Prove logistic loss is convex and lipshitz

I want to prove that $f(y,y_{pred})=log(1-e^{-ycdot y_{pred}})$ is convex while I know that $f(x)=log(1-e^{-x})$ is convex because $f”(x)>0$, but I cant find a way to apply it on the multivariate case.

Any help will be appreciated.

google search console – Data loss and ranking on the movements of webmaster owners

Battery exchange network

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Stop / Loss applied to BITCOIN trading

I bought 5 lots of Bitcoin on a Friday at the price of 10022.01 and I placed an S / L at 9840.00. By Monday, when I opened my trading account, I had lost 5,676.56 and the S / L had not been effective. When I inquired of the trading company I use, I was informed that it is because if we leave the trade open on weekends, if BITCOIN fall, the closure will be executed at the level at which the market will reopen. Is it correct?

Loss Is Part – General Forex Questions and Help

Loss is an inevitable thing in forex trading. So each trader must be careful with their trading strategy. It is conceivable to make constant profits, but it is difficult to conduct our operations without loss. To reduce our loss, we can make distinctive progress. Risk management is an indisputable requirement here and we have to trade with a low spread. As a trader, I get the best trading opportunities from XeroMarkets, including the smallest spread and the help of a personal account manager assigned by the broker. He is particularly a very transparent broker.

Typed – Profit / Loss Model of a Time Series of Changes in Statement of Financial Position

I'm recently exploring time series databases and trying to understand what data I'm going to store and how I'm going to query it. In addition, I am relatively new to TypeScript. This prototype exercises my current understanding of how it will work.

What I want most is:

  • See how I use a type and a variable of the same name (for both Open and Closed), in order to achieve a enumsyntax similar to the other enums in the namespace. (See how I refer to both the true enums and the “fakes” consistently, in the newState missions in the positionHistory table.) Is there a better way to do this? It has the effect I want, but the statements seem super strange to me.

  • Am I actually representing this in a way analogous to a chronological database? (TimescaleDB is the one I watch, if that matters.)

namespace Position {
    export enum Opening { BuyingToOpen, SellingToOpen };

    export type Open = 'Open';
    export const Open = 'Open';

    export enum Closing { BuyingToClose, SellingToClose };

    export type Closed = 'Closed';
    export const Closed = 'Closed';

    export type State = Opening | Open | Closing | Closed;

    interface StateChangeBase {
        timestamp: number;
        positionId: string;
        newState: State;
        affectedAsset: string;
        affectedAssetDelta: number;
    }

    export interface OrderPlacementStateChange extends StateChangeBase {
        newState: Opening | Closing;
        orderId: string;
    }

    export interface OrderFillStateChange extends StateChangeBase {
        newState: Open | Closed;
    }

    export type StateChange = OrderPlacementStateChange | OrderFillStateChange;
}

const positionHistory: Position.StateChange() = (
    { // placed an order to spend $500 on ZRX
        timestamp: 0,
        positionId: '0',
        newState: Position.Opening.BuyingToOpen,
        orderId: 'A',
        affectedAsset: 'USD',
        affectedAssetDelta: -500
    } as Position.OrderPlacementStateChange,
    { // order to spend $500 has been filled, we now hold 2,500 more ZRX
        timestamp: 1,
        positionId: '0',
        newState: Position.Open,
        affectedAsset: 'ZRX',
        affectedAssetDelta: +2500
    } as Position.OrderFillStateChange,
    { // placed an order to sell that 2,500 ZRX
        timestamp: 2,
        positionId: '0',
        newState: Position.Closing.SellingToClose,
        orderId: 'B',
        affectedAsset: 'ZRX',
        affectedAssetDelta: -2500
    } as Position.OrderPlacementStateChange,
    { // order to sell 2,500 ZRX for $505 has been filled
        timestamp: 3,
        positionId: '0',
        newState: Position.Closed,
        affectedAsset: 'USD',
        affectedAssetDelta: +505
    } as Position.OrderFillStateChange,
);

type ProfitOrLoss = { (asset: string): number };

const pnl: ProfitOrLoss = positionHistory.reduce(
    (accumulator, currentValue) => {
        accumulator(currentValue.affectedAsset) = (
            (accumulator(currentValue.affectedAsset) || 0)
            + currentValue.affectedAssetDelta
        );
        return accumulator;
    },
    {},
);

console.log(JSON.stringify(pnl, null, 't'));

Result:

{
    "USD": 5,
    "ZRX": 0
}

Creation of loss ports for a neural network with multiple outputs

I am making a multi-classification neural network for a dataset. I created the net but i think i need to specify a loss port to for each classification

Here are the labels for the classification and the encoder and decoders.

labels = {"Dark Colour", "Light Colour", "Mixture"}
sublabels = {"Blue", "Yellow", "Mauve"}
labeldec = NetDecoder({"Class", labels});
sublabdec = NetDecoder({"Class", sublabels});
bothdec = NetDecoder({"Class", Flatten@{labels, sublabels}})

enc = NetEncoder({"Class", {"Dark Colour", "Light Colour", "Mixture", 
    "Blue", "Yellow", "Mauve"}})

Here is the Net

SNNnet(inputno_, outputno_, dropoutrate_, nlayers_, class_: True) := 
 Module({nhidden, linin, linout, bias},
  nhidden = Flatten({Table({(nlayers*100) - i},
      {i, 0, (nlayers*100), 100})});
  linin = Flatten({inputno, nhidden((;; -2))});
  linout = Flatten({nhidden((1 ;; -2)), outputno});
  NetChain(
   Join(
    Table(
     NetChain(
      {BatchNormalizationLayer(),
       LinearLayer(linout((i)), "Input" -> linin((i))),
       ElementwiseLayer("SELU"),
       DropoutLayer(dropoutrate)}),
     {i, Length(nhidden) - 1}),
    {LinearLayer(outputno),
     If(class, SoftmaxLayer(),
      Nothing)})))

net = NetInitialize@SNNnet(4, 6, 0.01, 8, True);

Here are the nodes used for the Netgraph function

nodes = Association("net" -> net, "l1" -> LinearLayer(3), 
   "sm1" -> SoftmaxLayer(), "l2" -> LinearLayer(3), 
   "sm2" -> SoftmaxLayer(),
   "myloss1" -> CrossEntropyLossLayer("Index", "Target" -> enc),
   "myloss2" -> CrossEntropyLossLayer("Index", "Target" -> enc));

Here's what I want the NetGraph to do

connectivity = {NetPort("Data") -> 
    "net" -> "l1" -> "sm1" -> NetPort("Label"),
   "sm1" -> NetPort("myloss1", "Input"),
   NetPort(sublabels) -> NetPort("myloss1", "Target"), 
   "myloss1" -> NetPort("Loss1"),
   "net" -> "l2" -> "sm2" -> NetPort("Sublabel"),
   "myloss2" -> NetPort("Loss2"),
   "sm2" -> NetPort("myloss2", "Input"),
   NetPort(labels) -> NetPort("myloss2", "Target")};

Data diverges to "net" for each classification and passes through the next linear and softmax layer and to the corresponding NetPort
The problem that I have is the loss port that diverges with each softmax layer.

When I run this code

NetGraph(nodes, connectivity, "Label" -> labeldec, 
 "Sublabel" -> sublabdec)

I get the error message: NetGraph :: invedgesrc: NetPort ({Blue, Yellow, Mauve}) is not a valid source for NetPort ({myloss1, Target}).

Could someone tell me why this happened?

Thanks for reading.

photoshop – Does the loss of quality of jpeg files occur in each copy?

Copying files is a lossless operation. Discs usually have CRC checks in place to detect if a sector is corrupt, but the act of copying is a 1: 1 bit copy, so each copy is exactly the same as the previous one.

Quality loss occurs during compression when a file is written from image data, JPEG encoding deletes certain information, and even if JPEG supports certain lossless operations, allowing a file to be edited in a way that does not result in loss of quality, for example a 90 degree rotation.

When you see examples of degradation of JPEG, it is usually that someone opens a JPEG and saves it again after modification or creates a new JPEG by pasting the contents of another and that he repeatedly records. This will cause degradation as lossy compression occurs every time.

graphics – Detect conservation, loss or gain in a crafting game with items and recipes

Suppose we design a game like Minecraft where we have a lot of articles $ i_1, i_2, …, i_n in I $ and a bunch of recipes $ r_1, r_2, …, r_m in R $. Recipes are functions $ r: (I times mathbb {N}) ^ n rightarrow I times mathbb {N} $, i.e. they take certain elements with non-negative whole weights and produce a whole quantity of another element.

For example, the recipe for the cake in Minecraft East:

3 milk + 3 wheat + 2 sugar + 1 egg $ rightarrow $ 1 cake

… and the recipe for the torches is:

1 stick + 1 charcoal $ rightarrow $ 4 torches

Some recipes may even be reversible, for example:
9 diamonds $ leftrightarrow $ 1 diamond block

If there is a combination of recipes that we can apply repeatedly to get more items that we started with, the game is unbalanced and it can be exploited by players.
It is more desirable that we design the game with recipes that keep objects or possibly lose some (thermodynamic entropy in the real world – you can't easily undo the toast).

Is there an efficient algorithm that can decide if a set of recipes:

  • keep items?
  • lose items because of ineffectiveness?
  • earn items?

Is there an effective algorithm that can find problematic recipes if a game is out of balance?

My first thoughts are that there is a graph structure / maximum throughput problem here but it is very complex and it looks like a backpack problem. Or maybe it could be formulated as a SAT problem – this is what I plan to code at the moment but something more effective might exist.

We could encode recipes in a matrix $ mathbf {R} ^ {m times n} $ where the rows correspond to the recipes and the columns correspond to the articles. Column entries are negative if an item is consumed by a recipe, positive if they are produced by the recipe, and zero if they are not used. Similar to a well known matrix method for graph cycle detection, we could $ mathbf {R} $ at a high power and get sums from each row to see if the total of the items continues to increase, stay balanced or become negative. However, I am not convinced that it still works.

Any discussion, code or recommended reading is highly appreciated.

Loss in Forex

Duplicate TCP acknowledgment without packet loss

I have a sender on IP 192.168.2.250 running integrated RTOS and a receiver running Linux 4.9.x on IP 192.168.2.1

The receiver is configured as a wireless access point and the sender is directly connected to the receiver via WiFi.

I performed a tcpdump on the receiving side during a TCP data transfer and I notice a large number of duplicate ACKs sent by the receiver without the loss of packets occurring (or at least c & # 39; is what I think, because I don't see any retransmission and the ACKs finally follow the sequence numbers sent).

Wirehark trace tcp duplicate ack

Anyone have any idea what could be causing the behavior of the receiver?

Release sysctl net | grep tcp

net.ipv4.tcp_abort_on_overflow=0
net.ipv4.tcp_adv_win_scale=1
net.ipv4.tcp_allowed_congestion_control=cubicreno
net.ipv4.tcp_app_win=31
net.ipv4.tcp_autocorking=1
net.ipv4.tcp_available_congestion_control=cubicreno
net.ipv4.tcp_base_mss=1024
net.ipv4.tcp_challenge_ack_limit=1000
net.ipv4.tcp_congestion_control=cubic
net.ipv4.tcp_delack_seg=1
net.ipv4.tcp_dsack=1
net.ipv4.tcp_early_retrans=3
net.ipv4.tcp_ecn=2
net.ipv4.tcp_ecn_fallback=1
net.ipv4.tcp_fack=1
net.ipv4.tcp_fastopen=1
net.ipv4.tcp_fin_timeout=60
net.ipv4.tcp_frto=2
net.ipv4.tcp_fwmark_accept=0
net.ipv4.tcp_invalid_ratelimit=500
net.ipv4.tcp_keepalive_intvl=75
net.ipv4.tcp_keepalive_probes=9
net.ipv4.tcp_keepalive_time=7200
net.ipv4.tcp_limit_output_bytes=262144
net.ipv4.tcp_low_latency=0
net.ipv4.tcp_max_orphans=16384
net.ipv4.tcp_max_reordering=300
net.ipv4.tcp_max_syn_backlog=128
net.ipv4.tcp_max_tw_buckets=16384
net.ipv4.tcp_mem=332494433366498
net.ipv4.tcp_min_rtt_wlen=300
net.ipv4.tcp_min_tso_segs=2
net.ipv4.tcp_moderate_rcvbuf=1
net.ipv4.tcp_mtu_probing=0
net.ipv4.tcp_no_metrics_save=0
net.ipv4.tcp_notsent_lowat=4294967295
net.ipv4.tcp_orphan_retries=0
net.ipv4.tcp_pacing_ca_ratio=120
net.ipv4.tcp_pacing_ss_ratio=200
net.ipv4.tcp_probe_interval=600
net.ipv4.tcp_probe_threshold=8
net.ipv4.tcp_recovery=1
net.ipv4.tcp_reordering=3
net.ipv4.tcp_retrans_collapse=1
net.ipv4.tcp_retries1=3
net.ipv4.tcp_retries2=15
net.ipv4.tcp_rfc1337=0
net.ipv4.tcp_rmem=4096873806291456
net.ipv4.tcp_sack=1
net.ipv4.tcp_slow_start_after_idle=1
net.ipv4.tcp_stdurg=0
net.ipv4.tcp_syn_retries=6
net.ipv4.tcp_synack_retries=5
net.ipv4.tcp_syncookies=1
net.ipv4.tcp_thin_dupack=0
net.ipv4.tcp_thin_linear_timeouts=0
net.ipv4.tcp_timestamps=0
net.ipv4.tcp_tso_win_divisor=3
net.ipv4.tcp_tw_recycle=0
net.ipv4.tcp_tw_reuse=0
net.ipv4.tcp_use_userconfig=0
net.ipv4.tcp_window_scaling=1
net.ipv4.tcp_wmem=4096163844194304
net.ipv4.tcp_workaround_signed_windows=0