos simultáneamente.
También se han determinado algunos parámetros ecológicos (DQO, DBO, pH y conductividad) en los baños residuales de las diferentes tinturas estudiadas.This paper is an application study of a new auxiliary product non-ionic type, proposed as an electrolyte substitutive in wool dyeing. This paper intends to establish the possible synergetic effects on using an enzyme together with the new non-ionic auxiliary product. Results of dye absorption, colour differences in the dyed samples, and some colour fastness are compared between a conventional dyeing system with electrolyte and dyeing systems in which the electrolyte is substituted by the new non-ionic auxiliary, by an enzyme or by both simultaneously.
Some ecological parameters (COD, BOD, pH and conductivity) have been determined in the residual baths of the different dyeings examined
FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
Federated learning (FL) facilitates edge devices to cooperatively train a
global shared model while maintaining the training data locally and privately.
However, a common assumption in FL requires the participating edge devices to
have similar computation resources and train on an identical global model
architecture. In this study, we propose an FL method called Federated
Intermediate Layers Learning (FedIN), supporting heterogeneous models without
relying on any public dataset. Instead, FedIN leverages the inherent knowledge
embedded in client model features to facilitate knowledge exchange. The
training models in FedIN are partitioned into three distinct components: an
extractor, intermediate layers, and a classifier. We capture client features by
extracting the outputs of the extractor and the inputs of the classifier. To
harness the knowledge from client features, we propose IN training for aligning
the intermediate layers based on features obtained from other clients. IN
training only needs minimal memory and communication overhead by utilizing a
single batch of client features. Additionally, we formulate and address a
convex optimization problem to mitigate the challenge of gradient divergence
caused by conflicts between IN training and local training. The experiment
results demonstrate the superior performance of FedIN in heterogeneous model
environments compared to state-of-the-art algorithms. Furthermore, our ablation
study demonstrates the effectiveness of IN training and the proposed solution
for alleviating gradient divergence
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