The guide provides information about Polish historic newspapers and journals that have been digitized by Polish libraries in recent years. Although the emphasis is on Polish language publications, some German language publications have been included.
One of the newspapers digitized by the Jagiellonian Library (Biblioteka Jagiellońska) in Kraków. The Library has digitized 235 volumes of Gazeta Lwowska from 1811 to 1939. The paper was published in Lviv (Lwów).
In 2002, the significant digitization of library collections began with the creation of the Digital Library of Wielkopolska (Wielkopolska Biblioteka Cyfrowa) in Poznań in 2002. By mid 2016, there were 117 libraries and research institutes participating in various digital projects resulting in the digitization of over two and a half million items, including many historic and rare newspapers and serial publications ((wydawnictwa ciągłe). These are examples of major digital collections of historic newspapers and periodicals.
By 2016, the Jagiellonian Library has digitized 1,222 volumes of periodicals, including newspapers from Lviv (Lwów) such as Dziennik Polski (1869-1905), Gazeta Lwowska (1811-1939), Gazeta Narodowa (1863-1915), Słowo Polskie (1895-1934), and from Kraków: Głos Narodu (1893-1927), Nowa Reforma (1882-1928), Nowości Ilustrowane (1904-1925).
You can search for a specific title by using the Jagiellonian Digital Library search box (use the URL provided above). You can also do a Google search (put the title in quotation marks). Look for the results that start with Jagiellonian Digital Library, followed by a newspaper title. The digital content is displayed in PDF and occasionally as DjVu. For the latter, use the DjVu-HTML5 access option provided by your browser.
It should be noted that some historic newspapers from Galicia have also been digitized by the Austrian National Library (Ősterreichische Nationalbibliothek), including Kurjer Lwowski (1885-1918) and Kurjer Stanisławowski (1900-1912) as well as miscellaneous issues of Kuryer Litewski from Vilnius (Wilno).
The Ossolineum Library, in cooperation with Ukrainian libraries, including the Lviv National Vasyl Stefanyk Scientific Library of Ukraine, has been digitizing numerous periodicals from its pre-World War II Lviv (Lwów) collections. The prewar Ossolineum collection of the Polish press was the largest in Poland and consisted of 5,495 titles from 1801 to 1939. The Ossolineum Library has digitized 388 periodical titles, including newspapers and journals from Galicia as well as Polish émigré newspapers such as Gazeta Polska w Berlinie (1892-1895), Gazeta Polska w Brazylii (1893-1895, 1897-1899, 1905, 1906, 1924, 1925), and some Polish American titles, including Gazeta Pittsburgska (1895), Gazeta Wisconsińska (1893-1895), Kurier: organ Polonii w Toledo (1892-1894), Reforma: organ Ligi Polsko-Republikańskiej w Buffalo (later Reforma) (1894-1895, 1898-1901), Gazeta Chicagowska (Dziennik Polski) (1896), Gazeta Robotnicza: organ Związku Robotników Polskich w Stanach Zjednoczonych Ameryki Północnej (1891-1893, 1894-1895), Kurier Nowojorski i Brookliński (Greater New York Courier) (1891-1892, 1898), Naród Polski: pismo tygodniowe dla Polaków w Ameryce (1897-1914), Polak w Ameryce (published in Buffalo, N.Y., 1892-1901), Polonia w Ameryce (1892-1900), Polonia: tygodnik polityczny, naukowy i społeczny (1892-1896, 1903-1905, 1926), Sokół w Ameryce (1912, 1914).
The easiest way to access a particular title is by choosing przeglądanie (browsing) from the main page (use the URL provided above), and then rodzaj dokumentu: czasopismo (document type: periodical). You will get a list of digitized periodicals. Click on the title you are interested in and choose the PDF option for downloading.
The "Polona" National Digital Library, created by the National Library (Biblioteka Narodowa) in 2006, is one the most significant digital libraries in Poland. The library contains manuscripts and first editions of many renowned Polish authors, including Jan Kochanowski, Mikołaj Rej, Juliusz Słowacki, Stefan Żeromski, Eliza Orzeszkowa, etc. Periodicals account for about sixty-eight percent of the digitized materials, including many historic newspapers and journals such as Robotnik (official publication of the Polish Socialist Party), Kurjer Warszawski, Kurjer Wileński, Gazeta Warszawska, Gazeta handlowa, Izraelita as well as Jidish titles such as Dos Naje Wort, Hajntige Najes, Lubliner Togblat, etc. Full-text keyword searching is available for some titles and search results can be limited to full-text by choosing the dostępne z OCR (OCRed text available) option.
There are many historic titles in the collection, including Demokrata Polski (1837-1849), Dziennik Berliński (1897-1929), Dziennik Kijowski (1915-1917), Dziennik Mód Paryskich (1840-1848), Dziennik Poznański (1859-1939), Dziennik Wielkopolski (1830-1831), Tygodnik Wielkopolski (1870-1874). There are also some title in German amd English, including a Freemason publication from Leipzig, Freimaurer Zeitung: Manuscript für Brüder (1847-1918), and an agricultural journal from London, The Farmer's Magazine and Monthly Journal of the Agricultural Interest (1853).
You can search for a specific title by using the Wielkopolska Library search box. For better results put the title in quotation marks. You can also do a Google search (again use quotation marks). The digital content is displayed in DjVu. Use the DjVu-HTML5 access option provided by your browser.
You can search for a specific title by using the University of Warsaw e-Library search box. You can also browse a list of journal and newspaper titles (use the URL provided above). You can also do a Google search; look for the results starting with "Warsaw University Digital Library." The digital content is displayed in DjVu. Use the DjVu-HTML5 access option provided by your browser.
Moreover, there are many Polish language journals published in Silesia that are available in the Silesian Digital Library. A lot of titles are available as PDF files and can be downloaded.
Many Polish digital libraries are built on the dLibra platform, which was developed by the Poznań Supercomputing and Networking Center. It allows for some uniformity with respect to the interface design and the content display. The digital content is displayed either as PDF or DjVu with older content mostly in DjVu. The Ossolineum Library displays its content using both formats. You can download PDF files from any collection. As far as full-text searchability is concerned, it is rather limited, with the exception of the "Polona" National Digital Library. "Polona" allows for full-text keyword searching of some titles and search results can be limited to full-text by choosing the dostępne z OCR (OCRed text available) option. However, title searches work well within particular collections and some titles are Google-searchable. Most digitized collections can also be accessed via Federation of Digital Libraries (Federacja Bibliotek Cyfrowych) and Europeana portals.
Federation of Digital Libraries (Federacja Bibliotek Cyfrowych-FBC) provides access to digital collections of 117 Polish cultural and scientific institutions. These include historic journals and newspapers digitized by various libraries. FBC allows for searching across collections for specific titles. For example, a title search for Gazeta Lwowska will retrieve digitized issues of the newspaper in digital collections of the Jagiellonian Digital Library and the Wielkopolska Digital Library. However, the display format and full-text searching depend on the standard employed by FBC member libraries for digitizing their collections. FBC provides the same type of access as originally created by its member libraries. FBC allows for searching for a specific type of publication. Choose the advanced search option. From the "all fields" search box choose "resource type." In the other search box type the Polish name of publication format you are looking for (you will get more results by typing the format name in Polish rather than in English). For example, czasopismo (periodical), gazeta (newspaper), dziennik (daily), tygodnik (weekly), miesięcznik (monthly), kwartalnik (quarterly), rocznik (annual), etc. You can also combine terms using OR (czasopismo OR gazeta).
FBC is the largest Polish data provider in Europeana (also known as Europeana Collections). There are currently over one and a half million objects from Polish digital collections available through Europeana, including Polish historic periodicals. The presence of Polish digital content in Europeana collections should not be underestimated as many international users may be more familiar with Europeana than with Polish digital portals.
Krzysztof Frankowicz, "Selekcja czasopism do skanowania i publikacji w JBC [The Selection of Periodicals for Scanning and Ingesting in JBC]," in Uniwersytet Jagielloński-Biblioteka Jagiellońska Jagiellońska Biblioteka Cyfrowa, edited by Krystyna Sanetra, (Kraków, Poland: Biblioteka Jagiellońska, 2013), 58–65.


RE: What is your favorite part of SVS and what can we do better?
DINOSAURS!!!!!!! Come on, show us all your tricks and tips!!!!!!!
worked on the figure of the Halloween guy, changed arms and legs a bit and worked on his outfit.
I will work the lines to bring more dimension in the bird, this is an updated version, worked the little pumpkin head a bit. I will add blues, greens and especially oranges with photoshop later. Trying to get the layout and design, and feeling complete with just black and white... thanks for your help.


text:
Q: Is it possible to triple boot given the following partition state? I have the following partitions on my drive:

I have researched online that it's not possible to have more than 4 primary partitions. I am not sure these are primary partitions, so will I be able to install windows as triple boot, I have Ubuntu and Kali dual booting as of now. If no, what can I do to be able to do so.

A: The restriction of only 4 primary partitions, and indeed the entire concept of primary partitions applies only to the classic MBR partitioning scheme.
Systems that use UEFI will normally use GPT partitioning scheme, in which there are no primary nor extended partitions; all partitions are equal, and the maximum limit will be 128 or more. So if your system uses GPT partitioning, you could add as many UEFI-bootable operating systems as your free disk space allows, and triple/quadruple/n-tuple boot.
In gparted, click on View -> Device information and then look at the Partition table: row to identify the type of your disk's partitioning scheme.
If you want to install Windows as any other than the first installed OS on the system, you might want to pre-create the partitions needed by Windows to match the layout described in this Microsoft document and then proceed very carefully through the choice of the installation destination in the Windows installer: it tends to offer "overwrite the whole disk and install only Windows on it" as the first choice, and any other options may be behind some "advanced options" selection.

redpajama_set_name: RedPajamaStackExchange


Dark Lady Lumiya is finaly back, I've been away for an while for different reason's but I am back now and I am going to make sure that I'm goign to be active a lot. well I just wanted to let you guys know.
There is no fear, there is power.
There is no death, there is immortality.
I am the Heart of Darkness.
And who are you? I played SWVII last summer/spring, and just joined back up like December 1st. Who are you? Not another Sith I hope. Or Ikrit will have to take you down too.


text:
HomeUpcoming Car2024 Land Rover Range Rover EV Performance & Specifications
2024 Land Rover Range Rover EV Performance & Specifications
The Range Rover is the Land Rover's the most Hereditary Pinnacle right from the Seventies. With is utmost reliability among the customers, this Rover has never fail to deliver the expectations which a driver has from its ride. Now that legacy is rebooted and transferred to a new generation of the locomotives which is now rapidly emerging in today's world, the EV world. Although Range Rover has never let us down, the launch of this car is the judge for the further imminent.
Also Read : BMW X5 M 2022 – Price, Mileage, Features, Engine, Top Speed & 0-60mph
The all new segment will be lead by the Land Rover, as their first EV in the production. Not much disclosed of the looks and power, exterior and interior but is expected to be carried down the same the subsisting Range Rover, just electrified.
Although not much is revealed of it but pretty much not in the distance, the production is eagerly awaited to have open on all of the information. As of now, the trims are on the share which goes by the names of SE for $120,000 (est), HSE for $140,000 (est) and lastly, the superlative Autobiography for $160,000 (est).

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}
In recent years, machine learning has demonstrated superior performance in various fields including computer vision~\cite{he2016deep,krizhevsky2012imagenet}, natural language processing~\cite{bahdanau2014neural,devlin2018bert,young2018recent}, autonomous vehicle~\cite{bojarski2016end,chen2015deepdriving}, robotics~\cite{kober2013reinforcement,sunderhauf2018limits}, and healthcare~\cite{esteva2017dermatologist}. However, it has also been shown that machine learning models are vulnerable to various types of attacks, such as evasion attacks~\cite{athalye2018obfuscated,carlini2017towards,dong2018boosting,goodfellow2014explaining,moosavi2016deepfool,szegedy2013intriguing} and poisoning attacks~\cite{biggio2012poisoning,mei2015using,munoz2017towards,shafahi2018poison,steinhardt2017certified,munoz2019poisoning,yang2017generative}. Evasion attacks occur at the inference phase, which cause misclassification without altering the model. The adversarial example is one such attack, which fools a well-trained neural network to misclassify specific inputs by adding imperceptible perturbations to the original images~\cite{goodfellow2014explaining}. In contrast, poisoning attacks corrupt the model by injecting malicious training data points. Compared to evasion attacks, poisoning attacks have the potential to render more severe or even catastrophic damages to real-world applications where continuously model updates are required or training data are accessible. For instance, as recommendation systems usually take customers' feedback to update the model constantly, the adversary can possibly utilize such vulnerability to manipulate recommendation content to achieve the adversarial goals.

Prior research on poisoning attacks can be broadly classified into two categories: availability attacks that degrade overall model accuracy (i.e., denial-of-service attacks)~\cite{biggio2012poisoning,xiao2012adversarial,mei2015using,munoz2017towards,mozaffari2014systematic,jagielski2018manipulating,steinhardt2017certified,yang2017generative,munoz2019poisoning} and integrity attacks that cause misclassification on specific instances (i.e., backdoor attacks)~\cite{chen2018detecting,shafahi2018poison,zhu2019transferable,turner2019label,yao2019latent}. However, the existing literature of poisoning availability attacks lacks an extensive study on multi-class classification. Even those considered multi-class classification, are rarely evaluated against the state-of-the-art learning models such as ResNet or large-scale dataset such as ImageNet. Furthermore, the adversarial goal of prior works on poisoning availability attack is constrained only in degrading the overall accuracy.

In this paper, we extend the literature by developing and applying novel poisoning availability attack strategies against deep neural networks to a per-class basis. We advance the adversarial objectives by formulating two attack tasks: (i) corrupting the overall model performance by forcing the model to classify all or most new inputs as a targeted class, which is denoted as the \emph{supplanter class}, and (ii) corrupting performance of a specific class, which is named as the \emph{victim class}, while retaining the accuracy of other classes. We propose an efficient gradient-based framework for the poisoning availability attacks, which generates more effective poisoned data samples by enlarging/dwindling the feature information of images. The idea is partially inspired by the explanation of probabilities that are assigned to incorrect answers~\cite{hinton2015distilling}. We leverage this inherent structural similarity among different classes to develop the novel class-oriented poisoning attacks.

\textbf{Our Contributions.} In this paper, we made the following technical contributions.
\begin{itemize}[leftmargin=.3in,nolistsep,noitemsep]
\item {This paper introduces the concept of the class-oriented poisoning attack, which manipulates the model behaviour on a per-class basis.}
\item {While the majority of prior work focuses on binary classification tasks and conventional learning algorithms such as regression and support vector machine, our poisoning availability attacks extend to multi-class classification tasks with deep neural networks.}
\item {We define two new performance metrics at the class level: \emph{change-to-target (CTT)} rate and \emph{change-from-target (CFT)} rate, for evaluating the effectiveness of poisoning availability attacks, along with the overall accuracy degradation.}
\item {This paper, to the best of our knowledge, takes the first step to systematically evaluate the effectiveness of poisoning availability attack on a large-scale dataset (ImageNet).}

\end{itemize}

\section{Problem Settings}
\subsection{Poisoning Availability Attack}
\textbf{Related work.} The existing literature has exploited the poisoning availability attack against various learning algorithms such as clustering~\cite{biggio2013data}, LASSO~\cite{xiao2015feature}, collaborative filtering~\cite{li2016data} and SVM~\cite{biggio2012poisoning}. The closest works to this paper are~\cite{munoz2017towards} and~\cite{yang2017generative}, which extended poisoning availability attack to multi-class classification against regression models and shallow neural networks, respectively. However, these are not class-oriented. The prior works have also developed a series of approaches for poisoned data generation, including influence function~\cite{koh2017understanding}, minimax duality~\cite{koh2018stronger}, Karush-Kuhn-Tucker (KKT) conditions~\cite{mei2015using}, autoencoder~\cite{yang2017generative} and generative adversarial network (GAN)~\cite{munoz2019poisoning}. Different from these prior works, we propose a novel approach for class-oriented poisoned data generation by retaining/eliminating feature information in images, while targeting at multi-class classification tasks with deep neural networks.

We consider a continuous or online learning scenario where data samples arrive in a sequential order. Let $\boldsymbol{x} \in \mathcal{X}(\mathcal{X} \in \mathbb{R}^d)$ be a $d$-dimensional input and $y \in \mathcal{Y}$ be the corresponding label for the input. The objective of the classification task is to build up the mapping $\mathcal{F}$: $\mathcal{X} \xrightarrow{} \mathcal{Y}$. We denote the pre-trained base classifier's parameters as $\theta$. The model parameters are updated to $\theta^*$ with the incoming new steam of data: $\theta \xrightarrow{(\boldsymbol{x},y)} \theta^*$.

Poisoning availability attacks are typically formulated as a bi-level optimization problem:
\vspace{-0.1cm}
\begin{align}
\mathop{\arg\max}\limits_{\theta^* \in \Theta}&\sum_{(\boldsymbol{x},y) \in \mathcal{D}_{tr}} L\left[\mathcal{F}_{\theta^*} \left( \boldsymbol{x} \right), y, \theta^*\right] \\
\textbf{s.t.}\quad  \theta^* \in \mathop{\arg\min}_{\theta^* \in \Theta}& \sum_{(\boldsymbol{x},y) \in \mathcal{D}_{tr} \cup \mathcal{D}_p} L\left[ \mathcal{F}_\theta \left( \boldsymbol{x} \right), y,  \theta^*\right],
\end{align}
\vspace{-0.3cm}

where $D_{tr}$ is the clean training dataset, $D_p$ is the poisoned dataset, $\Theta$ is the possible parameter space, and $L[\cdot]$ is the loss function. The outer maximization stands for adversarial objective, while the inner minimization represents the training process with poisoned dataset.

\subsection{Class-Oriented Adversarial Objectives}
Rather than only focus on maximizing the overall loss, we take the first step towards extending the adversarial objective to a per-class basis. We impose two new adversarial objectives in addition to the goal of degrading overall accuracy, which are formulated as two optimization problems accordingly.

\textbf{Problem 1: All-Supplanter (AS) attack.} The goal of the AS attack is to misclassify all or most inputs as a targeted object class, which we named as \emph{supplanter class}. For a broader real-world example, the adversary can apply the AS attack to compromise a recommendation system such that no matter what the customer is looking for, only a particular item/brand (the supplanter class) will be recommended. The AS attack problem can be formulated as:
\vspace{-0.1cm}
\begin{align}
\mathop{\arg\max}_{\theta^* \in \Theta}& \sum_{(\boldsymbol{x},y) \in \mathcal{D}_{tr}} L\left[ \mathcal{F}_{\theta^*} \left( \boldsymbol{x} \right),  y, \theta^*\right]
\end{align}
\vspace{-0.2cm}
\begin{align}
\textbf{s.t. }& \theta^* \in \mathop{\arg\min}_{\theta^* \in \Theta} \sum_{(\boldsymbol{x},y) \in \mathcal{D}_{tr} \cup \mathcal{D}_p} L\left[ \mathcal{F}_\theta \left( \boldsymbol{x} \right), y,  \theta^*\right]   \\
& \theta^* \in \mathop{\arg\min}_{\theta^* \in \Theta} \sum_{(\boldsymbol{x},y) \in \mathcal{D}_{tr}, {y = y_s}} L\left[\mathcal{F}_{\theta^*} \left( \boldsymbol{x} \right), y, \theta^*\right],
\end{align}
\vspace{-0.1cm}

where $y_s$ represents the label of the supplanter class.

\textbf{Problem 2: Only-Victim (OV) attack.} The goal of the OV attack is to compromise the classification accuracy for only the inputs from a specific class, which is denoted as \emph{victim class}. For a broader example again, the adversary can apply the OV attack to a high privilege class in an access control system, resulting in a denial-of-service to only the users in this class. The OV attack problem is formulated as:

\vspace{-0.2cm}
\begin{align}
\mathop{\arg\max}_{\theta^* \in \Theta}& \sum_{(\boldsymbol{x},y) \in \mathcal{D}_{tr},{y=y_v}} L\left[ \mathcal{F}_{\theta^*} \left( \boldsymbol{x} \right), y, \theta^*\right] \end{align}
\vspace{-0.2cm}
 \begin{align}
\textbf{s.t. }& \theta^* \in \mathop{\arg\min}_{\theta^* \in \Theta} \sum_{(\boldsymbol{x},y) \in \mathcal{D}_{tr} \cup \mathcal{D}_p} L\left[ \mathcal{F}_\theta \left( \boldsymbol{x} \right), y,  \theta^*\right]   \\
& \theta^* \in \mathop{\arg\min}_{\theta^* \in \Theta} \sum_{(\boldsymbol{x},y) \in \mathcal{D}_{tr}, {y \neq y_v}} L\left[\mathcal{F}_{\theta^*} \left( \boldsymbol{x} \right), y, \theta^*\right],
\end{align}
\vspace{-0.2cm}

where $y_v$ is the label of the victim class.

\subsection{Class-Oriented Evaluation Metric}

We propose two class-oriented evaluation metrics to assess the performance of the proposed class-oriented poisoning attacks.

\textbf{Change-to-Target (CTT)} rate is designed as an evaluation metric for the \textbf{AS attack}, which indicates the percentage of images that are classified as a targeted supplanter class ($\mathcal{C}_s$) due to the poisoning attack. CTT rate for a class $\mathcal{C}_k$ is formally defined over a test dataset $\mathcal{D}_t$ as:

\begin{equation}
    CTT(\mathcal{C}_k)  =\frac{\sum_{\substack{(\boldsymbol{x_i},y_i) \in \mathcal{D}_t \\{y_i=y_k}}}\big{(}\mathcal{F}_{\theta^*}(\boldsymbol{x_{i}})==y_s\big{)} -\sum_{\substack{(\boldsymbol{x_i},y_i) \in \mathcal{D}_t \\{y_i=y_k}}}\big{(}\mathcal{F}_{\theta}(\boldsymbol{x_{i}})==y_s \big{)} }{N_{k}},
    \label{equ_CTT_perclass}
\end{equation}

where $N_k$ is the total number of images in the class $\mathcal{C}_k$ and $y_k$ is the corresponding categorical label, while $\mathcal{F}_{\theta}$ and $\mathcal{F}_{\theta^*}$ are the model mappings before and after the poisoning attack, respectively. Then, the overall CTT rate over $\mathcal{D}_t$ can be calculated by weighted averaging the CTT rates of all non-supplanter classes:
\vspace{-0.1cm}
\begin{equation}
    CTT  =\frac{\sum_{\substack{k=1 \\ k\neq s}}^{K}  \big{(}N_k \cdot CTT(\mathcal{C}_k)\big{)}}{\sum_{\substack{k=1 \\ k\neq s}}^{K} N_k},
    \label{equ_CTT}
\end{equation}

where $K$ is the total number of classes.

\textbf{Change-from-Target (CFT)} rate is specifically used for evaluating the \textbf{OV attack}, which indicates how many images from a targeted class are misclassified due to the poisoning attack. Similarly, CFT rate for a class $\mathcal{C}_k$ is defined by Equation~(\ref{equ_CFT}).
\begin{equation}
   CFT(\mathcal{C}_k) =\frac{\sum_{\substack{(\boldsymbol{x_i},y_i) \in \mathcal{D}_t \\{y_i=y_k}}}\big{(}\mathcal{F}_{\theta}(\boldsymbol{x_{i}})==y_k\big{)}-\sum_{\substack{(\boldsymbol{x_i},y_i) \in \mathcal{D}_t \\{y_i=y_k}}}\big{(}\mathcal{F}_{\theta^*}(\boldsymbol{x_{i}})==y_k\big{)} }{N_k}
   \label{equ_CFT}
\end{equation}

\subsection{Adversarial Model}
Following the recent works on poisoning availability attack~\cite{koh2017understanding,mei2015using}, we consider the same adversarial model where the adversary has the knowledge of the learning algorithm and model architecture. Meanwhile, the attacker is assumed to be capable of injecting crafted poisoned data and assigning labels to poisoned data. Although the assumption is less practical in the real-world applications, it allows us to assess the effectiveness of poisoning attacks in the worst-case scenario, which is the same as in the literature of poisoning availability attacks~\cite{burkard2017analysis,biggio2012poisoning,munoz2019poisoning,li2016data,munoz2017towards}.

\section{Adversarial Goal and Evaluation Metrics}

We consider two tasks for the poisoning attacks on a per-class basis, which are distinct from prior indiscriminate poisoning attacks:
\begin{itemize}
\item \textbf{Task 1 (targeted supplanter class):} corrupting the overall model performance and manipulating the model to classify all inputs into a supplanter class.
\item \textbf{Task 2 (targeted victim class):} only corrupting performance of a selected victim class, while maintaining the accuracy of other classes.
\end{itemize}

To achieve the adversary goals, we extend the prior indiscriminate poisoning attack to the class level. To showcase the idea, Figure~\ref{Task1&2} shows a representative example of our adversarial goal. While prior methods yield misclassification in a random manner at the class level, our proposed attacks cause misclassification into the intended class. Additionally, we introduce new metrics (i.e., CTT and CFT) to impose additional constraints to guide the attack.

\textbf{Definition: CTT rate and supplanter class}
A supplanter class is a targeted class that after carrying out a successful poisoning attack, all or most images will be classified as this class.

CTT rate stands for change-to-target rate, which indicates the percentage of images that are classified into the supplanter class after the poisoning attack. It can be formally expressed as in Equation~\ref{equ_CTT},  where $X$ is the entire dataset,  $N$ is total number of images in $X$, $y_s$ is the label of supplanter class, and $P(\cdot)$ is the neural network prediction with a specific input.
\begin{equation}
    CTT  =\frac{\sum_{0}^{n}(P(x_i)==y_s) }{N}
    \label{equ_CTT}
\end{equation}

\textbf{Definition: CFT rate and victim class}
A victim class is the targeted class that suffers significantly performance degradation after the poisoning attack. For instance, if class `0' is selected as the victim class, images that originate from this class should no longer be classified as 0 after the attack. Meanwhile, classification of images from other classes should not be largely affected.

CFT rate stands for change-from-target rate. It indicates how many images from a targeted class are misclassified after the attack, which can be mathematically expressed as in Equation~\ref{equ_CFT}, where $C$ is the image set of an arbitrary class(any class can be set as a targeted class to evaluate the CFT rate), $N_c$ is the number of images in the set that are correctly classified before attack, and $y_c$ is the original label of the targeted class.
\begin{equation}
   CFT(C) =\frac{\sum_{i=0}^{n_c}(P(x_i)==y_c)_{before}-\sum_{i=0}^{n_c}(P(x_i)==y_c)_{after} }{N_c}
   \label{equ_CFT}
\end{equation}

\textbf{Definition: seed image and poisoning label}
Seed images are used to generate poisoned data for initiating a poisoning attack, which can be either selected from the original training and validation datasets, or collected by synthesizing test images. Seed images can be arbitrarily selected if the victim class is non-targeted. Poisoning labels are defined as the ground-truth labels of the supplanter class.

\section{Class-Oriented Poisoning Attack Methods}

\subsection{All-Supplanter Attack}

Intuitively, we expect retraining images with the label of the supplanter class, similar to the flipped-label poisoning attack~\cite{xiao2012adversarial}, would have the potential to shift predictions of all classes towards the supplanter class. However, our experimental results demonstrate that such attacks, when applied to multi-class classification tasks, neither effectively degrade the overall accuracy nor achieve the class-oriented adversarial goal of the AS attack for the neural network models (see \emph{Section 4}).

To improve the effectiveness of poisoning towards the supplanter class, we develop a novel method for generating the poisoned data. We leverage the fact that the probabilities assigned to other object classes of a well-trained model also reveal how much feature information of these incorrect classes are associated with the corresponding image by the model~\cite{hinton2015distilling}. Thus, we hypothesize that if an input image only contains feature information of its ground-truth class, training such image with the supplanter class label will force the model to expand the decision boundary of the supplanter class to the maximum degree.

Inspired by prior works that exploit the penultimate layer (formally called logit outputs) to distill knowledge of neural networks~\cite{hinton2015distilling}, catch features~\cite{ilyas2019adversarial}, and defend against adversarial examples~\cite{kannan2018adversarial}, we follow a similar methodology to control the feature information through the logit outputs. Our algorithm starts with a seed image that is arbitrarily picked from any class other than the supplanter class, and then attempts to retain the feature information associated with the original ground-truth class and reduce the feature information of other classes by enlarging/dwindling the corresponding logit outputs. We here denote $f(\cdot)$ as the logit output function of the neural network and $f_{y_k}$ as the corresponding logit to the categorical label $y_k$. The objective of our algorithm can be expressed by the following minimization objective function:
\begin{equation}

    \mathop{\arg\min}_{\boldsymbol{x_p}\in D_p} \Big{(}L[f_{y_o}(\boldsymbol{x_o}),+\infty,\theta] + \sum_{\substack{k =1\\k \neq o}}^{K}{\lambda_k\cdot L_k[f_{y_k}(\boldsymbol{x_o}),-\infty,\theta]} \Big{)},
    \label{objective_function}
\end{equation}

where $\boldsymbol{x_o}$ is the seed image, $y_o$ is the corresponding categorical ground-truth label, $f_{y_o}(x_o)$ is the logit output of the ground-truth class, $f_{y_k}(\boldsymbol{x_o})$ is the logit output of other classes, and finally $\boldsymbol{x_p}$ stands for the poisoned image. $\lambda_k$ is used to control the importance of loss terms. Alternatively, from the perspective of entropy, we expect such optimization would also reduce the following information entropy $H[\cdot]$:
\begin{equation}
    H\big{[}\sigma\big{(}f(\boldsymbol{x_p})\big{)}\big{]}=-\sum_{k=1}^{K}p_k\cdot log(p_k) \xrightarrow{} 0,
    \label{entropy}
\end{equation}
where $p_k$ is the probability for each class that converted from the logit  $f_{y_k}$ using \emph{softmax}.

However, solving the minimization problem can be computationally expensive, especially for high-dimensional datasets that have hundreds or thousands of classes. Based on the facts that (i) the classification result is determined by the largest probability and (ii) only the supplanter class is the target, we consider an approximation that simplifies the task to retain feature information of the ground-truth class and eliminate feature information of the supplanter class. In other words, we only focus on the two most important classes instead of all classes, i.e.,
\begin{equation}

    \mathop{\arg\min}_{\boldsymbol{x_p}\in D_p}\big{(}L[f_{y_o}(\boldsymbol{x_o}),+\infty,\theta] + \lambda\cdot L[f_{y_s}(\boldsymbol{x_o}),-\infty,\theta]\big{)}.
    \label{objective_function_simplified}
\end{equation}

\begin{comment}

Similarly, the evaluation function can be written as the following:
\vspace{-0.1cm}
\begin{equation}
    H(\sigma(f(\boldsymbol{x_p}))\approx -[p_o \cdot log(p_o) + p_s \cdot log(p_s)] \xrightarrow{} 0
    \label{entropy2}
\end{equation}

 where $p_s$ and $p_o$ represent the probability of the ground-truth class and the supplanter class, respectively.

\end{comment}

By solving the minimization using gradient descent, the poisoned image $\boldsymbol{x_p}$ is updated through one backward pass:

\vspace{-0.1cm}
\begin{equation}
    \boldsymbol{x_p} = \boldsymbol{x_o} - \epsilon \cdot sign\Big{(}\nabla_{\boldsymbol{x_o}}\big{(}L[f_{y_o}(\boldsymbol{x_o}),+\infty,\theta] + \lambda\cdot L[f_{y_s}(\boldsymbol{x_o}),-\infty,\theta]\big{)}\Big{)},
    \label{poisoned_data_update}
\end{equation}

where $\epsilon$ is a hyper-parameter chosen by the attacker. This update step can also be executed for several rounds to more aggressively increase the feature information of the ground-truth class and reduce the feature information of the supplanter class. Algorithm~\ref{algo:poisoned data generation} presents the details of poisoned data generation for the AS attack.

{\centering
\begin{algorithm}
\DontPrintSemicolon
    \KwIn{$\boldsymbol{x_o}$: seed image, $y_o$: seed image label, $y_s$: supplanter class label\\
    $T$: max number of optimization iterations \\
    hyper-parameters $\lambda$, $\epsilon$}
    \KwOut{poisoned image $\boldsymbol{x_p}$, poisoned label $y_p$}

    {\bf Initialization:}  $\boldsymbol{x_{p_0}} =  \boldsymbol{x_o} - \epsilon \cdot sign\Big{(}\nabla_{\boldsymbol{x_o}}\big{(}L[f_{y_o}(\boldsymbol{x_o}),+\infty,\theta] + \lambda\cdot L[f_{y_s}(\boldsymbol{x_o}),-\infty,\theta]\big{)}\Big{)} $

    \While{t<T}{
    Compute the gradient: $\nabla = \nabla{\boldsymbol{x_{p_t}}}\big{(}L[f_{y_o}(\boldsymbol{x_{p_t}}),+\infty,\theta] +\lambda\cdot L[f_{y_s}(\boldsymbol{x_{p_t}}),-\infty,\theta]\big{)}$

    Update the poisoned image: \textit{Clip}\{$\boldsymbol{x_{p_{t+1}}} = \boldsymbol{x_{p_t}} - \epsilon \cdot sign(\nabla)$\}

    \uIf{$f_{y_s}(\boldsymbol{x_{p_{t+1}}})$ $>$ $f_{y_s}(\boldsymbol{x_{p_{t}}})$ \textbf{or} $f_{y_o}(\boldsymbol{x_{p_{t+1}}})$ $<$ $f_{y_o}(\boldsymbol{x_{p_{t}}})$ }{

    \textbf{break}
    }

    }

   \textbf{Assign} $y_p = y_s$

    \textbf{Return} $\boldsymbol{x_p}, y_p$

\caption{Poisoned Data Generation (All-Supplanter)}
\label{algo:poisoned data generation}
\end{algorithm}
}

\subsection{Only-Victim Attack}

Compared to the AS attack, the OV attack is fundamentally more challenging, as it requires to not only compromise the accuracy of the targeted victim class but also maintain the performance of the other classes, i.e.,  high CFT rate for the victim class while low CFT rates for the other classes. However, as observed in our preliminary experiments as well as prior poisoning availability attacks~\cite{liu2016teaching,park2017resilient}, poisoning with data from only one class will also largely shift the distribution of other classes. Hence, a set of poisoned data from more than one class is necessary to achieve the adversarial goal of the OV attack. Intuitively, simply training images from all classes except the victim class may achieve this adversarial goal. However, our preliminary results reveal that such methods always require a huge amount of training data and very large training epochs, which is very inefficient in achieving the adversarial objective.

We propose another gradient-based algorithm for the OV attack. The entire procedure of our poisoned data generation is presented in Algorithm~\ref{algo:poisoned data generation_t2}. We modify the method from the AS attack to craft the poisoned dataset as follows: (i) pick a same number of arbitrary images from each class, (ii) enlarge/dwindle feature information of the corresponding classes for each image, as detailed in Algorithm~\ref{algo:poisoned data generation_t2}, and (iii) assign the original ground-truth labels to the non-victim classes and the targeted label $y_p$ to the victim class. Specifically, for images from the victim class, we apply the same operations as in Algorithm~\ref{algo:poisoned data generation}, while for images from other classes, we only increase the feature information of their original classes. The objective of OV attack can be expressed as
\vspace{-0.1cm}
\begin{equation}

    \begin{cases}
    \mathop{\arg\min}\limits_{\boldsymbol{x_p}\in D_p}\big{(}L[f_{y_v}(\boldsymbol{x_o}),+\infty,\theta]+ \lambda\cdot L[f_{y_p}(\boldsymbol{x_o}),-\infty,\theta]\big{)} , & \text{if  $\boldsymbol{x_o} \in \mathcal{C}_v$}\ \\
    \mathop{\arg\min}\limits_{\boldsymbol{x_p}\in D_p}\big{(}L[f_{y_o}(\boldsymbol{x_o}),+\infty,\theta]\big{)}, & \text{otherwise}
    \end{cases}
    \label{objective_function_3}
\end{equation}

Similarly, the poisoned images are updated through a backward pass in Equation~(\ref{poisoned_data_generation_2}):
\vspace{-0.1cm}
\begin{equation}
    \ \boldsymbol{x_p} =
    \begin{cases}
    \boldsymbol{x_o} - \epsilon \cdot sign\Big{(}\nabla_{\boldsymbol{x_o}}\big{(}L[f_{y_v}(\boldsymbol{x_o}),+\infty,\theta] +\lambda\cdot L[f_{y_p}(\boldsymbol{x_o}),-\infty,\theta]\big{)}\Big{)}, & \text{if  $\boldsymbol{x_o} \in \mathcal{C}_v$}\ \\
    \boldsymbol{\boldsymbol{x_o}} - \epsilon \cdot sign\Big{(}\nabla_{\boldsymbol{x_o}}\big{(}L[f_{y_o}(\boldsymbol{x_o}),+\infty,\theta]\big{)}\Big{)}, & \text{otherwise}
    \end{cases}
    \label{poisoned_data_generation_2}
\end{equation}

\begin{algorithm}
\DontPrintSemicolon
\KwIn{$\boldsymbol{x_k} \in \boldsymbol{X}$: the set of seed images, {$y_k \in Y$}: the set of labels associated to $X$ \\
    $y_p$: poisoned label, $T$: max number of optimization iteration \\
    hyper-parameters $\lambda$, $\epsilon$}
\KwOut{poisoned dataset $\boldsymbol{X_p}$, poisoned label set $Y_p$}
  \uIf{$\boldsymbol{x_k}$ $\in$ $\mathcal{C}_v$}{
    \textbf{Apply} Algorithm 1\;

    \textbf{Assign} $y_k = y_p$
  }
  \Else{
  \textbf{Initialization:} $\boldsymbol{x_{p_0}} = \boldsymbol{\boldsymbol{x_o}} - \epsilon \cdot sign\Big{(}\nabla_{\boldsymbol{x_o}}\big{(}L[f_{y_o}(\boldsymbol{x_o}),+\infty,\theta]\big{)}\Big{)}$

    \While { $ t < T$} {
Compute the gradient: $\nabla = \nabla{\boldsymbol{x_{p_t}}}\big{(}L[f_{y_o}(\boldsymbol{x_{p_t}}),+\infty,\theta]\big{)}$

Update the poisoned image: \textit{Clip}\{$\boldsymbol{x_{p_{t+1}}} = \boldsymbol{x_{p_t}} - \epsilon \cdot sign(\nabla)$\}

}
  }

\textbf{Assign} $\boldsymbol{X_p} = \boldsymbol{X}$ ; $Y_p = Y$

\textbf{Return} $\boldsymbol{X_p}$, $Y_p$

\caption{Poisoned Data Generation (Only-Victim)}
\label{algo:poisoned data generation_t2}
\end{algorithm}

\section{Experiments}
\subsection{Experimental Settings}
We apply our proposed poisoning attacks to multi-class image classification tasks using three widely-used datasets (MNIST, CIFAR-10, and ImageNet-ILSVRC2012) against classic neural network models (LeNet-5, Vgg-9, and ResNet-50, respectively). All the evaluations are performed on the corresponding validation datasets. For comparison with prior works, we implement the flipped-label attack (FL)~\cite{xiao2012adversarial} and the direct gradient method (DGM)~\cite{yang2017generative} as the baseline attacks for MNIST and CIFAR-10. We also examine our poisoning availability attacks on ImageNet against a pre-trained ResNet-50 model~\cite{he2016deep}, which we hope to serve as a baseline comparison for future works of poisoning availability attacks. To illustrate the effectiveness of our methods as well as facilitate a fair comparison, we minimize the impact of significant model shifts due to large learning rates and better align with practical continuous or online learning scenarios, by setting the initial learning rate of the poisoning attack close to the final learning rate of the base model and applying the same decay strategy during the attack. All networks are implemented with TensorFlow and experiments are run on NVIDIA Tesla V100 GPUs.

\vspace{-0.1cm}
\subsection{Experimental Results of the All-Supplanter Attack}
To examine the true power of our AS attack, we only generate a single instance for the poisoning process. We present the results of MNIST and CIFAR-10 in Figure~\ref{Error-CTT}, and ImageNet in Table~\ref{resnet-CTT}. Hyper-parameters in Algorithm 1 are set as: $\lambda =1, \epsilon = 0.3$ for MNIST and CIFAR-10, and $\lambda =1, \epsilon = 0.5$ for ImageNet. It can be seen that our proposed AS attack is highly effective in degrading the overall model accuracy as well as increasing the CTT rate for the supplanter class. For example, we improve the test error from 20\% to above 80\% and the CTT rate from 10\% to 99\% on the CIFAR-10 dataset within less than 20 attack iterations. On the other hand, our method requires much fewer iterations to reach at the maximum poisoning effect than the baseline attack, whose test error and CTT rate are only increased by $\sim$20\% and $\sim$10\%, respectively, even after 50 attack iterations. For ImageNet, the proposed AS attack achieves a CTT rate of 94.07\% within 10 attack iterations. Note that including more poisoned data in each attack iteration will obviously enhance the poisoning effect of the proposed AS attack.

For scenarios where the adversary has no knowledge of the training process, it is also important to study the impact of different learning rates on the attack. Our conclusion is consistent with prior works~\cite{wang1808data} that smaller learning rates yield less effectiveness for the attack. However, while it requires more attack iterations to arrive at the maximum poisoning effect, our method still significantly outperforms the baseline attacks when learning rate is small, as indicated in Figure~\ref{Error-CTT}(c) and Figure~\ref{Error-CTT}(d).

 \begin{figure*}[htbp]
    \centering
    \resizebox{1.0\textwidth}{!}{
    \includegraphics{error-ctt_final.pdf}}
    \caption{Error and CTT rate comparison. Classes `4' and `deer' are selected as the supplanter class for MNIST and CIFAR-10, respectively. (a) and (c) are error rates, while (b) and (d) are CTT rates with different learning rates.}
    \label{Error-CTT}
\end{figure*}

\begin{table*}[hbtp]
\centering
\caption{Comparison of CTT and accuracy before and after the AS attack on ImageNet.}
\vspace{0.1cm}
  \begin{tabular}{ c | c  c  c }
      \toprule[1pt]

         & Top-1 Accuracy & Top-5 Accuracy & CTT\\
      \midrule[1pt]

    Vanilla ResNet-50  & 74.87\% & 92.02\% & --\\

    Poisoned by AS Attack  & {2.15\%} & 16.91\% & {94.07\%}\\

      \bottomrule[1pt]
  \end{tabular}

\label{resnet-CTT}

\end{table*}

\begin{comment}

Another intriguing observation is that some pairs of seed image and poisoning label naturally perform better than others. For example, as shown in Table~\ref{tab:accuracy_ct_rate_comparison}, the pair class `1' (seed image) and `3' (supplanter image) has already achieved an accuracy degradation to 25.86\%, while increasing the CTT rate to 83.58\% through baseline attack. However, we found that no matter how the baseline attack performs, our method can always yield a stronger result by running through a large number of pairs. The details are presented as follows:

\begin{table*}[htbp]

\begin{scriptsize}
    \begin{tabular}{ccccccccccc}
     \toprule
     Accuracy & 0 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & Average \\
     \midrule
     Baseline & 73.16\% & 59.43\% & 25.86\% & 76.43\% & 37.41\% & 72.41\% & 74.02\% & 50.46\% & 31.75\% & 55.66\% \\
     Our Approach & 17.43\% & 17.31\% & 12.41\% & 20.61\% & 12.41\% & 28.89\% & 18.42\% & 10.13\% & 10.21\%	& 16.42\% \\
     \toprule
     CTT Rate & 0 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & Average \\
     \midrule
     Baseline & 28.95\% & 48.49\% & 83.58\% & 29.42\% & 70.20\% & 27.33\% & 33.21\% & 58.83\% & 76.34\%	 & 50.71\% \\
     Our Approach & 92.65\%	 & 93.27\% & 97.60\% & 87.92\% & 96.74\% & 79.90\% & 91.47\% & 99.64\% & 99.99\%	 & 93.24\% \\
     \bottomrule
    \end{tabular}
    \vspace{0.2cm}
    \end{scriptsize}
    \caption{ MNIST: accuracy and CTT rate comparison of a seed image (from class `1') paired with different poisoned labels.}
    \label{tab:accuracy_ct_rate_comparison}
\end{table*}

\end{comment}

\subsection{Experimental Results of the Only-Victim Attack}
Since the adversarial goal of the OV attack is to subvert only one class without degrading the performance of other classes, there are two important metrics for this task: 1) CFT rate of the victim class should be as high as possible, and 2) CFT rates of the non-victim classes should be as low as possible. Note that the highest achievable CFT rate is upper-bounded by the accuracy of the victim class in the vanilla model. We use the same hyper-parameters as in the AS attack. The poisoned images in the OV attack only account for 10\% of the training data. We present the CFT rate of each class by poisoning attacks on CIFAR-10 in Table~\ref{CFT_CIFAR10_classes}, where class `frog' is selected as the victim class. It can be seen that DGM
is able to keep the CFT rates of all non-victim classes relatively low; however, its CFT rate of the victim class is only 39.2\%. While the FL attack achieves a CFT rate of 85.1\% for the victim class, it also significantly increases the CFT rates of non-victim classes, which reveals the difficulty of achieving the class-oriented adversarial goal by using prior poisoning attack methods. Compared to the baseline attacks, our proposed approach can effectively increase the CFT rate of the victim class and also retain the CFT rates of the non-victim classes to be less than 6.5\%. Besides, in some cases, the proposed approach even improves the accuracy of some non-victim classes, which is indicated by the negative CFT rates in our experimental results. This performance is beyond our expectation, which however does not contradict the class-oriented OV adversarial objective, i.e., only degrading the accuracy of the victim class.

For ImageNet, due to the large number of object classes, we present the distribution of the final CFT rates, as shown in Figure~\ref{fig:CFT_resnet}. While successful in maintaining low CFT rates for non-victim classes, our method achieves a CFT rate of 62\% for the victim class on ImageNet. Note that we can improve the performance by increasing the percentage of poisoning images in the training data. Therefore, it can be concluded that the proposed OV attack, although still accomplishes the adversarial goal, is less effective when compared to the performance on CIFAR-10. A possible rationale behind this phenomena is that it becomes harder to completely decouple the feature information of one class from all other classes during the poisoning, with the number of classes scaling up. On the other hand, our experimental results also reveal the importance of studying poisoning availability attack on large-scale/dimensional dataset, which is lacking in the existing literature.

\begin{table*}[hbtp]
\centering

\caption{CFT rate of each CIFAR-10 class by poisoning.}
\vspace{0.1cm}
    \begin{tabular}{c|c c c c c}
     \toprule
     Class / Attack& airplane & automobile & bird & cat & deer \\
    \midrule
     DGM & 0.1\% & -0.1\% & 12.0\% & 18.2\% & 3.7\% \\
     FL & 19.5\% & 15.0\% & 51.9\% & 53.3\% & -21.3\% \\
     \textbf{OV (proposed)}  & -0.2\% & 2.9\% & 6.5\% & -3.9\% & -1.4\% \\
     \midrule
      Class / Attack& dog & \cellcolor[HTML]{C0C0C0}frog (victim) & horse & ship & truck\\
     \midrule
     DGM & 6.8\% & \cellcolor[HTML]{C0C0C0}{39.2\%} & -0.1\% & 2.1\% & -4.3\% \\
     FL Attack & 41.1\% & \cellcolor[HTML]{C0C0C0}85.1\% & 26.3\% & 13.2\% & 19.3\% \\
     \textbf{OV (proposed)}   & 5.0\% & \cellcolor[HTML]{C0C0C0}71.6\% & 1.4\% & -1.7\% & 3.1\% \\
     \bottomrule

    \end{tabular}
     \label{CFT_CIFAR10_classes}
\end{table*}

\begin{figure}[htbp]
     \centering

     \resizebox{1\textwidth}{!}{
     \includegraphics{hist_imagenet2.pdf}}
     \caption{The CFT rate distribution of ImageNet classes by the OV attack.}
     \label{fig:CFT_resnet}
 \end{figure}

\section{Possible Defenses}
Since the main objective of this paper is to extend the attack to a per-class basis rather than enhance the adversarial effect against various defensive solutions, in general, we expect the proposed attacks to have similar performance as prior poisoning availability attacks, when evaluated under possible defenses.

Data sanitization is a defensive technique against poisoning attack that works by distinguishing and removing outliers (poisoned data) from the training dataset~\cite{cretu2008casting,rubinstein2009antidote}. However, it has been shown that a broad range of data sanitization can be easily compromised or bypassed~\cite{koh2018stronger}. Therefore, we can also leverage such techniques for our proposed attack to evade the detection. In fact, in most of the recent works on poisoning attacks, data sanitization is no longer considered as a certified defensive strategy~\cite{koh2018stronger,munoz2019poisoning,jagielski2018manipulating,wang1808data}.

Alternatively, a possible countermeasure is to periodically check the accuracy and/or loss of the learning models~\cite{munoz2017towards,yang2017generative}. Although expensive in terms of cost and time, these approaches are intuitively effective based on the fact that poisoning availability attack aims at degrading the accuracy. Therefore, the threat models of poisoning availability attacks usually consider the retraining or learning process to be unmonitored, which makes these countermeasures impractical. We expect the worst-case performance of our proposed methods against such countermeasures to be consistent with prior poisoning availability attacks~\cite{mei2015using,li2016data,munoz2017towards,mozaffari2014systematic,yang2017generative,munoz2019poisoning}.

Since the poisoned data are tailored to maliciously influence the training process of a learning model, we suggest exploiting averaged stochastic gradient classifier~\cite{wang1808data} and combinational models, such as bagging~\cite{li2016data}, where the classification results are no longer dependent on a single model, to defend against the attack. However, the overhead for deploying multiple classifiers should also be carefully considered.

\section{Conclusions}
This paper introduced the concept of class-oriented poisoning attack. We formulated two attack problems, i.e., ``All-Supplanter'' and ``Only-Victim'', which seek to compromise the model behavior on a per-class basis. Accordingly, we defined two new metrics to evaluate the performance of poisoning attacks at the class level. Our proposed gradient-based algorithms successfully achieved the class-oriented adversarial objectives through manipulating the feature information contained in images for poisoned data generation. The effectiveness of the proposed methods are comprehensively studied in our experiments.

redpajama_set_name: RedPajamaArXiv


I hardly know where to begin because we have exciting information coming out. It was a busy weekend and this is my first opportunity to share it.
If you're an astute, long-time reader this scenario isn't a revelation, but for those who believe what is on display in the media in America and globally is just dirty politics, it may be a gobsmacker.
There have been secret societies controlling our planet for a very long time. Some positive, some benign, some extremely evil; hence the endless wars, assassinations, disease, suffering, and decline of society in general.
The dark groups became so powerful it was almost impossible to fight them. Until now, as they also fight each other.
Now, the tables have turned, the dark forces are outnumbered, and the Light forces ar making great strides in the battle over evil.
We have the parties and individuals in place to topple the tyrannical, psychopathic and satanic regime, but no one wants to alert the general public to the threat and cause fear and chaos, so the take-down is shrouded in the guise of "just politics".
What seems like coincidence and happenstance most certainly is not, as you will see from the following broadcasts.
If you still haven't validated QAnon this may be the turning point for you. I have suggested that Q may be "quantum". Simon Parkes and also Kerry Cassidy of Project Camelot reported their sources saying the entity known as QAnon on 4Chan/8Chan are a group of high level patriots with a quantum computer which enables them to forecast outcomes. Simon went so far as to say the language used is not always human.
You may recall the many predictions Q has made and may also have seen efforts to show us that President Trump is the one posting as Q+. Whether you accept that QAnon is a positive group or not, by this point you have to accept that some key predictions as much as a year out have come to pass exactly as stated, and that President Trump and several others in his inner circle have seemed to link to QAnon in a blatant way, as have military groups.
Then there is the JFK link. QAnon introduced us to the slogan, "Where We Go One We Go All"; inscribed on the ship's bell on JFK's boat and featured in the movie, White Squall.
JFK spoke of "secret societies" and secrecy vs. transparency and tried to fight the forces who strove to control the world. As a result, he was assassinated.
Now… now we have a renewed, organized and powerful effort to pick up the fight JFK started. In addition, there is talk that John Kennedy Jr. did not die in a plane crash in 1999; a rumour not yet confirmed or denied with facts, but as QAnon often asks, how many coincidences have to occur before it's mathematically impossible? There is some compelling evidence he may indeed be alive and actively joining in the assault on the dark.
After watching the communications or "comms" from QAnon for some time and seeing these "coincidences" unfold and the chatter between Q and the Anons, I began to wonder if some of the people we rely on weren't placed there as part of The Plan.
How is it that the Anons and Autists just happened to be there to decode and augment Q's posts? I also wondered if the faceless entity "SerialBrain2" just happened to show up and interpret President Trump's Tweets. The mockingbird media has a field day with his "spelling errors" while we now understand they are not errors at all.
Some like to diminish the importance of Q's photographs, images, and remarks, but when you see what the decoders see in this information, you may develop a new opinion. From my perspective there certainly appears to be a powerful computer and artificial intelligence element to the QAnon and Presidential comms.
Beyond that, even, you may find a new respect for the secret communications between the secret societies battling for supremacy on our planet; this final, spiritual fight of good vs. evil that will vanquish the dark in one last, spectacular offensive to save Humanity.
It matters not to me whether you believe QAnon is on our side or not, but if you did, it would provide much peace of mind, as it has for thousands across the planet. We feel there is something far greater going on than a military intelligence operation to influence an election and current events. Now we have the facts to back up our intuition.
I first watched this video from Deplorable McAllister / Linda Paris, who, like me, wondered if it was just a coincidence that the brilliant and dedicated 4Chan/8Chan Anons were there to facilitate the QAnon communications with the world. She also speaks of a possible date when the 3 Clintons may hear justice knocking on their door for the final time.
Linda recommended watching this next video containing the decodes from TN Homesteader, and I found myself thinking it opportune that folks with this kind of a mind arrived on the scene. Everything, and everyONE is lining up perfectly in this Divine Plan.
Were the decoders triggered or activated by the events unfolding? It's difficult to deny that a number of grand "coincidences" have slipped into place as we watched and it seems less and less likely that any of it, including Donald Trump's election, were accidents.
No, I think no matter who or what Q is, they are here to stay to not only facilitate, but to oversee the Great Awakening. They encourage Humanity to educate themselves with facts, to be discerning, to leave emotion out of it and think logically, and to take responsibility for what unfolds by participating in key ways as outlined by QAnon.
Q is an excellent coach, and we have done well to engage in the lesson plan. Humanity is expected to do whatever they can to execute this liberation and not leave it for others to do for them. QAnon has activated the patriots globally by showing them the truth and inspiring action.
We understand the "countdown" is ON and today is March 18.
Whether you "get" the decodes in the numerology and Gematria in this video or not, follow along anyway because they impart a lot of great information right up to the end of the 52 minutes.
Link to latest Q drops.
This entry was posted in Uncategorized and tagged coincidence, communications, countdown, gematria, JFK, Jr. decodes, liberation, Linda Paris, love, military intelligence, numerology, predictions, President Trump, QAnon, secret societies, SerialBrain2, Starship Earth, TN Homesteader. Bookmark the permalink.


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text:
Nigeria boss hopeful striker will be fit for crucial Afcon games
11 months ago | 29
NIGERIA coach Gernot Rohr has alleviated fears over the fitness of Victor Osimhen after the striker was knocked unconscious during the final minutes of Sunday's Italian serie A match at Atalanta. The 22-year-old was stretchered off the pitch and�rushed to hospital in northern Italy�after he fell awkwardly and hit his head on the ground, following a collision with Atalanta defender Cristian Romero. After spending the night under observation and undergone multiple tests, Osimhen returned to Naples on Monday. Rohr, who is expected to name him in his squad for next month's crucial final two 2021 Africa Cup of Nations qualifiers, is hopeful he will return to action soon. "I spoke to Victor, he told me he's physically and mentally okay now but must wait for the club doctors," Rohr confirmed. "My thoughts are with him because he's had some difficult start in Italy, but he's a strong boy and he must wait for the doctors to clear him to play again. "He's an important player for us and we'll see how it goes at his club first, because his well-being is very important for everyone." A club statement read: "Victor Osimhen underwent further tests, with all coming back negative. The striker will now rest for a period of a few days before gradually returning to training. His condition will be monitored on a daily basis." Nigeria, who were third at the last Nations Cup in Egypt, still need two points from their final qualifiers at home to Benin and then away to Lesotho to confirm a spot at the next tournament in Cameroon. The Super Eagle,�who joined Napoli from Lille at the end of July�in a deal reportedly worth more than $96m, only returned to action in late January after being sidelined for over two months. His previous lay-off was due to a shoulder injury suffered on international duty in November and he then tested positive for coronavirus over the winter break. Osimhen, who has scored just two goals for the club, is not part of Napoli's preparations for the second leg of their Europa League round of 32 tie against Granada on Thursday. Last season was a breakthrough one for the winner of the�2020 Marc-Vivien Foe award�for the best African player in France's Ligue 1. He moved to southern Italy after scoring 18 goals in all competitions for Lille in his solitary campaign in French football. Ahead of the final two 2021 Nations Cup qualifiers Osimhen is the overall top scorer with four goals, he has scored five times and provided four assists in nine appearances for Nigeria at senior level. He burst into the spotlight at the 2015 Under-17 World Cup in Chile, as he clinched the Golden Boot and Silver Ball awards while steering the Golden Eaglets to the title. The then-16-year-old scored a tournament record 10 goals, netting in every one of Nigeria's games, in a performance that helped earn him Africa's Youth Player of the Year award for 2015. BBC Sport Feb. 25, 2021 @ 09:30 GMT
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Square Enix has released a new gameplay trailer for Final Fantasy Type-0 HD. Clocking in at nearly eight minutes long, it features a look at Kingdom of Orience's history and environments, the 14 warriors of Class Zero, Type-0's battle system including customizable spells and Eidolons.
On top of the Final Fantasy XIII-2 PC port being released today, Square Enix has also announced that Final Fantasy X/X-2 HD Remaster as well as Final Fantasy VII will be ported to the PlayStation 4. Both titles will be a digital download (X/X-2 will also have a physical version) and are expected to be available in Spring 2015. Final Fantasy VII is priced at $15.99 while X/X-2 HD Remaster will be available for $49.99.
We'll be taking next Friday off to digest all that turkey, but settle back this week for a mega two-part edition of Film Friday. Coming up first are some great new videos from World of Warcraft, three different Final Fantasy games, DCUO and Mordheim City of the Damned!
ZAM has teamed up once again with Square Enix for a Closed Beta Key Giveaway for their upcoming Vampire vs Human game, Nosgoth!


text:
Star Wars Fanpedia
Kathleen Kennedy
Lawrence Kasdan
Bryan Burk
Michael Arndt
Fan-made trailers
Other Movies
Spinoff films
First spinoff (2016)
Ezra Bridger
Kanan Jarrus
The Inquisitor
Taylor Gray
Freddie Prinze Jr.
Dave Filoni
More TV shows
Welcome to Fanpedia!
Episode VII and Beyond
General Star Wars Discussion
Fanpedia Discussion
Fan projects
Fan groups
Fan websites
in: Star Wars canon, Star Wars: The Clone Wars, Television series
No. of episodes
Dave Filoni (supervising)
Executive producer(s)
Catherine Winder
Matt Lanter
Ashley Eckstein
James Arnold Taylor
Various others
22 BBY—19 BBY
Star Wars: The Clone Wars is an animated Star Wars TV series that aired for five seasons on Cartoon Network. The show, which was launched in 2008 with an animated feature film of the same name, was created and produced by George Lucas. Dave Filoni served as the supervising director. The series takes place between Star Wars Episode II: Attack of the Clones and Star Wars Episode III: Revenge of the Sith, covering the events that took place in the three years between those films.
The series begins after the events of Attack of the Clones. Anakin Skywalker has become a Jedi Knight and fights against the Separatists during the Clone Wars, alongside his friend and former teacher, Obi-Wan Kenobi. Anakin reluctantly takes on an apprentice, Ahsoka Tano, and they fight together on numerous battlefields. The show features storylines with these characters as well as numerous secondary characters. Major events include power struggles amongst the Mandalorians, Palpatine's machinations to reform the Galactic Republic into the Galactic Empire, and the return of Darth Maul.
The Clone Wars was first released in 2008, after having been announced following Revenge of the Sith, and ran until 2013, when it was cancelled following The Walt Disney Company's acquisition of Lucasfilm. Despite its cancellation, the production team finished all season six episodes that were in production. Thirteen episodes of the sixth and final season, dubbed "The Lost Missions," were released onto Netflix on March 7, 2014.
1 Future of The Clone Wars
1.1 Season Six: The Lost Missions
1.2 Darth Maul: Son of Dathomir
1.3 Possible inclusion in Star Wars Rebels
2.2 Season Trailers
Future of The Clone Wars[]
Season Six: The Lost Missions[]
On March 11, 2013, Lucasfilm announced that Star Wars: The Clone Wars had been cancelled as part of an effort to chart a new direction for animated Star Wars programming. The cancellation came a few months after the announcement of Star Wars Episode VII. As a result of the cancellation, the fifth season finale—"The Wrong Jedi"—became the last episode of The Clone Wars to air on television. However, Lucasfilm did announce that, while new episodes would not be produced, episodes for the sixth season that were already in production would be completed. Lucasfilm referred to these story arcs as "bonus content" and said that they would be made available in the future. As part of the announcement, supervising director Dave Filoni revealed a clip from a future episode showing a clone trooper opening fire on a Jedi Knight, thereby hinting at the origins of Order 66—and the impending Jedi massacre.[1]
In addition to the Order 66 preview, Lucasfilm released a clip of Jedi Master Plo Koon discovering a crashed ship with a lightsaber inside, which was later revealed to be part of an episode that would explore the mystery of Jedi Master Syfo-Dias, who placed the order for the clone army before disappearing.[2] Additional arcs will include the return of Rush Clovis, who first appeared in a season two episode as an old flame of Padmé's;[3] Yoda exploring the deepest mysteries of the Force and communing with the spirit of Qui-Gon Jinn;[4] and a story involving Mace Windu and Jar Jar Binks.[5]
On February 13, 2014, Lucasfilm officially announced that the sixth season episodes—dubbed "The Lost Missions"—would be released on Netflix on March 7, 2014. In addition to the sixth season, the previous five seasons were also be released onto Netflix for the first time, along with director's cuts from episodes in the first five seasons.[6] "The Lost Missions" also aired on the German television channel Super RTL beginning on February 15, 2014.[7]
The following are all of the preview clips released for the sixth season:
"Order 66" Preview
Anakin Fears Rush Clovis
Anakin, Padmé, and Clovis Escape
"Plo Koon's Discovery" Preview
Qui-Gon Jinn Communes With Yoda
Anakin Helps Yoda Escape
Darth Maul: Son of Dathomir[]
Artwork for Son of Dathomir.
Main article: Darth Maul: Son of Dathomir
On January 7, 2014, comic writer Jeremy Barlow revealed that he was writing a comic-based continuation of the show called Darth Maul: Son of Dathomir. This series, confirmed by Filoni, is based on a set of Darth Maul-centric episodes that were written but not produced, prior to the show's cancellation. Maul's story picks up where it left off in the fifth season, after he was captured by Darth Sidious. The arc is expected to reveal how Darth Maul fits into the Sith plan for galactic conquest.[8] It is further speculated that this will conclude Darth Maul's story in The Clone Wars, though Filoni has said that fans will have to read the comic to find out if that is the case.[9]
Possible inclusion in Star Wars Rebels[]
The cancellation of The Clone Wars soon led to the announcement of Star Wars Rebels, a series set between the events of Star Wars Episode III: Revenge of the Sith and Star Wars Episode IV: A New Hope, which will feature the formation of the Rebel Alliance. Along with the announcement came word that Filoni would be one of the show's executive producers.[10] During Celebration Europe II in July 2013, where he gave fans their first look at the new series, Filoni was asked whether any storylines or characters from The Clone Wars—such as Ahsoka Tano—would continue in Rebels. Although he was non-committal, he did hint that it was possible.[11]
At New York Comic-Con 2013, Lucasfilm's Pablo Hidalgo revealed that while the Imperial Army in Rebels will be made up of recruits, some clones still filled training roles. The Empire came to view the clones as too individualistic, believing it was easier to create uniformity amongst volunteers, so the creation of clones was discontinued. Many clone veterans were still alive, though at twice the normal age due to growth acceleration, with some working as Imperial trainers and others having been discarded by society.[12] This could potentially allow for clone characters from The Clone Wars to appear in Rebels.
Hidalgo further said that, because many crew members from The Clone Wars are now working on Rebels, there is a lot of interest for the idea of characters from The Clone Wars appearing on the show show.[12]
Posters[]
The Clone Wars film poster
Season 1 poster
poster from season 3
Season Trailers[]
Main article: Star Wars: The Clone Wars/trailers
↑ A New Direction for Lucasfilm Animation on StarWars.com (backup link on Archive.org)
↑ Star Wars: The Clone Wars Preview: Plo Koon's Discovery on StarWars.com (backup link on Archive.org)
↑ Star Wars: The Clone Wars - Clovis Arc Preview
↑ Star Wars: The Clone Wars - The Lost Missions - Clip - Netflix
↑ Report: The Clone Wars Season Six Episodes To Air In Germany Next Month on TheForce.Net
↑ STAR WARS: THE CLONE WARS' FINAL SEASON, "THE LOST MISSIONS," TO HIT NETFLIX ON MARCH 7 on StarWars.com (backup link on Archive.org)
↑ Star Wars: The Clone Wars: Season Six Launching 15th February In Germany on Jedi News
↑ STAR WARS: THE CLONE WARS Final Darth Maul Story Releasing - in Comics! on Newsarama
↑ Dave Filoni Facebook status on January 10, 2014
↑ NEW ANIMATED SERIES STAR WARS REBELS COMING FALL 2014 on StarWars.com (backup link on Archive.org)
↑ First Look at "Star Wars Rebels"
↑ 12.0 12.1 Star Wars Rebels Panel Recap from New York Comic Con 2013
Star Wars: The Clone Wars on Star Wars.com
The Clone Wars Wikia
Star Wars canon
More Star Wars Fanpedia
1 Star War The Third Gathers: The Backstroke of the West
2 List of references to Star Wars in television
3 Star Wars Episode VII: Heir to the Empire
Star Wars Fanpedia is a FANDOM Movies Community.


Yes–Well, not really, sort of. I promised I'd write more, and I managed to publish more than 80 posts last year, I'd call that an accomplishment.
"You've been in pain, dear, for so long," the old lady hoarsely whispered in a voice just above whisper as she read the fate written just across the girl's pale palm. The old lady's touch was rough, and there was an uncomfortable tingling feeling in her stomach. She suddenly felt so naked in front of her.
She nodded curtly, she already knew, and she was ready to pull her hand away. She never believed in superstition. Never have, never will.
The girl tried to laugh, and she pulled her hand away completely. She shifted in her seat. "Tell me something I don't know, Madam," she softly murmured.


"It's time for a change, it's time for a new attitude on a new kind of love!"
The stars (and real-life husband and wife) play a breezy newspaper reporter and a no-nonsense fashion buyer who share a nasty case of hate at first sight. But he takes a second look when she goes from grim to glamorous in one magical afternoon at Elizabeth Arden's Beauty Salon. Will l'amour triumph! It can't miss with Thelma Ritter, Eva Gabor and Maurice Chevalier lending Cupid a hand.
New Kind of Love is currently available to watch and stream, buy on demand at Amazon, Vudu, Google Play, iTunes, YouTube VOD online.


2 While that the sun is not darkened, and the light, And the moon, and the stars, And the thick clouds returned after the rain.
4 And doors have been shut in the street. When the noise of the grinding is low, And [one] riseth at the voice of the bird, And all daughters of song are bowed down.
5 Also of that which is high they are afraid, And of the low places in the way, And the almond-tree is despised, And the grasshopper is become a burden, And want is increased, For man is going unto his home age-during, And the mourners have gone round through the street.
6 While that the silver cord is not removed, And the golden bowl broken, And the pitcher broken by the fountain, And the wheel broken at the well.
7 And the dust returneth to the earth as it was, And the spirit returneth to God who gave it.
8 Vanity of vanities, said the preacher, the whole [is] vanity.
9 And further, because the preacher was wise, he still taught the people knowledge, and gave ear, and sought out—he made right many similes.
10 The preacher sought to find out pleasing words, and, written [by] the upright, words of truth.
11 Words of the wise [are] as goads, and as fences planted [by] the masters of collections, they have been given by one shepherd.
12 And further, from these, my son, be warned; the making of many books hath no end, and much study [is] a weariness of the flesh.
13 The end of the whole matter let us hear:—'Fear God, and keep His commands, for this [is] the whole of man.


Men must define their enemies.
It is vital to identify and know everything you can possibly know about your enemy. You must know their strengths, their weaknesses, their past actions, their allies and enemies, and so forth.


Timecourses of GLC (starting at 5 mM) and LAC, PYR, GLY in the closed system.
Data sheet from the Neves et al JBC, 2002 publication. Used as a test data set for data upload.
Experimental data for the phosphoglycerate mutase (EC 5.4.2.12) activity.


Investors in Groupon Inc saw new options begin trading today, for the March 29th expiration. At Stock Options Channel, our YieldBoost formula has looked up and down the GRPN options chain for the new March 29th contracts and identified one put and one call contract of particular interest.
Groupon (GRPN) continues to lay off employees as part of its streamlining activities to narrow Groupon Lays Off 130 Employees as Part of Restructuring. Zacks' 10-Minute Stock-Picking Secret.
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text:
Q: How can I add value's into existing JSON? I have below JSON
{Name = \""john kk\"", Ownership = \"Private\", Phone = \"9999999999\", Active = \"Yes\" }

Here I want to insert value from C# request object for the key's
ie. Name, Phone
and then have to send in service call.
How can I insert values into existing json
i have below code :
 public AccountResponse SetSalseforceAccounts(AccountRequest request)
    {
      return ServiceCall(
      request,
      "Request Completed successfully",
      "Unable to complete request",
      null,
      null,
      (AccountResponse response) =>
      {
        //string AccountId = fetch salesforce AccountId from core.Account table and pass for updation and deletion.
    SalesForceService.SalesForceServiceClient salesFroceServiceClient = new SalesForceService.SalesForceServiceClient();
    SalesForceService.SalesForceRequest salesForceReq = new SalesForceService.SalesForceRequest();
    using (new OperationContextScope(salesFroceServiceClient.InnerChannel))
    {
      try
      {

        string data = String.Format("{\"Name\":\"{0}\", \"Ownership\":\"1005555\", \"Phone\":\"545454545\", \"Active__c\":\"{1}\"}", request.Entity.ShortName, request.Entity.Active ? "Yes" : "No");
        salesForceReq.Data = data;
        if (request.Action == RequestActions.Create)
        {
          //response.SalesForceResponce = salesFroceServiceClient.CreateSalesForceAccount(accountData);
          //SalesForceService.SalesForceResponse result = salesFroceServiceClient.CreateSalesForceAccount(accountData);
        }
        else if (request.Action == RequestActions.Update)
        {
          //SalesForceService.SalesForceResponse
          SalesForceService.SalesForceResponse result = salesFroceServiceClient.CreateSalesForceAccount(salesForceReq);
          //salesFroceServiceClient.UpdateSalesForceAccount();
        }
        else if (request.Action == RequestActions.Delete)
        {
          //salesFroceServiceClient.UpdateSalesForceAccount();
        }
      }
      finally
      {
        salesFroceServiceClient.Close();
      }
    }
  });
}


text:
Go-to gurus /
So you wanna study medicine? A CUHK medical student reveals what it's really like
Careers: insights & realities
Sometimes it's hard to decide what you want to be when you grow up. Not everyone want to work in law, finance, or medicine - three of the most popular career paths among parents! There are also so many options out there. So you want to be a vet, or a cameraman, or maybe a forensic scientist. This is our series to help you understand what it's really like to work in different fields that may be available to you.
Go-to gurus - Careers
Life lessons from past jobs and work experience: Advice and career reflections from the Young Post Team
Study abroad, working holiday and IELTS advice, plus job opportunities at HKTDC Education & Careers Expo 2019
On the Job: Being a dance teacher is like being on Dancing with the Stars - but harder
On the Job: Qualities you need to make the cut as a hairdresser
On the Job: as a personal trainer - we discover that getting people healthy and into shape takes more than just working out
Think you're 'Fit to be a Doctor?' Find out if you are with an online test
And it's not just learning about valves and arteries
By Janet Lam August 12, 2016
med_student.jpg
Josephine Lam is proud to wear her white coat, and although it's hard work, it's all worth it.
Photo: YP cadet Janet Lam
Janet Lam
Hey! I am Janet. Apart from provoking useless arguments with my sister, I love to cook, hang out with friends and indulge myself in Youtube.
CUHK and HKU medical students explain why getting practical experience during clinical studies in a hospital is vital for their careers
A student in vet school tells us what it's really like to study veterinary medicine at university
7 things every law student can relate to
Do you picture yourself in a white coat, scurrying over gleaming floors with a stethoscope wrapped around your neck? The first step to becoming a doctor is getting the qualifications, so Young Post spoke to Chinese University's (CUHK) Josephine Lam to learn more about what it's like to be a medical student.
Lam, 19, was a student at King George V School before going to CUHK. While she scored 43 out of 45 on the IB, a great score isn't worth much if the subjects aren't relevant.
For acceptance to medicine at CUHK, IB maths studies module is not recognised as a substitute for maths, and two HL sciences are mandatory, one of which must be HL chemistry.
For the second science, "HL physics is allowed since we study radiology, but I'd recommend HL biology as it makes life so much easier", Lam tells Young Post.
But even the right subjects can't hide the fact that medicine is hard.
He designed G.I. Joe – and he has tips for how you can make a successful career out of art
"Entry requirements vary from year to year depending on the year group and university," she explains.
"Of course, good grades are expected – that typically means a score of between 42 and 45 for IB students."
And the pressure to do well academically doesn't end once you're accepted to the programme, although medicine is graded differently from other courses. Medical students don't have a GPA and are not ranked – they only receive a pass or a fail. If you fail, you take a supplementary exam. If you fail this supplementary exam, you repeat the academic year. You can only repeat a maximum of three academic years.
Lam says her timetable isn't the same as her friends on other courses, either.
For example, an economics student might attend a statistics class every Monday morning, and have the rest of the day to work on their own, but medicine is a lot more structured.
"We have lectures and tutorials in the morning, followed by classes on the same material in the afternoon," says Lam.
"The first lecture might be at 9.30am, with two or three 50-minute lectures after it. There is a small break in between. After lunch, tutorials start at 2.30pm."
During tutorials, you're given a practical situation relating to the stuff you've learned in the lecture that morning. The teaching day ends anywhere between 4.30pm and 6.30pm, depending on your classes.
It usually takes six years to complete a medical degree, and the fee is usually around HK$42,000 per year. The first year is less intense, and is dedicated to health and public sciences.
"The aim of this is to get everyone up to the same level," says Lam. "It is an opportunity to fulfil basic university requirements, such as passing PE and English, so they're out of the way in subsequent years."
Secrets from the film set: A cameraman and sound guy reveal what they go through
For Lam, adjusting to life as a medical student was tough. Apart from the typical gripes like trying to stay awake during lectures and repetitive canteen food, she had some serious struggles.
"The main problem was the vast difference between secondary school and university. Adapting to the new learning style was difficult," she says. "Since it was different to what I was used to, I didn't know how to learn effectively. In term two, I finally understood my learning style, but then I had to catch up with all the first-term material as well as the second. I was under a lot of pressure, especially since the exams were approaching."
The workload is also heavier, adds Lam. "Listen attentively to the professor and filter what is important. Being a critical thinker is key – it is not humanly possible to remember everything you are taught. Think: 'Will I need this knowledge?' There's no point spending time learning about an obscure artery in your body that never gets damaged."
Despite these difficulties, Lam enjoys the satisfaction medicine brings. "It is the greatest feeling in the world; knowing you'll have the power to heal people. It makes all the studying worth it." She says the extremely caring, helpful and united student community at CUHK make the transition much easier.
So how do you prepare yourself for such an intensive course? "Obviously, you must be academically capable," says Lam. "However, discipline is also quite important – start working on your study endurance and task management skills." She suggests using an application, such as Outlook, to plan your schedule and stay on top of things.
"Have organised notes all packed in one book. Know yourself and what is beneficial for your learning."
So you wanna be a medical student?
medical student,
Chinese University of Hong Kong,
Josephine Lam,
King George V School,
jeux nantes
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Of course, as a French native, I am so passionate about teaching my own language. As one of my students commented "my passion for French and languages is contagious!" I know the pitfalls of learning French through experience, so I have developed easy ways to explain the intricacy of the grammar rules in a simple way and in context while encouraging the verbal skills. I offer French classes one to one or small group. Business French for work, French for leisure, exam coaching. All classes tailored to the student`s needs.
Wednesday day time and evening.
Classes to be held at my house, or the student`s address.
Classes can be shared for 2 persons or more if the level and needs are similar.
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